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Keywords: Power Systems Control
Abstract: In this paper, an optimization technology for emergency capacity of demand-side storage battery with PV and EV discharge has been proposed. Conventionally, in the demand-side storage battery installed for emergency, only the rest of the capacity kept for emergency can be used in normal condition. When PV system and V2X are available in emergencies, the battery capacity for emergency can be reduced based on the prediction of PV generation and assumed EV schedule. The problem optimizing the battery capacity for emergency can be formulated in mathematical optimization problem. A numerical example shows that the proposed optimization of battery capacity for emergency contributes to the electricity cost reduction in normal condition by peak shaving.

Keywords: Adaptive and Optimal Control, Process Automation, Factory Automation
Abstract: Greenhouse Gases Emissions (GHG) is the main contributor to global warming. Japan and Thailand both have submitted their National Determined Contributions (NDC) to reduce GHG emissions. Advance Process Control (APC) which is a low cost and effective technology can improve the energy efficiency of plants and large building by optimizing the operation of the utility plant. APC can optimize the operation to minimize energy consumption and improve productivity, which leads to reduction of GHG emissions. 3 examples of APC optimization in different plants are also introduced with proven real life application of APC. The wide dissemination of APC to all countries should be promoted because it can help all industries to reduce GHG emissions through improving energy efficiency.

Keywords: Innovative Systems Approach for Realizing Smarter World, Temperature Measurement, Signal and/or Image Processing
Abstract: In recent years, office building’s Heating Ventilation and Air Conditioning (HVAC) systems are required to consider personal comfort in addition to indoor environment. Predicted Mean Vote (PMV) is mostly used for evaluation of comfort in indoor environment. However, calculation of PMV requires some factors that HVAC systems usually don’t measure. This paper provides a practical method for estimating PMV by applying state-of-the-art deep learning to infrared thermography.

Keywords: Multivariable Control
Abstract: Floating offshore wind turbines have challenging problems such as generator speed fluctuation and increase on the loads of wind turbine components because of platform motions caused by wind and wave variations. Blade pitch angle control is used to regulate output power at above rated wind speed and a careful design of a blade pitch controller has a possibility to solve the problems. In this paper, we design a blade pitch angle controller with incoming wind speed preview in order to reduce fluctuations of the rotor speed, platform motions, and load on blades.

Keywords: Nonlinear Control, Adaptive and Optimal Control, Multivariable Control
Abstract: One of the major factors that affect the fuel economy of gasoline vehicles is the strategy how the vehicle is driven. A Pulse-and-Glide (PnG) strategy, which periodically switches the operating modes of the powertrain between the engine driving mode(pulse mode) and the glide mode, is proven to be effective in improving fuel economy in a single traveling scenario by a past research. A further research finds that, when using PnG strategy, switching the operating point of the engine between the minimum Brake Specific Fuel Consumption (BSFC) points and the idling point can contribute in improving fuel economy in car-following scenario. However, the following factors about PnG are still not clear: 1)what are the optimal values of the initial vehicle speed, the duration of the pulse mode and the engine power during the pulse mode for fuel efficiency; 2)whether these values depend on the driving scenarios. In this paper, an optimization problem is formulated to find the optimal PnG pattern in a single traveling scenario for fuel economy. Under the assumption that the engine works at a constant operating point during the pulse mode, it is found that the most fuel-efficient engine operating point during the pulse mode is not the minimum BSFC point, but an operating point with a lower torque and a lower rotation speed.

Keywords: Identification and Estimation, Nonlinear Control
Abstract: Proposition of this study, we tried to make the gap of the estimated value between by ECU and by GP as small as possible in transient characteristics. In addition, we had thought that the GP: Gaussian process would be able to estimate more detailed engine behavior by using detailed simulation results as training data and verification data for Machine learning. It was thought that more detailed engine behavior could be learned by using detailed simulation results for training data and verification data. As a precaution when modeling, after confirming Max Time Lag or selecting by explanatory factor, it is advisable to confirm that the securing of the explanatory factor is correct by cross validation. And this study we used WLTC: Worldwide-harmonized Light vehicles Test Cycle running course for engine simulation. Depending on the choice of explanatory factors in the statistical model, the possibility of over fitting or multicollinearity can sometimes occur. And second point is some of the data fit testing seemed to Gibbs phenomenon ore prediction difference by lag setting. And we have verified the influence on the estimation accuracy by down sampling and LPF: low pass filter. In where modeling conducted by extracting the training data from the engine operation data according to a rule, and the major obtained results will be reported in this paper.

Keywords: Signal and/or Image Processing
Abstract: Many foreign people don’t know Thai language and most of the time Thai sign images, posters or text images do not have subtitles in English so, it is very necessary to have a system that can translate Thai text to English. In this paper, MSER and convolutional neural network (CNN) have used to understand Thai text in English. Firstly, region of interest has localized from natural image which is some particular Thai text. Then text has extracted and fed to CNN. We used a 7-layer self-designed CNN model that provides the output with an accuracy of 98%. The proposed system takes natural scene image as input and uses MSER, geometrical properties as well as bounding box algorithm to localize the text area then selected localized areas have fed to CNN and provide an output that has the English meaning for the Thai text image. This paper introduces a new approach of text translation by using image classification method. The proposed system can work on particular inputs which are indoor sign Thai text images.

Keywords: Signal and/or Image Processing
Abstract: Generalized frequency division multiplexing (GFDM) is a major candidate for the fifth-generation (5G) waveform and beyond, and can be used as a modulation technique for Internet of things (IoT) applications. GFDM, however, requires dedicated step to synchronize time and frequency. Current methods of synchronization for GFDM do not perform well, leading to an increase in the bit error rate. In this paper, we propose a method to synchronize time and frequency of GFDM waveforms in a fading channel, under a situation that the channel gains have already been estimated perfectly. The proposed method inserts a known marker and estimates the time and frequency jointly. We compare the proposed method and existing methods in terms of the root mean square error between an actual time or frequency offset and the estimated one. The results show that the proposed method outperform existing methods over a wide range of the signal to noise ratios (SNRs). Furthermore, the proposed method has a desirable property, that the root mean square error approaches zero with an increase in the SNR. The proposed method can synchronize time and frequency accurately and enhance the use of GFDM as a modulation technique in IoT applications.

Keywords: Power Systems Control, Intelligent Control, Adaptive and Optimal Control
Abstract: Smart grid has become one of the main research interests in power systems alternative energy sources are needed to seamlessly and efficiently integrated into the main grid. While most alternative energy sources such as PV systems provide DC power and electric loads in a resident such as LED lights are DC loads, the main power grid requires AC power. This paper proposes a AC-DC hybrid bus system with an IoT-based Energy Management Control (EMC) for a household with PV panel. Based on the proposed bus system, we propose a smart energy flow management strategy using a local IoT network to provide statuses of both the loads and available energy by which harvested energy is used efficiently. Based on the model-based design methodology, the overall system is modeled and verified on the MATLAB/Simulink platform using load and energy source profiles based on the actual data for Thailand case. The results showed that the proposed system with kW PV panel can reduce the energy from grid about 54.53% percent per day comparing with a case of feeding all harvested PV energy to the grid.

Keywords: Signal and/or Image Processing, Intelligent Systems
Abstract: Binary classification has applications in several areas, including the Internet of Things (IoT), and can be achieved by learning a predictive model from a labelled training dataset. A training dataset that is imbalanced leads undesirably to an inaccurate classifier. In this paper, we compare performance of existing methods that tackle class imbalance. These methods are undersampling, oversampling, hybrid, SMOTE, ADASYN, and a method that does not modify the datasets. We choose support vector machine (SVM) as a classifier and estimate classification accuracy, sensitivity, and specificity under each method. The datasets in the comparison cover a wide range of applications and class ratios. The research results indicate that undersampling does not achieve a highest accuracy, sensitivity, or specificity in any benchmark dataset, and is a not a recommended method in general. A method that does not modify the datasets has the most consistent performance; it is statistically most accurate, sensitive, and specific in three, one, and five datasets, respectively, at the significance level of the test at 10%. From the comparison, we find that a suitable method to tackle class imbalance depends on a dataset at hand and that no single method universally performs well across the benchmark datasets. The research results help researchers choose a method to mitigate a detrimental effect of class imbalance, improving classification performance.

Keywords: Adaptive and Optimal Control, Temperature Measurement
Abstract: In many applications of IoT platform, embedded IoT nodes are required to collect data and provide primary processing. In such deployment, heat dissipation from an embedded node can become a major issue due to space limitation. In this paper, we explore a possibility of using Peltier modules to provide an efficient cooling system for an embedded IoT node installed in a completely closed housing. We propose a cooling system using Peltier modules. Based on the proposed cooling system, a set of experiments were carried out to help determine feasibility of such system. In the experimental setup, a closed box with a set of Peltier modules as means to transferring heat from the box are built to represent a completely closed housing of an IoT node. A heater is installed inside the box to imitate heat generated from an IoT unit assumingly installed inside the box. The harvested energy from the Peltier set is fed to a load resistance of either 5Ω, 10Ω or 150Ω. Based on this setup, we found that (1) the effects of the temperature difference on the Peltier set to the output power which represents the amount heat transfer rate from the chamber are linear, and (2) the system can reduce the chamber temperature in a fraction of degree Celsius per minute when keeping the temperature difference of both sides of Peltier constant; e.g. it is about 0.025℃ per minute at 10℃. Although the rate of heat transfer is low, the system has advantage of continuously moving the

Keywords: Computational Intelligence
Abstract: This paper proposed a real-time data compression approach for a domain of data with partially known characteristics. Currently, the rapidly growth of technology causes a numerous amount of data. These data are generated by electronic devices or sensors that used in IoT devices and science experiments. The numerous amounts of data need to be distributed, accessed and processed all over the world. Thus, an effective data compression is mandatory to enables more economical use of storage and faster data transfer. In this paper, we describe the use of Discrete Cosine Transform (DCT) algorithm for lossy compression. Vector Quantization, and Huffman Coding are also applied by preparing codebooks which generated based on the signal characteristics to achieve higher compression ratio and enable real time application.

Keywords: Guidance and Flight Control
Abstract: At present, unmanned vehicles such as UGV (Unmanned Ground Vehicle), UAV (Unmanned Aerial Vehicle), etc. play a significant role in both military and consumer fields. Moreover, many researches have been actively done related to swarm robotics using multiple autonomous unmanned vehicles. Although recent researches have verified the effectiveness of swarm of unmanned vehicles at the time of natural disaster etc., most of them are homogeneous swarm. On the other hand, there are few researches related to heterogeneous swarm because of their complexity and difficulty of generalizing and formulating models. In this paper, we generalize the heterogeneous swarm model in case of surveillance mission which is one of the most possible missions for them. And we describe a partitioning method of region with multiplicatively weighted Voronoi tessellation and proposes an index of choosing number for heterogeneous swarm of unmanned vehicles using simple formulation.

Keywords: Guidance and Flight Control, Mechanical Quantity Measurement, Mechanical Systems Control
Abstract: It is known that the thrust of rotorcraft increases near the ground. This phenomenon is called ground effect. Due to the ground effect, it is difficult to land on target points precisely, which causes overturning and damage the helicopter body. The objective of this study is to realize a stable flight for multirotor helicopters near the ground. This paper focuses on a quadrotor helicopter. The aerodynamic characteristics of the quadrotor near the ground was experimentally evaluated. We measured the thrust, the rotational speed, and the power consumption of the rotor in hovering with a constant motor command. In addition, the flow between two rotors are visualized to verify the influence of the flow interference on the thrust.

Keywords: Guidance and Flight Control, Adaptive and Optimal Control, Robust Control
Abstract: The purpose of this study is to design a gain-scheduled (GS) controller for a spacecraft with control moment gyros (CMGs), while avoiding trial and error on determining the weighting matrices of the H_2 controller. For this purpose, we apply the inverse linear quadratic (ILQ) method to this control problem. The conventional ILQ method assumed that the system is linear time-invariant (LTI), however, our plant is modeled as a linear parameter-varying (LPV) system. Therefore, we theoretically extend the conventional ILQ method so that it can be applied to LPV systems. The derived optimality condition is expressed in linear matrix inequalities (LMIs). Numerical simulations are conducted to confirm the validity of the derived optimality condition.

Keywords: Guidance and Flight Control, Robust Control, Remote Sensing
Abstract: This paper is concerned with the LIDAR-based gust alleviation (GA) control, which mitigates dangerous vertical acceleration due to wind gust. Especially the GA control for an aircraft in an ascent/descent phase is discussed. Since the LIDAR can detect wind gust velocities in front of the aircraft, the preview feedforward compensation can be applied by treating forward wind velocities as previewable information. In order to control the flight path angle and vertical acceleration simultaneously, the proposed method does not require explicit gain switching, whereas the existing method introduced switching. Simulation results are provided to show effectiveness of the proposed method for simultaneous control of the flight path angle and vertical acceleration.

Keywords: Adaptive and Optimal Control, Guidance and Flight Control
Abstract: Asteroid related research needs to be done for three main reasons: to defend our planet in case of any future probable collision, to utilize the precious minerals these bodies contain, and to find more about the origin of the solar system. This paper will explore optimal landing trajectories for a binary asteroid as a means to pursue such kind of research. The binary asteroid is modeled as a circular restricted three-body problem (CRTBP) which is then used to formulate an optimal control landing problem using quadratic and linear constraints. The optimal control Hamiltonian system consists of non-linear differential equations arising due to the dynamics of CRTBP and is solved using an open-loop backward sweep algorithm. This algorithm fails to converge for a wide range of initial conditions and is found to be highly sensitive to errors. In this paper, it is proved that the cause of this divergence is related to integration errors occurring while performing numerical simulations. Finally, the stable optimal landing regions arising from the sensitivity of optimal control are characterized.

Keywords: Guidance and Flight Control, Nonlinear Control, Robust Control
Abstract: This paper is on the synchronizing motion control for tumbling debris using space robotic arm. The debris that are left in the space environment is basically uncontrolled with possible tumbling motion around certain axis. Synchronizing the motion of the end-effector of the robotic arm is not simple due to the complicated motion and the singularity that appears in the control. This paper introduces the control law to synchronize these motions applying the techniques of the delayed feedback control and SR-inverse method. The results show that the space manipulator can follow the periodic tumbling motion without falling into the singularity with relatively small input.

Keywords: Guidance and Flight Control, Nonlinear Control, Mechanical Systems Control
Abstract: Missions involving rapid and large-angle attitude maneuvers have been conceived in astronomical and earth observation satellites in recent years. As an actuator capable of generating a large torque is also going to be required, it will also be necessary to consider characteristics of an actuator in designing a control system. Actuators capable of generating a large torque include reaction control system (RCS). RCS gives an on/off input as it uses the reaction force from fuel injection by thrusters, it can generate a large moment. In addition, the control system of current application satellites normally uses both RCS and reaction wheel (RW) conventionally used for attitude control. This paper considers large angle attitude maneuver of spacecraft by a combination of RCS and RW.

Keywords: Transportation Systems
Abstract: In this study, the effect of grouping vehicles at unsignalized intersections is considered. A method for constructing vehicle groups in a traffic flow simulator is proposed, and the effect of grouping vehicles, on traffic is verified by simulation. Simulations are performed with varying traffic volumes and grouping conditions, and the results are compared to clarify the effect on traffic caused by the grouping of vehicles at unsignalized intersections.

Keywords: Intelligent Control, Transportation Systems
Abstract: Energy consumption of vehicles at signalized intersections is highly influenced by acceleration/deceleration maneuvers and idling time. Existing research on fuel-efficient driving at signalized intersections mainly focused on connected and automated vehicle (CAV) technologies. This paper introduces a sustainable eco-driving strategy learning scheme from driving data of a vehicle that generates the optimal speed trajectory when approaching a signalized intersection. The goal of the proposed system is to reduce fuel consumption by advising an appropriate driving strategy near a signalized intersection. Our main contribution is that the driving performance in traffic scenarios in the existing signalized intersection can be improved without utilization of vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communications. A Gaussian process regression (GPR) model is developed that predicts intersection crossing time and crossing probability of a vehicle from its driving data. Based on the crossing probability, the optimal speed profile is calculated through an optimization algorithm for fuel-efficient driving. The viability of the proposed system is assessed using microscopic traffic simulation and the findings show significant fuel economy enhancements.

Keywords: Transportation Systems, Network System Integration, Intelligent Control
Abstract: Freeway traffic around the ramp exhibits speed fluctuations and sometimes rapidly becomes congested. The ramp metering, a way of regulating traffic flows into the freeway, often causes inconvenience as most vehicles on the ramp have to stops unnecessarily. This paper introduces the concept of the least restrictive traffic control scheme using the ramp metering technique based on early detection of a traffic-breakdown. Specifically, in the scheme, the ramp meter is only activated when freeway traffic is in the transition state, and in the rest of the period, when there is no risk of a breakdown, vehicles are allowed to merge freely. The breakdown transition of traffic is detected using a model-free technique called dynamical network markers (DNM). In the DNM method, the traffic densities are recorded at several cells on the individual lanes of the freeway and their standard deviations and absolute correlation coefficients are obtained. Using that information, a scalar strong indication is obtained when the traffic is at the transitional state. By employing microscopic traffic simulation, the proposed ramp metering scheme is evaluated on merging traffic to a multi-lane freeway. The proposed scheme could detect the breakdown transition of the freeway traffic at a very early stage, and by timely activating the ramp metering, it could prevent the freeway from a breakdown.

Keywords: Intelligent Control
Abstract: The vehicle body acceleration and wheel deflection response are very important parameters in vehicle suspension system, to ensure the vehicle reach its maximum comfort and stability. This study uses two approaches in designing controller which are PID-LQR and Fuzzy-PID. The PID controller is set as a benchmark controller due to its easiness and simple mechanism. Throughout the paper, the performance of respected controller is examined and compared by the given road disturbances. Simulation results obtained from the response in Matlab and Simulink software is then analyzed for further investigation. The preliminary study shows the Fuzzy-PID controller has the higher ability to control and provide maximum ride comfort compared to PID and PID-LQR.

Keywords: Robotic and Automation Systems, Signal and/or Image Processing, Intelligent Systems
Abstract: The Advanced Driver Assistance System (ADAS) is invented and installed in vehicle to assist drivers while driving with the intention to reduce car accident, by providing alerts based on the availability of monitoring platforms. However, not all vehicles come with pre-installed ADAS system, due to the system complexity and the high price of the required sensors. These days, vision-based ADAS are being developed in cheap sedan vehicles, as it can be affordable by car owners, as it requires only cameras to monitor in the car. In this paper, we propose framework to monitor the vehicle condition, i.e. collision avoidance, by providing it a surrounding view of the vehicle, to alert the driver when obstacles near to the vehicle is detected. In this system, we 1) deployed the Raspberry Pi, as the main equipment mounted with three wide angle Pi cameras, and 2) proposed the obstacles detection framework, to capture the vehicle surrounding view. We used the Forward Collision Warning (FCW) features and embedded these features into the system, using a pre-trained TensorFlow model, normally deployed for vehicle detection system. Here we approximated the distance of the obstacles to the vehicle and used this value to check if the obstacles are close enough to the vehicle, to trigger the alert sound as warning. Experimental results reveal that the proposed framework provide a 360 degree monitoring system for a vehicle in real time.

Keywords: Signal and/or Image Processing, Intelligent Systems, Robotic and Automation Systems
Abstract: We research into a 3D Traffic Scene Reconstruction (3DTSR) task. 3DTSR aims to reconstruct a 3D traffic scene from video footage captured from a car’s dash-camera. The 3D traffic scene provides a new platform for various services to exploit, for example, self-driving cars, driving behavior analysis, and traffic accident analysis. In our approach, we resort to a passive sensing approach which detects objects and their positions based on visual information. Spatial positions of objects in a 2D scene are lifted into a 3D scene based on information from multi-sources: (i) semantic instance segmentation, (ii) spatial position and volume estimation through orthogonal images, and (iii) prior knowledge concerning shape and volume of objects. In this paper, we focus on semantic instance segmentation, the first phase of the proposed 3DTSR method. The semantic instance segmentation task is accomplished with the Mask R-CNN model pre-trained on COCO dataset. We report the performances of the semantic segmentation task from different Detectron2 models undergone the transfer learning process using information from various datasets. We show that it is feasible to obtain the shape and appearance of objects in the road scene using our proposed segmentation process.

Keywords: Sensors and Transducers, Signal and/or Image Processing, Identification and Estimation
Abstract: Measurement of body fat 3D distribution by using electrocal impedance tomography (EIT) has been studied. We have developed an AC measurement system with 48 electrodes for EIT, and made some basic experiments using this system and saline tank phantom simulating a human body.

Keywords: Signal and/or Image Processing
Abstract: This paper proposes a novel, noniterative reconstruction method for magnetic resonance electrical properties tomography (MREPT). In this method, we first estimate the electric field using a three-dimensional integral equation. Then the EPs are reconstructed from the estimated electric field, using Ampere's law. Our method can estimate an arbitrary three-dimensional distribution of EPs. The efficacy of our proposed method was validated through numerical simulations and a phantom experiment.

Keywords: Signal and/or Image Processing, Analytical Measurement, Identification and Estimation
Abstract: Elastodynamic response of orthotropic laminate composite structures subjected to an incident wave front is evaluated based on the integral representations in terms of Green’s function. Being different from the conventional TOF or TOFD method, Green’s function representation keeps information about the source and gives an opportunity for a quantitative near- and far-field analysis of the propagating wave field. A Green’s function is acting a point-source response of wave equation of the Eucredean space. This study, therefore, discusses a novel imaging method based on the point source constrained partial differential equation in order to reconstruct the silhouette of the subsurface defects. A point source response on the Lp space is analyzed mathematically and several acoustical experiments are performed to show accuracy and applicability of our proposed approach for imaging of defects in a plain woven CFRP plate.

Keywords: Signal and/or Image Processing, Identification and Estimation, Sensors and Transducers
Abstract: This paper presents a novel method for the magnetoencephalography (MEG) inverse problem. To distinguish neural currents flowing in a focal region on a cortical surface from background neural activities spread over the brain, we propose to use a heterogeneous source model which includes patch and distributed sources simultaneously.　 The efficiency of the proposed method is verified using MEG data of an epileptic patient.

Keywords: Identification and Estimation, Networked Sensor System, System Engineering
Abstract: Society 5.0 is characterized by the sophiticated intgration of cyberspace with physical space, that is, cyber-physical systems (CPS), which are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops. The authors have been applying Category Theory to describe a systems of systems(SoS) architecture that is crucial to develop and operate an object SoS and to achieve sustainability. This paper proposes universal views for CPS from the frame work of category theory.

Keywords: Nonlinear Control, Modeling, System Identification and Estimation, Mechanical Systems Control
Abstract: This work develops a simple, implementable region-wise model based quadratic controller for motor with embedded deadzone nonlinearities. This embedded nonlinearity is captured in two regions. In the linear region, the deadzone phenomena is approximately modelled through the use of simple differential equation. In the nonlinear region, a log-hyperbolic relationship yields a good approximation to the deadzone curve. The linear region required an LQR based controller to be designed, while the nonlinear region necessitated the SDRE based controller to be designed. Simulation results show that choosing large m parameters ensured better tracking and increases the overall stabilities of the control system.

Keywords: Power Systems Control
Abstract: Due to the concern of environmental problems, there is a drastic increase in renewable energy generation. Typical renewable energy resources consist of photovoltaic power plant and mini-hydro power plants, whereas the dispatch operation is enhanced by battery energy storage system. This paper proposes a dispatch strategy in islanding mode of Mae Hong Son which aims to optimize the ecomomic operation with zero CO2 emission in islanding mode for three seasons. In this study, renewable energy resources are used as the main supply of electrical power. Dispatch strategy of Energy Management System (EMS) is formulated as a linear programming. We further apply Model Predictive Control (MPC) to dispatch strategy to improve performance that minimizes the total operating cost (TOC) and gives zero CO2 emission. The numerical results show the comparison between the proposed MPC and the previous EMS. TOC in winter is improved by 0.52% using actual load and 0.53% using forecast load. In summer season, TOC is improved by 1.6% using actual load but is worsen by 1.86 % using forecast load. In rainy season, TOC remains the same in two cases. These results are deemed to be beneficial for MPC in reducing the operating cost and giving zero CO2 emission.

Keywords: System Engineering, Robust Control
Abstract: The strongly stabilization is the control method to make control systems stable by using stable controllers. Using this method, it's possible to construct a highly reliable control system because it has robustness. However, when a strongly stabilizing controller is used, steady-state error remains when step disturbances and uncertainties exist in the control system because the strongly stabilizing controllers doesn't have no pole on the origin. To solve the problem, Hoshikawa et al. proposed a design method of semi-strongly stabilizing controllers with a pole on the origin. However, the method of Hoshikawa et al. fails to place the poles on the imaginary axis. In this paper, a design method for semi-strongly stabilizing controllers with poles on the imaginary axis is proposed.

Keywords: Modeling, System Identification and Estimation, Identification and Estimation
Abstract: Servo motors are available for reasonable price and widely used for both commercial and research purposes. Servo motor makers provide the program libraries to manipulate the motors and we only need to send the command signals by communication protocols. These backgrounds make it appearing to construct and study robots using servo motor. However we need mathematical models when we apply advanced control methods. In this paper, we propose to employ a constrained least square method considering the modeling of servo motor and we also present the experimental results to demonstrate the validity of the proposed system identification method.

Keywords: Human-Machine Systems, Adaptive and Optimal Control, Robotic and Automation Systems
Abstract: Steering control in advanced driver assistance systems is required to make the vehicle follow the desirable path by properly supporting the human. However, the human's handling behavior accompanies the reaction delay which obstacles the vehicle path following. As a remedy for this problem, the preview control is expected to recover the control performance by compensating for the human's reaction delay. This paper designs a path following controller for a driver-vehicle system based on the H2 optimal preview output feedback control theory, and examines the features of the resulting controller by numerical simulations.

Keywords: Robust Control, Multivariable Control
Abstract: We propose an unknown input disturbance observer for constant disturbances based on state observer. Disturbance observer estimates disturbances effectively for a wide range of systems by using available signals of system. Since disturbance observer has many distinctive features, it has been used in many applications as the most promising approach to attenuate disturbances. However, control input of system is always necessary in the pre-existing design of disturbance observers. Because there are systems which the control input of system is indeed unavailable, it is necessary to design unknown input disturbance observers. On the other hand, state observer-based control has been extensively applied in many control cases. In this paper, we propose a design method for unknown input disturbance observer with constant disturbances based on state observer. By combining a pre-existing basic linear disturbance observer with a pre-existing state observer, the new unknown input disturbance observer has a lot of merits. In addition, the effectiveness of our design will shown by numerical example.

Keywords: Multivariable Control, Robust Control
Abstract: This paper examines a simultaneously stabilizing controllers in a two-stage compensator for multiple-input/multiple-output systems. The two-stage compensator system controls the plant using two types of controllers. Aims to show the relationship between simultaneously stabilizing controllers which can stabilize a plant in two-stage compensator systems. Previous works have shown the necessary and sufficient conditions for stable two-stage compensator single-input/single-output systems. The purpose of this paper is to expand the result by Morikawa et al. and to examine the relationship between controllers which can simultaneously stabilize the multiple-input/multiple-output plant.

Keywords: Human Interfaces
Abstract: In this paper, we examine a method for detecion of anxiety expression for human going down slope by using wheelchair. A method for detection of anxiety expression is fusion of Support Vector Machine and measurement of brain waves. In order to improve the accuracy of anxiety expression, we optimize hyper-parameters of Support Vector Machine. Anxiety expression can be estimated about 90% accuracy.

Keywords: Adaptive and Optimal Control
Abstract: This paper studies the optimal tracking control problem for continuous-time affine nonlinear systems and proposes a completely model-free approximate optimal tracking control design approach. This approach only uses measurement data collected from the trajectories of the system in real time to learn the optimal tacking control. At first, a new tracking policy iteration algorithm is developed based on the integral reinforcement learning technique. Then, the algorithm is implemented under the actor-critic structure, where the critic neural network and the actor neural network are updated iteratively. Finally, simulation results are provided to show the efficiency of the method.

Keywords: Adaptive and Optimal Control, Identification and Estimation, Nonlinear Control
Abstract: To estimate time-varying signal, we introduce a nonlinear parametrized form using a basis function expansion with bounded regressor vector and derive GESG update law to estimate unknown parameters. We adopt the basis function expansions using a sigmoid function. The estimation performance of the proposed estimator is verified by using the MATLAB/Simulink and a quadrotor equipment.

Keywords: Adaptive and Optimal Control, Process Control, Nonlinear Control
Abstract: This paper presents a novel perturbation-based extremum seeking (ES) based on the smooth approximation of the sign of the gradient of unknown plant maps, which will improve the stability and the control performance of ES. The stability and the convergence speed of conventional sinusoidally perturbed gradient-based ES are very sensitive to the curvature of an unknown plant map, which has motivated the development of improved ES schemes that are less sensitive to the curvature such as Newton-like ES schemes. In this paper, an alternative ES scheme is proposed by estimating the sign of the gradient instead of the gradient itself. The ES scheme not only frees us from taking care of the curvature of the unknown map but also is able to minimize the increase of implementation complexity by just incorporating the signum function for the conventional ES scheme. Moreover, the signum function is replaced by its smooth approximation such as the softsign or a sigmoid function in order not only to suppress chattering phenomena but also to be able to adjust the behavior in the vicinity of an extremum point. It is also shown that the local-stability of the proposed ES scheme can be proved under some mild assumptions for the approximation function. The effectiveness of the proposed ES is demonstrated by some simulation examples for a simple process model and for a complex nonlinear biological wastewater treatment process model.

Keywords: Mechanical Systems Control, Mechatronics Systems, Nonlinear Control
Abstract: Active control methods are used to suppress vibrations for a pneumatic isolation table. This study addresses the regulation problem for a pneumatic isolation table system, which includes a discrete-valued input actuator. For the sake of controlling the vibrations on the table, the control input was applied separately in two different ways.

These are positive and negative, which are the supply side and the exhaust side, respectively. In order to achieve better control performance, we suggest a novel idea that integrates the inputs, i.e., a combination of positive and negative inputs, and adds it as an extra input to the sytem. The effectiveness of the proposed method is verified through numerical simulations for a pneumatic isolation table system model.

Keywords: Nonlinear Control, Mechanical Systems Control
Abstract: In this work, we consider control of a multirotor unmanned aerial vehicle (UAV) for tracking a target and avoiding obstacles. We formulate the tracking-and-avoidance control problem as a nonlinear receding-horizon differential game (NRHDG) to take account of the lack of the assumptions on the future path of a target, nonlinearities in the dynamics, constraints in the rotor thrusts and angular velocities. Simulation results show that the NRHDG is more effective for tracking a target and avoiding obstacles than nonlinear model predictive control (NMPC) that requires some assumptions on the future path of a target in the tracking. In addition, we conduct experiments with an actual UAV to demonstrate real-time optimization for the NRHDG. Experiment results show that target tracking and obstacle avoidance can be accomplished by the NRHDG.

Keywords: Nonlinear Control, Mechanical Systems Control, Factory Automation
Abstract: This study attempts to extend a digital redesign method, called the Plant-Input-Mapping (PIM) method, to nonlinear control systems. The PIM method in the linear case can preserve the stability of the underlying continuous-time feedback control system without respect to the selection of the sampling interval. The PIM method is a model-based approach, and thus, the key of the extension scheme is to overcome the absence of the exact discrete-time model for nonlinear systems. In this paper, we propose a PIM method incorporating the feedback linearization. The proposed method can discretize the continuous-time controller designed by using the linearization technique, which is numerically verified through an example of a robotic manipulator with a flexible joint.

Keywords: Nonlinear Control, Robust Control, Mechanical Systems Control
Abstract: This short paper investigates region of attraction of the faster MPC of hydraulic arms. As the second step of our study, based on our modeling techniques, we numerically shows an estimated region of attraction which is important for safety. The validity is partially confirmed numerically.

Keywords: Power Systems Control, Nonlinear Control, Adaptive and Optimal Control
Abstract: This study proposes a model predictive controller for concentrating solar power (CSP) plants. Few studies have considered a thermal energy storage system and a power system. The proposed model predictive controller maintains the heat transfer fluid temperature by manipulating its mass flow rate and stabilizes the power frequency by balancing power supply and demand. In the case of the direct normal irradiance is low, the thermal energy storage system makes up for the shortage of thermal. The simulation using actual solar direct normal irradiance data and electricity demand data shows the proposed controller contributes to stable operation of CSP plants.

Keywords: Innovative Systems Approach for Realizing Smarter World
Abstract: With the development of information technology and the popularization of smart devices, crowdsensing has been widely accepted as a promising new paradigm. In the crowdsensing market, there are a large number of independent task initiators. On the other hand, The diversity of sensing tasks and the enhancement of smart devices make it possible for mobile users to accept multiple tasks simultaneously, which brings challenges to the design of task allocation and user incentives in a crowdsensing market full of competition. This study provides a new practical framework to meet the challenges. First, based on the non-cooperative game of mobile users, we adopt a co-evolutionary framework and design an algorithm to solve the global Nash equilibrium with constraints. Then, we design a price guidance strategy based on the task allocation method, and the provided guidance price of sensing tasks can ensure the initiators maximizing their benefits in the crowdsensing market.

Keywords: Intelligent Systems, Computational Intelligence, Intelligent Control
Abstract: We propose a cerebellar model articulation controller (CMAC) with a threshold learning cell (TLC). The CMAC is widely used for neural-network-based control, and we introduce the TLC to the CMAC. This CMAC + TLC has a simple structure and its learning algorithm consists only of comparison, increment, decrement, and count. Thanks to the simplicity of this learning algorithm, the advantage of the CMAC + TLC is its easy hardware implementation. We experimentally evaluated its function-approximation accuracy. The results indicate that the CMAC + TLC has a better approximation ability than the original CMAC.

Keywords: Intelligent Systems, Robotic and Automation Systems
Abstract: Visual odometry (VO) is an important and intensively studied localization technique that uses camera images for navigating autonomous mobile robot systems. VO estimates the relative robot position from two consecutive camera images. However, the accuracy of VO often deteriorates in less feature-filled environments, such as near a white wall. In this paper, we propose a novel active visual localization method using machine learning technique to perform and maintain accurate VO while the robot is moving around in unknown environments. Our proposed method tries to estimate the VO accuracy without actually moving to the location and then deciding the next command to obtain better VO. The key idea of our proposed method is using predicted images at virtual target poses, instead of moving into its position. The effectiveness of our proposed method was verified by applying it for a free exploration task assigned to a wheeled mobile robot in a 3D visual simulator, and comparing the results with the conventional active visual localization. According to the results presented in this paper, our method comfortably outperformed the conventional technique.

Keywords: Intelligent Systems, Signal and/or Image Processing, System Engineering
Abstract: Process monitoring plays an important role in process safety. Data driven model based latent variable monitoring has been successfully applied. The advances in image processing offer a new paradigm for monitoring as the images can offer spectral features and spatial features. Convolutional neural network are useful for feature extraction whereas variational autoencoders infer latent variables and carried out reconstruction of observation. In view of the powerful feature of the CNN and VAE, this work investigates the application of the convolutional neural network based variational autoencoder (CNN-VAE) to the monitoring of the combustion image. Unlike the conventional two-step process of process image the calculating the monitoring index, the CNN-VAE extracts the features and simultaneously provides a monitoring statistics. In additional, the adopted D and R index for monitoring are distribution index rather than point index. The proposed method is applied to flame images data from a combustion process to show its effectiveness.

Keywords: Molecular Robotics, System Engineering, Intelligent Systems
Abstract: Recently, a rationally designed method enabling the development of large-scale biomolecular circuits by combining three basic elements (catalysis, degradation, and annihilation mechanisms) like a Lego block was established. Although this method is instrumental in forming a functional biomolecular circuit, the circuit can operate normally only for a limited amount of time, which is a serious limitation in the field of DNA computing. In this study, we developed a conventional method of biomolecular implementation to make linear input-to-output systems renewable.

Keywords: System Engineering
Abstract: In software testability, there are various software measurement methods and metrics available for selection. To conduct the software testability evaluation, the combination of software expert tester judgement and appropriate metrics selection has reflected the of software quality accuracy. However, due to perspective of software expert tester, the software measurement and metric selection can lead to an inaccurate software testability result. Moreover, time and budget waste in conduct the software testability evaluation. Thus, this research aims to develop a testability measurement on model on Object-oriented programming software that will facilitate the accurate evaluation result. The investigation on how the factors of classes in a system influent the test effort. The research covers the gap analysis, automatic model creation with ANNs of AI Technology, and testability evaluation result comparison.

Keywords: Intelligent Systems
Abstract: Owing to the growing sophistication of rapidly developing wireless communications networks and systems, corresponding network traffic volume has similarly scaled in both proportion and complexity, posing a challenge in meeting user demand. Base station traffic forecasting is at the frontline of research currently as a response to meet the needs of future users. We propose an ensemble model that combines time series decomposition via Seasonal and Trend Decomposition using Loess (STL), individually forecast decomposed components with gated recurrent unit (GRU) neural network model, then recombine forecasts. The rationale behind the hybrid model is that the decomposition reduces the effect of noise and outliers in the time series data, thereby enabling improved results when forecasting traffic data as compared standalone statistical or machine learning techniques. Our proposed scheme shows performance gains in model forecast accuracy.

Keywords: Intelligent Systems, Human Interfaces
Abstract: In recent years, a lot of research work on action recognition are emerging. Nevertheless, there are only a few researches focusing on real-time applications, which usually require a human tracking system and can work at fast speed. The existing works still have some limitations in detection capability and slow speed. To address these problems, in this paper, we propose a lightweight deep learning architecture and a new construction of modality that suitable for real-time action recognition with a simple tracking system. The first step is to detect human in each frame by Tiny-Yolo and track the target by Euclidean distance from centroid. The second is to transform each input frame into a new modality, MRGB-Diff. The last step is the action recognition part, we reconstruct Conv3D and Conv-LSTM into a lightweight version and compare their performance. We use two datasets, KARD and BUPT-5daily, to evaluate the performance of our scheme and the reference schemes. Experiment results show that our approach can work efficiently with very good performance in terms of accuracy and speed in real time applications.

Keywords: FlowMeasurement and Control, Signal and/or Image Processing
Abstract: Technologies such as supercharging, exhaust gas recirculation, lean burn, and spark advance have been used to improve the thermal efficiency of DISI engine. However, the cycle-to-cycle variation of the combustion in the engine tends to occur due to various variation factors such as spray behavior, mixture formation, in-cylinder flow and so on. Therefore, it is important to clarify the source of the cycle-to-cycle variation to control the exhaust emission and fuel consumption in the engine. In this study, in order to clarify factor for cycle-to-cycle variation of fuel spray behavior, the effect of injection pressure on the variation of the spray behavior was investigated with the spray tip penetration measured by the shadow image technique. Moreover, the variation of the spray behavior was evaluated with a changing of the current supplied to a DI injector. As the result, it was found there was no much influence of injection pressure and the supplied current on the variation of the spray behavior. However, the variation of the spray behavior was three to four times larger than that of fuel quantity ejected from a DI injector.

Keywords: Process Automation
Abstract: In this paper, we propose estimation of the number of reference objects in an image using one-to-K feature matching. In the proposed method, the number of reference objects is estimated by using multiple candidate feature points using AKAZE features. A good estimation result is obtained for the created target image, demonstrating the effectiveness of this method．

Keywords: Signal and/or Image Processing, Computational Intelligence, Robotic and Automation Systems
Abstract: 3D object detection from point cloud is an important aspect of environmental perception in intelligent systems such as autonomous driving systems and robot systems. However, efficient 3D feature extraction and accurate object localization is challenging for current algorithms. In this paper, we introduce a new single-shot refinement neural network for fast and accurate 3D object detection. Firstly, we simplify the 3D feature extraction network and use single-shot object detector to increase processing speed. Secondly, we exploit self-attention mechanism in main object detection branch to improve object feature representation. Thirdly, an object refinement branch is introduced to produce a finer regression of objects upon the primary estimation from the main detection branch. Both modifications lead to further improvements in performance without additional computational cost. Our approach is tested on KITTI 3D Car detection benchmark and achieves good results in the validation set. The running speed is around 40 frame per second.

Keywords: Signal and/or Image Processing, Factory Automation, Robotic and Automation Systems
Abstract: Innovation in technology is playing an important role in the development of food industry, as is evidenced by the growing number of food review and food delivery applications. Similarly, it is expected that the process of producing and packaging food itself will become increasingly automated through the use of a robotic system. The shift towards food automation would help ensure quality control of food products and improve production line efficiency, leading to reduced cost and higher profit margin for restaurants and factories. One key enabler for such automated system is the ability to detect and classify food object with great accuracy and speed. In this study, we explore real-time food object segmentation using stereo depth sensing camera mounted on a robotic arm system. Instance segmentation on Japanese food dataset is used to classify food objects at a pixel-level using Cascade Mask R-CNN deep learning model. Additionally, depth information from the sensor is extracted to generate a 3D point cloud of the food object and its surroundings. When combined with the segmented 2D RGB image, a segmented 3D point cloud of the food object can be constructed, which will help facilitate food automation operation such as precision grasping of food object with numerous shapes and sizes.

Keywords: Signal and/or Image Processing, Guidance and Flight Control, Identification and Estimation
Abstract: This paper presents an alternative approach to identify and classify the small flying drones from the birds in near field, by examining the pattern of their flight paths and trajectories. The trajectories of the drones and birds were extracted from multiple clips of videos including various natural and synthetic database. Small drones that are piloted both automatically and manually usually fly in a stable manner. Their flight paths are usually straight and smooth with sharp angle turns. Whereas birds being natural flyers have flight paths that are intrinsically periodic due to their flapping motion with occasional straight glides and soaring sections. Five (5) trajectories characteristics and observed from the object’s flight paths: turning angle, periodicity (frequency), curvature, object pace (velocity and acceleration). Subsequently, principal component analyses were applied to reduce the number of these trajectory features from 5 to 2 parameters. Classification by support vector machine with Non-linear transformation kernel was used. Sample test results show that the prediction was > 80% accurate even at confident level of 90%. These predictions can be improved by gather more training data to cover more cases. Index Terms—Drone vs bird, trajectory characteristics, turning angle, periodicity, radius of curvature, principal component analysis, support vector machine, classification, non-linear transformation kernel.

Keywords: Signal and/or Image Processing, Identification and Estimation
Abstract: A method for estimating the walking speed of evacuees remaining in facilities at the time of a disaster using vibration sensors is proposed. Four types of features are extracted from the observations and are fed to a multiple lin- ear/nonlinear regression analysis. The experimental results show that the walking speed can be estimated by the proposed method with an accuracy higher than that of regression with a single feature.

Keywords: Signal and/or Image Processing, Identification and Estimation
Abstract: On a nondestructive exploration using a microwave radar, there are so many researches aiming an inspection of metallic buried objects, whereas those of nonmetallic one such as cracks are very few. In this study, we consider a method to discriminate and quantitatively evaluate an air gap. First, a relationship between a reflection waveform and the air gap thicknesses was confirmed by experiments. Next, we propose a method for discriminating air gaps using a phase change of the reflected waves based on a detailed propagation path model. Finally, we expand the proposed method to a quantitative evaluation method for the air gap thickness based on an amplitude of the reflected wave.

Keywords: Signal and/or Image Processing, Identification and Estimation
Abstract: Data collection by the internet of things (IoT) has been recognized as a key issue for creating new services or for improving existing systems. For a large scale deployment of sensor nodes for the data collection, the architecture so-called edge computing is attractive. ARS is a computationally efficient period estimation method suitable for the edge computing architecture. Toward further improvement of the efficiency, we can easily think about a possibility that averaging operations contained in ARS are replaced with bit shifts. This paper evaluates the performance of ARS with the bit shift compared with conventional ARS with the averaging. It is clarified that the performance degradation caused by the replacement is limited.

Keywords: Signal and/or Image Processing, Identification and Estimation, Agricultural and Bio-Systems
Abstract: Nowadays, single board small computers have been a great region of interest for their low cost and reasonable processing power in a compact board. In this paper, a vision-based automatic strawberry shape and size estimation for occluded and non-occluded calyx conditions will be implemented on Raspberry Pi 3 model B with Python programming for the purpose of embedded system implementation instead of software simulation. There are several research works for strawberry grading system but most of them are used with high performance CPUs which are more expensive and not as portable as Raspberry Pi. With machine vision technology, classification of strawberries can be time saving, reduce labour costs and avoid different judgements of each labour. A test image of strawberry is accquired with a webcam to lower the cost of system and a morphological operation is used to preserve the size and shape of strawberry after a segmentation task. Strawberries are classified into four classes and total number of 60 strawberry samples are tested. The average classification accuracy is 96%. The algorithm has been optimised to acquire high processing speed on Raspberry Pi development board regardless of its limited computing power. The average processing time for image processing and image analysis with capturing image of each strawberry sample is 0.6 seconds.

Keywords: Signal and/or Image Processing, Identification and Estimation, Agricultural and Bio-Systems
Abstract: This paper presents the development of an image processing algorithm to estimate the weight and volume of the mango. In this study, a total of 24 mangoes are used as samples. On account of simplicity, only a single web camera is used to capture the top view image of the mango. From this image, geometric features of the mango such as area, width and length are evaluated. Area of the mango's visible surface is determined by using curve fitting and area between the two curves method. Thickness of the mango, which is not visible in top view image, is necessary to estimate weight and volume more accurately. The required thickness value is calculated from the correlation equation between width and thickness. The thickness variation along the mango length is modelled as an ellipse function. The total estimated volumes of the mango is calculated by using the sum of volumes of transverse sectional elliptic cylinders method. The estimated results are compared with actual measured values. The average accuracy of this algorithm is 95% for both volume and weight estimation. Average processing time for each mango is 0.2 seconds.

Keywords: Signal and/or Image Processing, Intelligent Systems, Agricultural and Bio-Systems
Abstract: In this paper, we proposed a classification of Thai herb leaves based on the properties of image regions. The image regions that we considered are the leaves of herb images that placed over the smooth white background. The proposed method can be divided into three parts which are pre-processing, feature extraction, and classification. The pre-processing has performed to get the binary image from the input color of the herb leaves image. It composes of RGB to gray conversion, Histogram equalization, edge detection, and morphological techniques. After that, the binary image of Thai herb leaves is extracted with feature extraction using the region properties which are majority axis, minority axis, aspect ratio, eccentricity, circularity, extent, perimeter, and the ratio of the perimeter and majority axis. After obtained the feature vectors, an artificial neural network is executed to help classification. The experimental results demonstrate that the classification performance of the proposed method in term of accuracy achieved 90.50% on average

Keywords: Signal and/or Image Processing, Intelligent Systems, Human Interfaces
Abstract: This paper introduces a method to analyze the dynamics of a spontaneous smile expression that is induced by genuine positive emotion, and a posed smile that conceals an underlying non-positive emotion for serving a social purpose. In this research, we used a Quasi Recurrent Network (QRNN) to recognize if a sequence of smile images is spontaneous or posed one. Finally, the network's parameters are visualized for further analyzing the semantic relationship between facial expressions and emotions.

Keywords: Signal and/or Image Processing
Abstract: This article presents a design of high-order Elliptic ladder high-pass filter based on differentiators and integrator approach. Since the design of general Elliptic high-pass filter can be realized with high complexity, moreover the high-pass filter held very complicated design. The differentiators and integrator are deployed for synthesis high-pass filter from its Signal Flow Graph (SFG). The Elliptic RLC high-pass filter is transformed by using the original Elliptic RLC low-pass filter. The SFG is straightforward analyzed from the Elliptic RLC high-pass filter prototype. The SFG describes three differentiators and one integrator. The operational transconductance amplifiers (OTA) and grounded capacitors are preferred to implement the differentiators and integrator. The proposed filter contains only 7 OTAs with 4 grounded capacitors. The frequency responses can be varied between 100kHz and 10MHz by manipulating the bias current (IB) from 1A to 100A. The performances and the capability are carried out by using PSpice simulation which agreed to the theory.

Keywords: Medical and Welfare Systems, Human-Machine Systems, Mechatronics Systems
Abstract: The rapidly aging population of Japan has created one of the most super-aged societies in the world. Elderly people with low muscle strength often find it difficult to stand up from a sitting position. We have developed a device to assist people with the motion of standing up, optimized for specific user characteristics. In this paper, we analyze the standing-up movement using sensors installed in the device and evaluate the efficiency of the support provided. We demonstrate the enhanced efficiency provided by optimizing the support device for the user’s physical characteristics.

Keywords: Medical and Welfare Systems, Innovative Systems Approach for Realizing Smarter World, Social Systems
Abstract: Gerontechnology is a promising interdisciplinary research and practice area connecting two very diverse fields, namely demography and technology. Older adults are exposed to risks of events leading to ill health and disability or even death. Gerontechnology can support older adults to live independently in their own environments and can improve their overall satisfaction with life. Similarly, smart homes using wireless sensors networks embedded in cyber-physical systems, internet of things as the infrastructure and big data and machine learning, can support older adults to be independent longer in their communities and mitigate the risks leading to ill health and disability. The development of technologies in the last 20 years has opened up the possibilities of developing various models, tools and techniques for supporting older adults to live safer and more connected community lives. The central gap found in the literature review is the lack of the common dataset for measurement of the quality of technologies embedded in a smart home and recording of patterns of decline in functional capacities, which can cause events leading to ill health and disability. Based on these developments, we propose the multistate competing risk model for control of events leading to ill health and disabilities. This paper aims to review the development of control in gerontechnology and ambient assisted living technologies, to shape the model and propose a future research agenda.

Keywords: Medical and Welfare Systems, Signal and/or Image Processing, Ubiquitous Healthcare
Abstract: Fall accidents are among the most serious problems at medical centers as numerous accidents occur at the bedside of a patient. To resolve this problem, the authors have proposed a method using a point cloud dataset (PCD) with images obtained from a depth camera to calculate the fall risks of hospitalized patients. However, in the conventional method, there is a strong constraint that the depth camera must be placed at a particular position above the bed; thus, it is occasionally difficult to install the camera using the conventional system. To remove this restriction, this study adds a method for correcting an original PCD to the conventional method and proposes it as a new system. Through verification, it was demonstrated that this method could convert the PCD measured at the bedside of a patient into a bed-top-view dataset. Moreover, even after the correction of the PCD, the precision of fall risk estimation remains unchanged; therefore, a system that relaxes the constraint of depth camera location and is compatible with various hospital rooms has been realized.

Keywords: Medical and Welfare Systems, Social Systems, Innovative Systems Approach for Realizing Smarter World
Abstract: Rural municipalities in the European Union are ageing faster than urban areas. Consequently, the digital transformations of health and social infrastructure and the development of smart villages have the potential to support the growing number of older adults with declining functional capacities. In this context, social innovations based on the digital transformation of health care and social care delivery systems can support older adults to live autonomously and independently in their communities and postpone or even prevent entering nursing homes. This paper aims to review the research on Smart Villages and to propose a future research agenda.

Keywords: Medical and Welfare Systems, System Engineering, Computational Intelligence
Abstract: In recent years, early detection of fracture risk has been emphasized for the prevention of fractures, which are one of the causes of nursing care. Faculty of Medicine Tottori University of School of Health Science and Kawasaki University of Medical Welfare obtained the epidemiological data of 397 tests (e.g., muscle mass, grip strength, and walking ability) in 468 elderly people in Hino-cho, Hino-gun, Tottori, Japan, to investigate the causes of fractures. In this study, we used principal component analysis on the acquired epidemiological data to extract the inspection items that were highly related to fractures. The results showed that the risk factors for fractures in males were muscle mass, body fat percentage, and walking ability, while those in females were fat mass, muscle mass, leg points (ratio of leg muscle mass to body weight), and walking ability. Based on the extracted risk factors, a fracture risk prediction map (FRPM) was constructed to indicate the distribution of subjects with a particularly high fracture risk. FRPM is an easy-to-understand tool for patients, which allows both male and female patients to see their own fracture risk and therefore contributes to the education of the elderly.

Keywords: Medical and Welfare Systems, Ubiquitous Healthcare
Abstract: A urinary tract infection (UTI) is an infection of the bladder or kidneys. UTIs may be caused by germs due to poor hygiene and tubes placed in drain tubes, and occur in some elderly living in nursing homes. In clinical situations, most caregivers help residents with urination by using diapers or catheters. However, the use of both medical devices has a risk of infection and requires caregivers’ labor and time to change. If it is possible to predict the urinary volume in the bladder without attaching any sensors to the body, it will ease the burden on caregivers and prevent residents from contracting the infection by not using diapers and catheters. Therefore, we propose a method for predicting urinary volume in the bladder without attaching a sensor to the body. In this study, the prediction phase is divided into two steps: First, we predict urinary volume in the bladder based on a model that demonstrates how urine accumulates in the bladder considering the absorption spectrum of urine obtained immediately after urination. Second, we correct urinary volume predicted in the first step of the multi-task Gaussian process (MGP). We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted by the proposed model at the time of urination. The mean error rate of the proposed method was 8.40%.

Keywords: Medical and Welfare Systems, Virtual Reality Systems, Human Interfaces
Abstract: A projected virtual hand interface, which visually extends arms of users using projection images, enables wheelchair users to reach unreachable objects; however, its projection area is narrower than the reaching area required for the users. To address this problem, we propose a wheelchair system enhanced with a projected virtual hand that allows controlling a projection area using user's head orientation. The proposed system estimates the current orientation of a user's head and controls the pan and tilt of a projector accordingly to move a projection area considering the positional relationship with a projection plane. As users can operate the projection area simply by turning their head, this operation can be executed simultaneously with operations of a virtual hand using their hands. We propose a control model for projector rotation according to the user's head direction. The conducted user experience study has revealed that the proposed method enables users to perform pointing tasks in a shorter time compared with the existing method, and moreover, it has acceptable interface usability.

Keywords: Robotic and Automation Systems
Abstract: In this paper, we focus on an aerial surveillance system composed of multiple agents, such as sentinels and searchers. In the surveillance system, single sentinel detects a position of an incoming target, and multiple searchers approach and identify the target. In order for the searchers to identify moving targets as many as possible, it is required to optimize the approach behavior in consideration of the future positions. For these challenges, we use a directional statistics and value iteration method. Through simulation experiments, we show that the surveillance system is able to estimate the future positions of a target. We further show that the searchers based on the optimized approach behavior identify the most moving targets. Finally, we discuss the effectiveness of the surveillance system for the moving targets.

Keywords: Robotic and Automation Systems
Abstract: For autonomous navigation of mobile robots, obstacle avoidance capability and straight-line stability are essential performance. Robots are required to move with suitable control values regarding velocity and angular velocity. We have focused on motion planning techniques and proposed end-to-end discrete motion planners. In one of the motion planners, a deep neural network, DNN, was used to decide motion commands, such as straight, right, and left. In environments with obstacles, the robot based on the motion planner autonomously moved with discretized control values depending on the motion commands. In this paper, we further present an end-to-end continuous motion planner that allows the robot to move with continuous control values. In the navigation experiments, these two motion planners are applied to the robot, and the navigation performance are discussed.

Keywords: Robotic and Automation Systems
Abstract: Many studies on bipedal robots have been reported. However, most of them focus on the structure and behavior of the lower body. In this study, we investigate the effect of arm swinging with a flexible trunk in a biped robot and realize natural walking. Our previous studies have shown mathematical analysis that the Center of Pressure(CoP) oscillates sideways by asymmetrically swinging arm and passively oscillating the viscoelastic joint embedded in the trunk. This paper shows that the passive oscillation of the flexible trunk by swinging the arm enables walking by driving the leg. It was confirmed that the physical robot achieved approximately 20 steps by adjusting the air supply and exhaust period of pneumatic artificial muscles that drive the joints.

Keywords: Robotic and Automation Systems
Abstract: We design classical apparatuses to measure the weighting factor of eigenstates without breaking the quantum mechanical superposition state. The apparatuses work as ``quantum'' computer even though they follow classical physics. To do quantum computation, we need to know what percentage of a particular state is included in a superposed quantum state. However, we face difficulties that large-scale equipment, such as cryogenic temperature, is required to maintain the overlap. Combining causal formulation of quantum mechanics and mediocre classical mechanism overcomes the situation. The weighting coefficients of the superposition can be determined by monitoring the trajectory of a classical particle under quantum flucutation force. Such monitoring is possible only in macroscopic physics. Efficacy of our macroscopic apparatus model is shown in one of typical quantum algorithms, Deutsch algorithm. This classical mechanical design opens the way for ``quantum mechanical'' artificial intelligence to be mounted on robots in usual macroscopic world.

Keywords: Robotic and Automation Systems
Abstract: Path planning is one of the vital tasks in the intelligent control of autonomous robots. It is of prime importance from industrial as well as commercial point of view. Various autonomous vehicles are gaining popularity to reduce the on-field intervention of humans. The path planning problem is divided into two sub-problems: finding the feasible node pairs and then evaluate the shortest feasible path from the obtained feasible node pairs. Feasible node pair is the part of the path which do not pass through any obstacle. For the known static arena, the most popular method to solve these kinds of problems is the visibility graph (VGraph), but it is computationally intensive. This paper proposes an Enhanced Linear Graph (ELGraph), a method for finding feasible node pairs. The ELGraph has simplified linear structure which exploits the concept of line segment intersection, which may take low computational time. The simulations were carried out for VGraph and ELGraph to get the feasible node pair, and then Dijkstra algorithm evaluates the shortest feasible path. The results obtained shows that ELGraph takes lesser evaluation time and is computationally lesser intensive than VGraph, without compromising on finding the shortest possible feasible path.

Keywords: Robotic and Automation Systems, Human Interfaces, Mechanical Systems Control
Abstract: We have been developing a six degrees of freedom (6DoF) vertically articulated robot with integrated torque sensors. As an application of joint torque sensing, the robot provides lead-through programming (LTP) function for intuitive teaching. This paper describes a LTP control method, which consists of existing LTP method and newly developed two control methods. One of them is start assist control. It reduces torque to guide the robot staying at a position, and thus also reduces operators’ fatigue during manipulation. The other is brake control. It prevents the robot from moving after guided, and thus reflects operators’ manipulation accurately on robot movement when they are trying to finish guiding. Experiments on the proposed LTP method are performed with a prototype robot.

Keywords: Robotic and Automation Systems, Intelligent Control, Mechanical Systems Control
Abstract: The necessity of redundant robot arm is increasing with demands of automatic production line, and extreme environ-ment problem such as nuclear plant dismantling. There are many methods to calculate trajectory of redundant manipu-lator in globally or locally. In real application, there are possibility to confront sudden obstacles or disturbance. To cope with these problems, the calculation should be in real-time and locally. Obstacle can change the workspace, and it can cause various problems such as a surge in speed or acceleration, or fail-ure to reach the goal position and orientation. This paper presents optimal method combined with configuration con-trol and potential field to calculate local trajectory in real time while ensuring joint limits, goal orientation and posi-tion. The simulation results based on 7 degree of freedom redundant manipulator, with simulation of cylinder welding trajectory.

Keywords: Robotic and Automation Systems, Intelligent Systems, Identification and Estimation
Abstract: The GNSS RTK has attracted the attention of many researchers by significantly improving positioning accuracy and enabling accurate waypoint navigation for mobile robots. The key to autonomous waypoint navigation is accurate self-positioning and accurate path generation to the waypoint destination. In this paper, we describe the development of a navigation system that can compensate for the path of a mobile robot using GNSS information. The system compares the waypoint path generated by the odometry to the trajectory generated by the GNSS RTK. If the position of the mobile robot obtained from these results is different, the one that is more accurate is automatically selected and self-positioning is performed. The results confirmed the validity of the proposed method.

Keywords: Robotic and Automation Systems, Intelligent Systems, Signal and/or Image Processing
Abstract: In this paper, we propose a method to search the specified person using a spherical camera and a 3D range sensor. This is one of the items of the Tsukuba Challenge. By using the camera and the sensor, the view field becomes wider than the conventional system. We employed color information to reduce the computational load in image processing. In addition, we focused on the view size of the target person to improve the accuracy of LiDAR. The proposed component is implemented as a ROS-based application, which allows for rapid verification as well as rapid prototyping. The validity of the proposed algorithm was confirmed by preliminary experiments simulating the Tsukuba challenge environment.

Keywords: Robotic and Automation Systems, Intelligent Systems, Signal and/or Image Processing
Abstract: This paper describes the development of an algorithm for detecting blocked route signboard in an autonomous mobile robot using a combination of omnidirectional camera and 3D-LiDAR. A signboard recognition technique is used to detect a blocked route signboard. An omnidirectional camera and 3D-LiDAR are used for stable signboard detection. The omnidirectional camera is mainly used for stable color detection regardless of outdoor sunlight position, while the 3D-LiDAR is used for the retroreflective painted areas used in the signboard and for detecting the shape of the known signboard dimension by point cloud extraction. The validity of the proposed algorithm was confirmed by detecting blocked route signboard with the same dimensions as those defined in the Tsukuba Challenge rule.

Keywords: Manufacturing Systems, Robotic and Automation Systems
Abstract: This paper discusses a passive deformation control of elastic fingers of a robot gripper. The passive deformation control is performed using deformation constraint members arranged outside the elastic fingers that are largely deflected outward after gripping the object. After the contact of the fingers with the deformation constraint members, the elastic deformation behavior of the fingers is changed. The effect of the passive deformation control on gripping force was examined through simulations for a wide range of locations where the deformation constraint members can be arranged. The maximum gripping force was largely different depending on the deformation constraint member arrangement and ranged from 0.828 to 2.51 times that without the passive deformation control.

Keywords: Mechatronics Systems, Robotic and Automation Systems
Abstract: The purpose of this study is to develop a tactile feedback device for electromyographic hand users. The proposed system transmits the tactile sense to the user by the stimulation of vibration and pressure, assuming that it is mounted on the finger of the artificial hand. A vibration sensor can be used to determine the surface roughness of an object through the amplitude of vibration at the fingertip. Using the pressure sensor, the hardness of the object can be discriminated through the force applied to the fingertip. Finally, vibration discrimination experiments were conducted to confirm that both stimuli are sufficiently available for tactile feedback devices.

Keywords: Autonomous Decentralized Systems
Abstract: This study presents a bio-inspired decentralized controller for multi-agent systems to track a target value and satisfy the following: (1) the controller requires no communication between agents; (2) the control inputs of the agents are alternately set to zero in response to the changes in the environment; and (3) the number of agents can be changed during tasks. To fulfill these requirements, the decentralized controller uses only the target error measured locally or broadcasted. The proposed controller is a nonlinear oscillator that plays the role of switching the binary input. This is inspired from the mechanism of the interlimb coordination in the locomotion of quadrupeds. We first verify the proposed controller by applying the speed control experiment of a three-motor system. We then validate the controller in a numerical experiment to control the velocity of a rigid object with a variable massive number of agents (maximum: 100 agents). These experiments confirm that the proposed controller works even when the environment is changed (e.g., existence of an unknown disturbance and increasing or decreasing of the number of agents) and automatically achieved while some agents stopped alternately.

Keywords: Autonomous Decentralized Systems
Abstract: This work studies formation control of double integrator agents where its formation size is scalable via a scaling factor (scalable formation). Previous works on scalable formation are limited to either fixed undirected graph or fixed directed graph with p-rooted graph condition. This work proposes a novel distributed algorithm for scalable formation based on a distributed observer to estimate agents' positions. The novel algorithm results in a new matrix, a Laplacian matrix with additional diagonal terms, where the desired formation pattern is embedded in this diagonal terms. With the proposed algorithm, only spanning tree condition is needed as the necessary and sufficient condition to achieve the scalable formation. Numerical examples are shown to illustrate the proposed method.

Keywords: Autonomous Decentralized Systems, Multivariable Control, Nonlinear Control
Abstract: This paper studies continuous-time algorithms for solving consensus optimization problems over P2P networks. The consensus optimization is one class of distributed optimization problems. In this problem, each agent has an independent objective function but finds the same decision variables. To solve consensus problems, secure and robust algorithms over the P2P networks are proposed in this paper.

Keywords: Autonomous Decentralized Systems, Safety, Environment and Eco-Systems, System Engineering
Abstract: For smooth and reliable evacuation in the event of a fire, it is necessary for each evacuee to take into account "control of the number of people using the same exit and escape route and timing" and "rescheduling that adapts to the ever-changing fire situation" without the need for a management server or an external power supply. This study aimed to construct an autonomous evacuation guidance system that uses screen displays and voice guidance on a dedicated device in each room of an operational facility. In this system, all information is transmitted only as beacons. This paper describes the communication between a fire alarm and a dedicated device, sharing of fire information between the dedicated devices, self-position estimation of the dedicated device, and an evacuation guidance experiment that integrates them.

Keywords: Mechatronics Systems, Transportation Systems, Sensors and Transducers
Abstract: The water depth is an important information for the ship navigator when the ship enters the port. However, the depth is not always the one indicated in the chart due to tides and sedimentation. This study aimed to develop an underwater robot to easily measure the water depth. To improve the sounding accuracy, this robot was equipped with two sensors: sonar and water pressure gauge. In places where the sounding accuracy with sonar was unsatisfactory, the underwater robot was sunk and the water depth was measured using a water pressure gauge. Actual sea area experiments indicated the sensor fusion effectiveness.

Keywords: Transportation Systems
Abstract: This paper presents a method for estimating the traffic volume for multiple time zones using an origin-destination (OD) traffic volume survey. The traffic volume of each road link is calculated with the aid of a user equilibrium assignment model. The data commonly used for such calculation is traffic volume between the origin and destination points in one day, which provides an estimate of daily average traffic; this is known as the primary aggregated data. Existing research methods divide this daily traffic volume by time and assign it to road links. However, a problem with this method is how to handle traffic remaining from previous time zones. In this paper, we construct a road network with road links for each time zone. When calculating and assigning the O-D traffic volume for each route, the time required for the vehicle to reach each road link from the origin point is determined, and the traffic volume is assigned according to these measurements.

Recent years have seen the advent of high-speed processing for big data, a hence, it is possible to use original data from person trip (PT) surveys. Since this data includes the O-D and travel start time of each trip, it is possible to improve estimation accuracy. In this paper, we present a calculation algorithm based on dynamic assignment and show its validity using simple examples.

Keywords: Transportation Systems, Autonomous Decentralized Systems
Abstract: In this paper, we propose a novel methodology of local trajectory planning (LTP) for a lane-change maneuver during a path tracking control. We designed the lane-change trajectory between two different paths, which given in the form of waypoints. Then, a trajectory tracking controller was employed. To validate the proposed LTP method, we collected the waypoint data by an actual driving and used the data for the lane change simulation. In the experiment, we used full-state feedback control for lateral motion. To investigate the improved performance by the proposed method, we also made a comparative study between the proposed method and a simple lane-change method without LTP. We observed that the proposed method showed even stable lane-change performance than the method without LTP.

Keywords: Transportation Systems, Intelligent Systems
Abstract: As ships in a large bay are headed to various destination ports, there is a high possibility that these ships will collide with each other. In congested sea areas, veteran navigators predict the movement of other ships and evacuate in advance to minimize the danger of collision with other ships. Therefore, this study aimed to predict the movement of other ships, similar to veteran navigators, using big data of ship traffic flow in a bay and a machine learning system. By learning ship traffic data using the machine learning system, a model to recursively calculate the trajectories of ships was constructed.

Keywords: Intelligent Systems, Signal and/or Image Processing, Computational Intelligence
Abstract: In the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop an intelligent method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub–band analysis for Japanese Yens.

Keywords: FlowMeasurement and Control, Identification and Estimation, Process Automation
Abstract: We propose a Kalman filter-based method to estimate the molten steel surface shape in a continuous casting machine mold. The state-space equations are derived from the surface wave model expressed as the sum of sinusoidal basis functions, and the expansion coefficients of the basis functions are estimated from height information measured by level meters at some points of the mold. The effectiveness of the developed method was verified by demonstration experiments that used a mold water model device.

Keywords: SCADA and Network Systems
Abstract: Cyber security standards focused on industrial automation and control systems (IACSs) such as ISA/IEC 62443 series are useful to establish security programs for IACS related organizations. ISA/IEC 62443 series has already been referred by various industries and become a strong influence. This paper provides an overview of ISA/IEC 62443 series and the latest trends of the standards including safety issues and conformance certification programs.

Keywords: Nonlinear Control, Adaptive and Optimal Control
Abstract: Recently, data-driven controller design methods represented by the virtual reference feedback tuning and the fictitious reference iterative tuning have been attracted much attention because these methods can directly obtain optimal control parameters without any system models. In this presentation, as one of the data-driven controller design approaches for nonlinear systems, the database-driven controller design approach that can obtain optimal parameters at each equilibrium point is introduced. This approach is based on the idea of just-in-time modeling, however, our proposed method extends the method by an off-line updating algorithm. This paper presents the effectiveness of the proposed method through experimental examples.

Keywords: Computational Intelligence, Process Automation
Abstract: New value is being created by the remarkable development of Artificial Intelligence (AI) technology. From viewpoint of the Industrial Automation (IA) industry, various problems such as increase in measurement data due to the development of Industrial Internet of Things (IIoT) technology, expansion of the operation target range, variation in material quality, shortage of skilled technicians, etc. have being occurred, and AI technology is expected to solve these problems. Under such circumstances, YOKOGAWA aims not only to analyze measurement data by using AI technology, but also to realize operation that can achieve customers goals by utilizing domain knowledge. This policy is summarized as “knowing the present, predicting the future, optimizing operations” and delivering it as AI Vision. This paper report on the issues that are expected to be solved in the use of AI technology in the IA industry. In addition, we will also introduce our AI-related products and service that embody YOKOGAWA's AI Vision, as well as future-oriented technologies such as automated plant operation.

Keywords: Adaptive and Optimal Control, Process Automation
Abstract: In this paper, we utilize FRIT to update the controller for a cold tandem rolling mill which is mainly used to process a metal such as aluminium under normal temperature. It is important to process the metal uniformly with respect to thickness and material characteristics. On the other hand, the dynamics of the metal and the tandem mill system are complicated and with large scale, so it is also difficult to maintain the desired characteristics and a required accuracy in the thickness of the processed metal. Here, we apply FRIT to reduce the effect of deformation disturbance. Finally, examples are illustrated to show the validity and effectiveness of our approach to control of cold tandem rolling mill by using the numerical simulator.

Keywords: Networked Sensor System, Identification and Estimation
Abstract: The IIoT sensor in the plant automatically collects the equipment status information of the site, which has been collected manually until now. Further, it is automatically transmitted to the host system. As a result, data storage and monitoring are facilitated. By using this feature, we aim to reduce the work load on site and also to collect detailed information on equipment that plays a part in the realization of CBM (Condition Based Maintenance). However, if the installation and monitoring are performed in the same manner as the conventional way, it requires a great deal of manpower and time and much know-how. In this research, we will describe the results of verification by considering the method of reducing the burden of trend monitoring itself while facilitating the introduction of the trend monitoring system using IIoT sensor with machine learning technology.

Keywords: Power Systems Control, Simulation of Large Systems, Social Systems
Abstract: The introduction of a significant amount of variable renewable energy (VRE) destabilizes power systems due to the decrease in the number of conventional generators connected to power systems. In the destabilized power systems, the fluctuated power system frequency due to VRE output power disrupts operation of electrical appliances at consumers, power-systems are disrupted after faults such as ground fault on transmission line, the total power generated by VRE may exceed the power-transfer limit. Stability evaluation and proactive measures are required to maintain stability. We developed a power-system simulator for stability evaluation, which consists of power generation planning for calculating generator operation based on future demand and renewable energy, power-system modeling with topology such as connection of transmission lines and generator dynamics, and practical evaluation such as stability, economic and environmental impact. The simulator can also simulate a future power system and analyze various destabilizing conditions and the measures. We give an overview of this simulator and discuss destabilization due to introduction of VRE and stabilization by suppressing its output power.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: This paper proposes an improved particle swarm optimization based Q-learning (IPSO-Q), which is a novel swarm reinforcement learning (SRL) technique. In addition, application of the proposed IPSO-Q to operational planning of energy plants (OPEPs) in buildings and factories is also proposed. The results of the proposed method are compared with those of the conventional PSO-Q and differential evolutionary particle swarm optimization based Q-learning with Advantage (DEEPSO-Q w/A) based methods. It is verified that the proposed method can reduce both energy costs and standard deviations the most among the three applied methods. It is also verified that the proposed method can reduce calculation time among the three applied methods.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: This paper proposes a correntropy based Artificial Neural Network (CANN) using early stopping for daily peak load forecasting. The proposed method utilizes stochastic gradient descent with momentum (SGDM) as a learning algorithm. In order to provide an electric power generation schedule for pumped-storage hydro plants and gas turbine power plants operated in peak load periods, it is important to forecast the daily peak load accurately in advance. Correntropy and early stopping are applied in order to prevent over-fitting of ANNs. Effectiveness of the proposed method is verified by comparison with the conventional least mean square based ANN (LANN) using SGD, a LANN using SGDM, a LANN using Adam, and a CANN using Adam.

Keywords: Process Control, Adaptive and Optimal Control, Intelligent Control
Abstract: Deep reinforcement learning (DRL) for process control is one of challenging applications of state-of-art artificial intelligence (AI). It has been proven that DRL has a strong ability to learn superior strategies for complex tasks such as igo, video game playing, automated drive, and so on. For many years, model predictive control (MPC) has been a main successful control method in industrial control. And the authors have proposed an approach for MPC combined with DRL to achieve more precise and adaptive control. In this paper, we extend the approach to avoid oscillation of manipulated input. We propose a novel policy which blends actions according to the trained Q-function in DRL.

Keywords: Adaptive and Optimal Control, Process Automation, Agricultural and Bio-Systems
Abstract: This paper is concerned with the applicability of extremum seeking (ES) control for wastewater treatment plants (WWTPs) under several practical constraints such as effluent regulations and/or operational constraints. Considering such constraints to optimize a trade-off between effluent improvement and operational cost reduction is important in real WWTP operation. To meet such practical requirements, this paper proposes a novel ES scheme incorporating multiple output constraints where the priority of each constraint is also considered. Such priority becomes important when one or more of the constraints cannot be satisfied, which will not be the exception in real WWTP control problems due in part to the considerably large inflow load variation. To make ES be effective even in such situation, the paper proposes a prioritized output constraints ES scheme incorporating switching functions to change the performance index for minimization depending on the active constraints. It is suggested that such switching mechanism is preferably combined with the gradient-sign-based ES that was also proposed by the authors to mitigate the sudden change in the curvature of the performance index caused by the switching functions. Some numerical simulations of the proposed ES with a simple model are performed to illustrate the principle of the proposed idea at first, and then the effectiveness of the proposed ES to WWTPs is demonstrated by using a complex nonlinear biological model.

Keywords: Standard of Measurement, Factory Automation
Abstract: As the problem of global warming becomes more serious, the need for energy management becomes more and more important. The IEC / TC65 Japan National Committee is working on the standardization of FEMS (Industrial Facility Energy Management system). This article describes the current trends in energy management standardization, the need for total optimization in FEMS, and international standardization approaches.

Keywords: Analytical Measurement, Innovative Systems Approach for Realizing Smarter World, Adaptive and Optimal Control
Abstract: Nowadays, traditional retail stores have greatly reduced their popularity. Due to the increasing number of modern shops, they have been attracting more people to enter their shops and use their services. Modern stores are better able to meet the lifestyles of people. Consequently, the GREAT model has been developed. It is suitable for traditional retailers in Thailand, which will enhance the competitive power of the local traditional stores and manage the stores to be more systematic and effective. It was designed and analyzed based on the marketing theory, whether as SWOT, 7P, and business model canvas, with additional questionnaires collected from the 400 owners of local traditional stores. To understand the perspective of traditional shops, a gap was being discovered and provided this model a complete and comprehensive development in every dimension. When store owners utilize this model, it will significantly help them get a better overall image of their business operations and conduct business with clear guidelines and procedures. In addition,this model would be able to help in identifying weaknesses and reinforcing strengths. As a result, traditional stores would be able to develop themselves to be the efficient stores for their community along with the enhancement of the Pracharat Blue Flag project and survive in this era of digital transformation.

Keywords: Information Management Systems, Standard of Measurement, Advanced Computics
Abstract: Currently, digital transformation is important to the business. Most would move to serve customers with online self-services and automation processes. One of the key successes is the identification and authentication process using facial recognition. This paper experiment with the accuracy of the acceptance algorithm by measure the error rate by calculating false acceptance (FAR) and false reject rate (FRR) from the test set by matching all faces together. Ideally, this method should be measured by the total number of populations in the markets but it’s not practical to get all the data. Therefore, the experiments applied with the appropriate number of test set by using various data sets from 100, 200, 400, 800, 1500, and 3,000 faces, the experiment show the error rate did not change when the size of the test set increased.

Keywords: Innovative Systems Approach for Realizing Smarter World
Abstract: Nowadays, Tablet plays a key role in the creation of artwork, especially for the artwork creators. Tablet can be used with a stylus pen which can be drawn directly on Tablet’s screen with the same feeling as drawing with a pencil on a sheet of paper. The stylus pen has been continuously developed and improved for higher performance. There are many brands of stylus pen -in Thailand which popular among the young students who are the target user group of this type of devices. This research aims to study factors that influence the acceptance of digital painting on tablets by using the Technology Acceptance Model (TAM) and the application factors of Modified Unified Theory of Acceptance and Use of Technology (UTAUT2) as the theoretical basis of the research. The survey was conducted on 209 students from year 1-4 of Media Art Faculty.

Keywords: Analytical Measurement, Social Systems, Adaptive and Optimal Control
Abstract: Demand and supply can be met easily using the modern business model called the sharing economy. In this work, the business models of service cars using software platforms are focused. Most of the previous work focuses on the passenger part, while in our work, the car driver or the asset owner part is focused. The loyalty factors of car drivers to software platforms are investigated in this work. The factors are initially collected from the literature review. After that, the in-depth interview based on grounded theory is applied to discover new factors arising apart from the literature review. From in-depth interviews with the experienced taxi drivers, we found that factors affecting partner loyalty in sharing economy consist of 5 dimensions with 18 factors. These factors have value for developing an application that meets the needs of partners of sharing economy.

Keywords: Modeling, System Identification and Estimation, Process Automation, Information Management Systems
Abstract: The management of foreigners in Thailand is an important issue that may have both positive and negative effects. Therefore, a good foundation for management structure is required. According to the immigration law, the collection of addresses of foreigners involves three topics related to immigration rules: arrival card (TM.6), Notification of Residence (TM.30), and 90 days report. Arrival card (TM.6) requires the foreigner to present to the officer at the gate. Notification of Residence (TM.30) requires the residence owner to send a report to immigration when the foreigner present in the place within 24 hours and 90 days report (TM.47) requires the foreigner with the long-stay visa confirm their stay every 90 days. Although the process is not complicated, some parts of the processes have failed to implement and process duplication. This research aims to propose an appropriate flow of residence tracking. This research considers using root cause analysis tools to assess and analyze the problem to design the new state of the residence tracking process.

Keywords: Mechanical Systems Control
Abstract: We examine a fault tolerant control system. Past studies of design method for the fault tolerant control system are classified into two types. One is a method using a simultaneous stabilization problem. The other is a method using a disturbance rejection problems. However, these methods are complicated to design a fault tolerant control system. In this paper, we propose a simple design method of a fault tolerant control system using fault detector

Keywords: Adaptive and Optimal Control, Guidance and Flight Control
Abstract: This paper proposes a PID gain tuning for disturbance attenuation based on a data-driven controller parameter tuning method. The study considers a case where the position control is required, and a velocity signal is available from the controlled plant. In such a situation, a cascade controller structure is available, which has an inner velocity feedback loop and an outer PI control loop. The present work provides a disturbance attenuation FRIT for PID gain tuning with the cascade controller structure. The proposed method is applied to a drone height control problem, and the effectiveness of the proposed method is shown through experimental results.

Keywords: Adaptive and Optimal Control, Nonlinear Control
Abstract: The study examines the effect of damping parameters on the continuation method in linear continuous-time model predictive control. The continuation method mitigates the calculation effort of model predictive control, and realizes on-line implementation of the model predictive control. In such a continuation method, the damping parameters work as design parameters, and it has been shown that they contribute to realize numerically stable calculation. However, they have a possibility to give some effect on the control performance evaluation. The present work focuses on linear continuous-time model predictive control, and shows the relation between the damping parameters and the characteristics of the closed-loop system. Using numerical simulation results, the analysis of the present work will be demonstrated.

Keywords: Adaptive and Optimal Control
Abstract: Data-driven controller parameter tuning allows us to design controller parameters directly from input and output measurements of the controlled plant. This approach requires no mathematical model of the controlled plant, but it should determine the reference model that characterizes the specifications beforehand. However, the existing studies still have not establish adequate design of the reference model. The present study provides a reference model design based on the prediction of closed-loop response with updated control parameters. The pre-filter design for improving preciseness of the prediction is also provided. The effectiveness of the proposed method is shown through a numerical example.

Keywords: Identification and Estimation, Modeling, System Identification and Estimation, Components and Devices
Abstract: Most strategies of motor control are based on mathematical models. Therefore, the proper parameters of interior permanent magnet synchronous motor (IPMSM) are considerably required, especially their inductances, which have much sensitivity. This paper presents using the affine projection algorithm (APA) to estimate the inductances. APA guarantees a fast convergence rate and good tracking capability. It also has simple computation, which makes it a suitable method for real-time control. The simulation results firmly verify the proposed method.

Keywords: Nonlinear Control, Computer Aided Design, Mechanical Systems Control
Abstract: This paper reports a numerical study for the speed control to suppress the vibration of mechanical systems with backlash. In the study, the mechanical system is modeled with a three-inertia system. We build this system by using not a simple model like the dead-zone model, but the exact backlash model to analyze the influence of backlash nonlinearity more accurately. For this system, we design a fractional PID controller, whose orders of I controller and D controller are expanded to any real number, based on the idea of fractional calculus. Then, this paper verifies the effectiveness of the exact backlash model and the fractional PID control method by computer simulations.

Keywords: Robotic and Automation Systems, Modeling, System Identification and Estimation, Computer Aided Design
Abstract: In this paper, the problem of asymmetric weight distribution in consequence of an additional load by transportation service robot is mentioned. The mobile manipulator of the robot is a holonomic wheeled mobile robot with three active caster wheels. The kinematic and dynamic model of the active caster wheeled mobile robot with asymmetric weight distribution is proposed. The mobile robot will tip over if its acceleration is too high. By means of the simulation the relation between the control parameter and the model dynamic is studied.

Keywords: Laboratory Automation, Nonlinear Control
Abstract: This research aims to develop the low-cost radio frequency (RF) laboratory for microwave filters and antennas laboratory for the microwave engineering course in Technological University TU (DAWEI) Myanmar. The research includes two main parts: Firstly, the hardware and software system of the proposed magnitude RF network analyzer. Secondly, the hardware and software system of the antenna radiation pattern measurement equipment is proposed. The low-cost equipment such as the RF transmitter (Tx) using voltage control oscillate (VCO), receiver (Rx) using RF power detector and the turntable using low-cost motor are employed. The control unit, display unit and the graphic user interface (GUI) using MATLAB together with the Arduino controller are employed. The proposed low-cost RF measurement equipment was evaluated by 3 experts. Moreover, the student testing groups consisted of 3 groups with 3 students in each group. The results showed that the average score of students is 4.8 points (total of 5 points). As a result, it can be conducted that the proposed system can be used effectively and provide students with the knowledge and practical skills.

Keywords: Power Systems Control, SCADA and Network Systems, FlowMeasurement and Control
Abstract: This paper discusses the detection of attacks on decentralized and intelligent power networks, so-called smart grids. The approach used here, estimates the states of the power network, which is modelled by its power flow, via an Extended Kalman Filter. The use of this filter allows the consideration of nonlinearities, thus enabling the modelling of the losses of the systems. The final detection of attacks on a generator in a power network is realized by a correlation analysis. The correlation analysis provides the result of the inner correlation of sequential signals. Based on this, an assessment can be made as to whether or not the network is affected by an attack.

Keywords: Power Systems Control
Abstract: Since the doubly-fed induction generator (DFIG) is very sensitive to grid fluctuations, it is very important to improve the fault ride-through (FRT) capability of DFIG during the grid faults. The method of determining the optimal timing to release Crowbar from the analysis of flux and rotor current at the time of the asymmetrical voltage dip is proposed. The determination of timing for crowbar deactivation is validated by simulations. Besides, we will clarify the effect of the voltage drop on the period of the rotor current.

Keywords: Power Systems Control
Abstract: Wind power generation has been spotlighted as a solution to global warming and the depletion of fossil fuel. However, wind farm (WF) output is fluctuating for an intermittent nature. Energy storage systems (ESSs) are used to directly satisfy technical requirements by smoothing the WF output. How to smooth the WF output without increasing the rated power of the ESS is one of the important issues. In this paper, we propose a novel optimization method to smooth the WF output to satisfy the technical requirements and reduce the rated power (rated energy capacity) and charge/discharge loss of the ESS. The effectiveness of the proposed method is verified by a comparative analysis with a conventional approach through simulations.

Keywords: Power Systems Control
Abstract: This paper presents a new robust control method for a single-phase PWM inverter based on positive realness of load inpedance. A PWM inverter needs to control output voltage independent of uncertain loads. Load impedance can be regarded as positive real uncertainty because load resistance is positive. Thus, the proposed controller is designed by positive real method to ensure robust stability and robust performance for all loads.

Keywords: Power Systems Control
Abstract: This paper proposes a method of preventing over-current in virtual synchronous generator for a grid-connected inverter. In order to enhance stability in the power system with distributed generator,~the concept of virtual synchronous generators~(VSGs) has been proposed.~The VSG is designed based on the swing equation of synchronous generator.~However the VSG has weak point.~It is a low fault ride through~(FRT).~Therefore over-current occurs and the power semiconductor device inside the inverter may be damaged.~So,~this paper proposes the method which prevents over-current during grid voltage dips.

Keywords: Signal and/or Image Processing, Sensors and Transducers
Abstract: Optical flow estimation is one of the big research area in computer vision, and is now rapidly improving due to the deep learning technique. In this study, we investigate the effect of multiscale loss in our optical flow estimation neural network whose input is a single correlation image, not conventional RGB images. As a result, the effect of multiscale loss doesn't show big difference among flat and linearly low- and high- resolution heavy weights, but the end point error is slightly better in low resolution heavy weights.

Keywords: Process Automation, System Engineering, Human-Machine Systems
Abstract: One of the methods for analyzing the skin condition is to photograph the entire face and analyze the image. By discovering facial skin problems that are difficult to see with the naked eyes by image analysis, it is possible to quantitatively evaluate the customer's skin condition and propose the best optimal cosmetics and care methods. Under such circumstances, it is necessary to determine the facial feature points which determine the analysis areas. In this research, we aim to develop algorithms which automatically extract the facial feature points on the front and side faces.

Keywords: Signal and/or Image Processing, Identification and Estimation
Abstract: Silicon single crystal is the most commonly used material in the semiconductor industry. The dislocation-free silicon is important for semiconductor industry, and it is confirmed by habit lines observed on its exterior. In this study, we investigated an automated method to detect habit lines using DoG images with anisotropic standard deviation, and to determine the pull-up timing during necking process. As a result, habit line detection and pulling instructions were successful three out of three times.

Keywords: Signal and/or Image Processing, Analytical Measurement, Identification and Estimation
Abstract: In this paper, we discuss a method to measure 3D shape of puppet head for a digital archiving. In order to reconstruct an inner shape of head, we use CT images. Firstly, we divide four regions (wood, hair, paint and air) by thresholds which obtained by manual directed regions. After that, we divide these regions by a graph cut method. We apply this method to a puppet head and we confirm effectiveness of our proposed method.

Keywords: Sensors and Transducers, Human Interfaces, Remote Sensing
Abstract: In this study, we propose a stimulus position evaluation method for aerial ultrasound tactile display by measuring the reflected ultrasound from the skin surface. This approach aims imaging of reflective ultrasound intensity distribution on the target volume area by a phased array transmitter and a single receiver. In this paper, we confirm that the reflected ultrasonic wave distribution can be qualitatively measured with a resolution of the wavelength. This result shows to confirm that the ultrasound wave is applied to the fingertip in the situation where there is only a single fingertip in the workspace.

Keywords: Signal and/or Image Processing, Agricultural and Bio-Systems
Abstract: Detection of insect pests in the agricultural field, which is useful in achieving smart agriculture, has attracted considerable attention. In particular, automatically monitoring the number of crop insect pests has evolved into key means of managing and optimizing agricultural resources. However, despite the fact that conventional convolutional neural network (CNN)-based approaches have yielded sufficient results for general object detection, few methods have been developed to detect and recognize small objects such as insect pests. In addition, no large dataset exists for pest detection even though CNN-based methods require a large dataset to optimize many parameters. In this study, we propose two-stage detection and identification methods for small insect pests based on CNN. We also present a region proposal network for insect pest detection using YOLOv3 and propose a re-identification method using the Xception model. To train these models, we propose a data augmentation method using image processing.

Keywords: Nonlinear Control
Abstract: The design of control Lyapunov functions (CLFs) for guaranteeing finite-time stability is a difficult problem. In this paper, we propose a finite-time controller design method by using locally semiconcave CLF (LS-CLF) for differentially flat systems. The LS-CLF is composed of two CLFs: a global CLF obtained by the minimum projection method for an original system and a homogeneous CLF for a homogeneous system at the origin. Then, a discontinuous state feedback controller that finite-time stabilizes the origin of the original system is obtained. The effectiveness of the proposed method is confirmed by computer simulation for a trajectory tracking control problem of a two-wheeled mobile robot.

Keywords: System Engineering, Nonlinear Control, FlowMeasurement and Control
Abstract: Maintaining laminar flow and reducing drag is important from a turbulence engineering perspective. Much of the existing work on the stabilization control problem for stationary solutions modeled by the Navier-Stokes equation is based on state feedback. Our previous work has proposed a stabilization method for a linear distribution parameter system of parabolic type based on observer-based feedback, and the model of the system corresponds to the linear approximation problem of the Navier-Stokes flow stabilization problem. In this paper, we show that a nonlinear system applicable to the Navier-Stokes system can be stabilized by using the linear controller proposed in the previous work if the state is sufficiently close to the stationary solution with respect to a seminorm. This design method has also been successful in suppressing control and observation spillovers.

Keywords: Nonlinear Control, Human-Machine Systems, Mechanical Systems Control
Abstract: In modern society, human operation errors cause some accidents. To achieve moving obstacle avoidance, we need to know the velocity of the moving obstacle. However, the velocity of a moving obstacle is unknown in most cases. For the unknown velocity of the moving obstacle, Tezuka et al. apply the exact differentiator to estimate the velocity of a moving obstacle. Moreover, they proposed a human assist control law by using a time-varying CBF based on estimated values. In this paper, we construct an electric wheelchair's collision avoidance system by using a time-varying CBF based on estimated values. We confirm the effectiveness of the proposed system by actual machine experiments.

Keywords: Nonlinear Control, Mechanical Systems Control
Abstract: In this research, a controller that combines friction compensation and disturbance observer is proposed for control of the wheelchair. Conventionally, the friction between the tire and road is considered and the disturbance-observer-based approaches are used to mitigate it. In addition to this conventional approach, this paper also considers the friction in the motor drive mechanism and it is put in the inner loop of the control system. Such a friction compensation approach applies to both automatic and manual control systems. Simulations using parameters of a wheelchair are conducted. The results with and without friction in the mechanism are presented and it validates that the proposed combination of disturbance observer and friction compensation improves the performance over conventional approaches.

Keywords: Nonlinear Control, Robust Control
Abstract: The capability of non-medical strategies to reduce spread of infectious diseases is limited, but evidently quarantining and isolation methods have been effective to slow the transmission. This paper studies regulations on immigration which manipulates the number of individuals entering the area. The immigration including newborns cannot be zero to maintain human societies or ecological systems, but a decision can be made to slow or delay the entering individuals when it is effective to reduce the spread peaks. This paper proposes a feedback control strategy with theoretical guarantees of non-local convergence to the steady state which the society targets. It is proved that small errors in the manipulation of immigration cause only small deviations and they never result in tragedies in terms of input-to-state stability.

Keywords: Nonlinear Control, Robust Control, Multivariable Control
Abstract: This paper investigates max- and sum-separable Lyapunov functions for monotone networks exhibiting balancing kinematics. Input-to-state stability is sought for internal stability and disturbance attenuation generalizing operator-norms nonlinearly. For linear networks it is known that max- and sum-separable Lyapunov functions can be expressed analytically with right- and left-eigenvectors. This paper discusses extensions of the formulas to nonlinear networks. The main target is non-identical balancing kinematics for which analytical solutions have not been available. It is shown that max- and sum-separable construction is no more given by eigenvectors, but it still accepts closed-form expressions.

Keywords: Entertainment Systems, Human Interfaces, Computational Intelligence
Abstract: Facial expressions of human emotions play an essential role in gaining insights into human cognition. They are essential for designing human-computer interaction models. Although human emotional states are not limited to basic emotions such as happiness, sadness, anger, fear, disgust, and surprise, most of the current researches are focusing on those basic emotions. In this study, we proposed a new methodology to create facial expressions of compound emotions that evolve from combining those of basic emotions. In our experiments, we train our proposed model, namely Emotion Generative Adversarial Network (EmoGANs), in both unsupervised and supervised manners to improve the quality of generated images. To demonstrate the efficiency of the proposed method, we use the Extended Cohn-Kanade Dataset CK+) and Japanese Female Facial Expressions Dataset JAFFE) as inputs and visualize the generated images from our proposed EmoGANs. In the experiment, our proposed methodology can manipulate basic facial expressions to create facial expressions of compound emotions.

Keywords: Human Interfaces, Embodied-Brain Systems Science, Human-Machine Systems
Abstract: In this paper, an algorithm to estimate instantaneous cognitive load-strength is proposed. For the estimation of the cognitive load-strength, two factors derived from the conventional method with raw cerebral blood flow (rCBF) and from the frequency characteristics were considered. The latter focuses on changes of the statistical properties in the rCBF signals based on the author's previous findings that the frequency bandwidth and characteristics of rCBF depends on cognitive load. For an instantaneous estimation of the statistical properties, a self-organizing particle filter was constructed, and the validity of the estimation of the instantaneous standard deviation of the rCBF was confirmed with dummy data. In the experiment, the cognitive load-strength estimation algorithm was verified by utilizing three levels mental calculation tasks. Moreover, in order to adjust parameters in the algorithm, regression lines obtained from the difficulty levels and the estimated cognitive loads are analyzed, and an uncorrelated test was performed. As a result, the effectiveness of the proposed algorithm was confirmed that twelve out of thirteen participants were significantly correlated (r = .39, N = 48, p = .006) .

Keywords: Human Interfaces, Human-Machine Systems
Abstract: Transfemoral prosthetic users have motor functional impairments during running. A major impairment is unintended prosthetic knee flexion during weight bearing on the prosthetic side. Therefore, the present study aims to develop a passive mechanism for a transfemoral prosthetic knee to prevent flexion during the prosthetic stance phase. The proposed mechanism has a single axis for prosthetic knee flexion–extension. The shaft that functions as the prosthetic knee joint axis can move 3 mm along the long axis of the shank part to change functions between the stance and the swing phase. The prosthetic knee joint limits only flexion and allows extension possible during the stance phase. However, it rotates freely (flexion–extension) during the prosthetic swing phase. These functions of the proposed mechanism were verified using a rough prototype. The he participant with the attached prosthetic limb prototype performed level walking with habitual gait velocity on a 10 m straight walkway. The result shows that the kinematic data of the hip, knee, and ankle were similar to able-bodied gait. Moreover, the mechanism was able to mechanically limit only the flexion with compression force acting along the shank part. These proposed mechanisms and results of the preliminary trial suggest that transfemoral amputees can run safely.

Keywords: Human Interfaces, Human-Machine Systems, Integrative Biophysical Engineering and Informatics
Abstract: Sign language is the most commonly used non-verbal communication method for people with impaired hearing. However, there are hundreds of sign languages which have their own alphabets, vocabularies, and grammar. What is more, sign language is composed of complicated hand gestures which require people to receive special training to understand. These situations narrow the communication between people. Although some studies focus on applying cameras or gloves for sign language translation, many limitations of these sensors exist. Such as the requirement of a suitable environment for camera sensors and the high cost of the gloves. Therefore, in this paper, a hand gesture recognition system based on electromyography signal is proposed to ease the difficulty of communication between hearing impaired people and those who do not understand sign language. A message library is built for hearing impaired people to select short sentences using hand gestures. A convolutional neural network is used as the classifier and seven hand gestures are selected to decrease the individual difference among different people. The classification results indicate that this system can recognize seven gestures with good accuracy among ten experimenters. Therefore, this system has the potential to be widely applied as a communication method for hearing impaired people in their daily life.

Keywords: Human Interfaces, Human-Machine Systems, Medical and Welfare Systems
Abstract: In recent years, various types of human-machine interfaces (HMIs) have been investigated for patients who are unable to move their lower neck muscles freely due to nerve damage. In this paper, we propose a method to estimation of the tongue motions and silent words based on convolutional neural network (CNN) using the surface electromyogram (EMG) from the anterior neck region. We attempted to estimate 6 types of tongue movements and 20 types of silent words simultaneously from a 4-channel EMG. The results of the validation experiment with four subjects showed that the classification was possible with an average accuracy of 76.6%.

Keywords: Human-Machine Systems, Intelligent Systems
Abstract: Several studies have been reported on the influence of personified agents in persuasion. However, depending on the experimental situation, the number of agents, and the words in persuasion uttered by the agents, there is a difference in their influence on the persuadee's attitude. The effective style of agents in persuasion is not yet clear. In this study, we prepared six personified agents with different appearances to investigate their effects of regular communication (RC) and over-heard communication (OHC) in the persuasion. The experiments have been done changes in motivation to watch a film after persuasion. As a result, the RC was more efficient than the OHC. Although no particularly influential agents were found among the six agents, it was found that their appearance had some influence.

Keywords: Modeling, System Identification and Estimation, Multivariable Control
Abstract: An analysis and design of distributed power supply in DC architecture based on power balance control technique is presented in this paper. The main objective of the proposed system is to design converters and simple control technique for various voltage bus in the system. The simulation results by MATLAB Simulink indicate that the proposed control system offer the fast transient response and the implementation is simple.

Keywords: Process Control, Mechanical Systems Control, Components and Devices
Abstract: This paper presents a design and implementation of an inexpensive learning aid device for teaching visually-impaired students the concepts of angles and directions. The device combines the feature from regular protractor and compass, with added functionalities such as Braille labels and voice, allowing students to learn and review the concepts without frequent intervention from the teachers. This is achieved by using Arduino microcontroller with a variable resistor to detect the pointer’s movement and orientation. When the pointer is adjusted to a desired angle, the device speaks in user’s preferred language the angle in degrees using the sound module. Our proposed device can tell from 0 - 360 degrees on horizontal plane in 5-degree increment. Evaluation of the device was conducted with 2 groups of volunteers, both sighted and visually-impaired students, who tested all features of the device and provided scores in 4 aspects which are ease of use, design, operation on angle mode, and operation on direction mode. The device received overall high scores in all aspects from both groups, indicating the viability of using the device in a real school setting.

Keywords: Virtual Reality Systems, Human Interfaces, Human-Machine Systems
Abstract: The authors have newly built an augmented reality system that assigns fiduciary markers to 3D entities. Experiments confirm its fundamental characteristics with regard to 2D and 3D markers. They have suggested that identification accuracy depends on the point of view at the markers. We discuss here the causal relationship between the number of feature points of 3D markers and their recognition rate.

Keywords: Modeling, System Identification and Estimation, Analytical Measurement
Abstract: In this study, we validated the occupant behavior simulation considering the effect of the vehicle turning, a frontal collision model as a simple occupant model during vehicle steering. A collision avoidance operation may change the occupant behavior before the collision, which may cause injuries with a tendency different from conventional assumption. To confirm the validity, the result of the simulation was compared with the occupant behavior for the lateral acceleration in the actual experiment. Moreover, the behavior of driver and passenger seat during vehicle turning were confirmed, respectively. Finally, the behavior of the head of the occupant model in the present simulation was similar to that in the experiment.

Keywords: Computational Intelligence
Abstract: This paper proposes a novel surrogate model for genetic programming that estimates the fitness of each individual by using the tree structure similarity. In particular, the fitness of each individual is estimated with the nearest neighbor method by comparing each individual with the evaluated population. We conduct an experiment to investigate the effectiveness of the proposed method. In the experiment, we compare genetic programming with and without the proposed surrogate model on the symbolic regression problem. We assess the convergence speed and the discovery ratio of the optimum program. The experimental result reveals that the proposed method improves the convergence speed of genetic programming while maintaining the discovery rate of the optimum program.

Keywords: Computational Intelligence
Abstract: We introduce and discuss the methodology of the chaotic evolution (CE) algorithms, which is supported by a chaotic system. Because properties of the chaotic systems are the essential factors to influence the performance of the CE algorithms, we design and analyse several CE algorithms using different chaotic systems: logistic map, tent map, Gauss map and Henon map in a well-designed chaotic evolution framework. We propose a new CE algorithm using the combination of these chaotic systems to accelerate the convergence speed and improve convergence performance and analyse its effectiveness by comparing these new algorithms with our previous proposed CE algorithm with the logistic map. From the evaluation, our proposed CE helps the search to avoid the local optimum.

Keywords: Computational Intelligence
Abstract: In recent years, evolutionary algorithms have been used for many real-world problems, but it takes enormous computation time to obtain the optimal solution due to its high calculation cost. Multi-objective evolutionary algorithms using surrogate models have been studied to reduce the computation time for the optimization. ELMOEA/D is one of the surrogate-assisted multi-objective evolutionary algorithms. ELMOEA/D combines MOEA/D with an extreme learning machine (ELM). This paper analyzes the relation between the estimation accuracy of the surrogate model and the search performance of ELMOEA/D. We experiment on several well-known multi-objective benchmark problems and compare the different number of generations. The experimental results reveal that the estimation accuracy and the search performance decrease as the number of generations increase.

Keywords: Computational Intelligence, Advanced Computics, Medical and Welfare Systems
Abstract: Structural Equation Modeling (SEM) is a statistical analysis technique for fitting networks of constructs to data. The strength of extracting latent factors and estimating causal relations among them makes it accessible to analyze the structure of social problems, such as those around the economy, healthcare, and ecology. The present paper introduces SEM to the machine learning field for reducing the number of data dimensions. Too large dimensionality of the input data increases the computational load to build machine learning models. The proposed method utilizes SEM to sift variables and sort out relations between factors. The results show that the proposed method is able to identify the least number of data dimensions for machine learning-based prediction models to achieve the same or even higher accuracy as the prediction models trained with the full dimensions do.

Keywords: Computational Intelligence, Intelligent Systems
Abstract: This paper proposes the method to improve the CPU utilization by using precedence evaluation of tentative offspring for the previous method. The previous research proposed the Interleaving Generation Evolutionary Algorithm (IGEA) that generates individuals which parents are evaluated before the evaluation of all individuals completed. IGEA can reduce the execution time for the optimization with EA. We compare the proposed method with the original IGEA and investigate the effectiveness of the proposed method. The experimental results show that the proposed method has higher CPU utilization than the original IGEA.

Keywords: Computational Intelligence, Intelligent Systems
Abstract: In general, the purpose of a machine learning technique is to obtain an approximation of a function based on a set of training data. To obtain a good approximation of the function, the input variables irrelevant to the output should be removed in advance. The process used to remove the irrelevant input variables is known as feature selection. The existing feature selection methods generally select input variables that make it possible to approximate the function more accurately. As a consequence, these methods often fail to detect input variables that weakly affect the output, especially when the training data given are insufficient. In some practical applications of machine learning techniques, however, all of the input variables that actually affect the output must be detected. In this study, we propose a new feature selection method to overcome this drawback of the existing methods. Our method evaluates the relevance of a certain input variable by comparing it with a random variable. We show, through numerical experiments, that the proposed method is capable of detecting even input variables that weakly affect the output.

Keywords: Computational Intelligence, Intelligent Systems, Advanced Computics
Abstract: In recent, a specific machine learning method called deep learning has obtained great results in broad applications such as recognition and classification. Recent research has also been show that deep learning techniques can be combined with Reinforcement Learning(RL) to learn useful representation for the problems with high dimensional raw data input. This work show a method to reproduce the behavior of fish by learning virtual environment made by Unreal Engine4 that difficulty finding reward relationship(sparse reward) using RL methods such as Normalized Advantage Function made by Tensor Flow. Action spaces that forward, backward and rotation of the learning agent is continuous.

Keywords: Computational Intelligence, Intelligent Systems, Modeling, System Identification and Estimation
Abstract: Differential evolution (DE) is a method of evolutionary computation(EC) with a superior search capability for real-value optimization in nonlinear problems, nondifferential problems and multimodal problems, among others. While various DE methods have been proposed, DE with an individual-dependent mechanism (IDE) features a pair of control strategies and its superior search performance has been confirmed in various benchmark problems. This study aims to further improve the individual-dependent mutation (IDM) strategy in IDE. The IDM strategy divides individuals into superior and inferior populations over the search process and applies mutation in each population. Further, the ps criterion is used as a reference in dividing the population and applying mutation. This criterion changes by the number of generations, but it is not based on the individuals’ information. From our preliminary experiments, premature convergence occurs in some problems. This study focuses on the ps criteria and proposes a method of adaptively controlling the value based on the variance of individuals. Performance evaluation was performed using a 30, 50 and 100-dimensional benchmark problem generally used in EC. In the performance evaluation, we showed that the proposed method significantly outperformed conventional DE and IDE, and improved the search performance by preventing convergence to the local minima in problems prone to such an issue by controlling the variance of the individuals.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: In recent years, large earthquakes such as the Great East Japan Earthquake have occurred in Japan. Unfortunately, large-scale disasters are expected to occur in the near future. In such a case, logistics of emergency relief supplies is so important for us to survive in the situation. This paper proposes a reactive tabu search based optimal vehicle routing method in the distribution area when natural disaster occurs. The effectiveness of the proposed method is verified using the actual evacuation facility data of Chiba city, Japan, which are actually planned to utilize when a natural disaster occurs, by comparing with the conventional tabu search based method.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: This paper proposes a fault detection method for hydroelectric generators using isolation forest. In hydroelectric generators, since faults rarely occur, it is difficult to obtain fault data. Therefore, it is required to construct models automatically using only normal data and perform fault detection. Hydroelectric generator data have non-linear correlation. Isolation forest can develop models automatically with only normal data and have been verified to be effective for the data with the characteristic. Namely, it is expected to be effective for fault detection of hydroelectric generators. Effectiveness of the proposed method is verified by comparing with a Multivariate statistical process control (MSPC) based fault detection method. Results indicate that the proposed isolation forest based fault detection method is more accurate than the MSPC based method.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: This paper proposes OCSVM with a new feature selection method for fault detection of GTGs in thermal power plants. There are three requirements for practical systems of fault detection of GTGs in thermal power plants. Firstly, it is necessary to detect fault using only normal data. Secondly, it is necessary to consider non-linear correlation of GTG data. Thirdly, limited features should be selected with only normal data considering development cost of the system. Using actual GTG data in thermal plant, effectiveness of the proposed method from the view point of calculation time and fault detection accuracy is verified. Especially for fault detection accuracy, OCSVM with the proposed feature selection method is compared with OCSVM with all features and OCSVM with the basic time-consuming feature selection method.

Keywords: Computational Intelligence, Social Systems, Intelligent Systems
Abstract: The paper combines the theory of aspiration level and the theory of planned behavior to explain the convergence of cooperation among agents in social dilemmas. The focus is on a category of social dilemmas that individuals have a higher payoff if they choose not to cooperate, with a consequence that some players will receive a lower payoff. However, all players will receive better payoff if they all choose to cooperate. This study suggests combining the two theories noted above that the level of aspiration adjusts the attitude variable from the theory of planned behavior. This paper also introduces the commitment level as a representation of agents' actual behavioral control (the variable from the theory of planned behavior). The paper illustrates the social dilemma in the agricultural context, particularly in the problem of entering or not the farmers' organization. Three necessary conditions have been shown from the result of agent-based simulations for the cooperative behaviors to emerge: when farmers do not change much their aspiration level on the benefit received from the organization, initial members of the organization have a higher connection in the social network, and the high commitment level of members to cooperate. The paper theoretically demonstrates the merit of utilizing behavioral theories to improve the explanation of the analytical game theory in more practical application issues.

Keywords: Safety, Environment and Eco-Systems, Information Management Systems, Computational Intelligence
Abstract: Imbalanced problems often occur in the classification problem. A special case is within-class imbalance, which worsen the imbalance distribution problem and increase the learning concept complexity. Most methods for solving imbalanced data classification focus on finding a globe boundary to solve between-class imbalance problem. My thesis proposes a effective quasi-linear network with local offsets adjustment for imbalanced classification problems. First, we proposed a gated piecewise linear network, an autoencoder-based partitioning method is modified for imbalanced datasets to divide input space into multiple linearly separable partitions along the potential separation boundary. Construct a quasi-linear SVM based on the gated signal that obtained by autoencoder partitioning information. Then training a neural network that let F-score as loss function to generate the local offsets on each local cluster. Finally a quasi-linear SVM classifier with local offsets is constructed for the imbalanced datasets. Our proposed method avoids calculating Euclidean distance, so it can be applied to high dimensional datasets. Simulation results on different real world datasets that our method is effective for imbalanced data classification especially in high-dimensional data.

Keywords: Simulation of Large Systems, Identification and Estimation
Abstract: The Kumamoto Castle in Japan was disastrously damaged by the Kumamoto earthquakes in 2016. In this study, we focus on collapsed stone collation of the Kumamoto Castle. Stone wall in Kumamoto Castle is a important cultural facilities, it is required to find an accurate original position for each stone. In the previous research, contour information is used to collate the location of stones. This previous study showed that contour information was useful for stone wall collation. However, there are some problems, it was impossible to obtain complete contour information and stones with few features are difficult to identify correctly stone. Then, we were focused on the position of the fallen stones to improve the ability to find the correct original location in previous study. By using non-linear SVM that trained by fall position information, it is expected that we can narrow down collapse area of the collapsed stones compared to the previous study. The effectiveness is illustrated by numerical simulations. Moreover, we propose an stone wall collation algorithm by multimedia using fall position information in addition to contour information.

Keywords: Signal and/or Image Processing
Abstract: A method of sound source localization using a robot arm and a particle filter is proposed in this paper. The purpose of this method is to estimate the location of leakage points of gases/liquids in the piping of various facilities. A particle filter is employed to track the target sound source during the scan using the robot arms. The robustness of the proposed method against the ego noise of the robot arm and sudden noises is experimentally verified.

Keywords: Signal and/or Image Processing, Components and Devices
Abstract: A three-input single-output multi-function filter based on voltage differencing gain amplifiers (VDGAs) as active devices is introduced. The proposed circuit consists of 3 VDGAs and 2 capacitors, without any resistor, suitable to implement into integrated circuit architecture. The proposed circuit can provide 5 standard filter functions including lowpass (LP), bandpass (BP), highpass (HP), bandstop (BS) and allpass filter (AP) without changing structure. The outstanding feature of the mentioned filter is that the natural angular frequency (ωo) and quality factor (Q) can be controlled independently through transconductances of the VDGAs. The total power consumption of proposed circuit is 0.82mW.

Keywords: Signal and/or Image Processing, Components and Devices
Abstract: This paper describes a synthesis of versatile biquad filter with four input and single output nodes. The principle to synthesize the proposed filter is based on parallel RLC configuration. The proposed filter structure is very compact employing VDDDA, two resistors and two capacitors. In this design, the VDDDA is constructed from off-the-self available ICs. Five voltage mode second order filtering functions, band-pass, low-pass, high-pass, notch and all-pass filter are obtained by applying the input voltage signal to appropriate input nodes of the proposed filter. The relevant filtering parameters, the natural frequency (w0) and quality factor (Q) are tuned electronically. Additionally, the Q is tuned without affecting the w0 by changing value of resistor. The proposed filter is designed and supported by Pspice simulation. Several simulation results are obtained and confirmed the design and theorical analysis

Keywords: Signal and/or Image Processing, Remote Sensing, Identification and Estimation
Abstract: This paper presents a comparative studies of drone detection capabilities of different cameras, Monocular (webcam) and fish-eye (omnidirectional) camera. The drone detection is based on an in-house hybrid motion-based model together with deep-learning. The criteria considered in the study are sensitivity, detection rate and detection time. The optimal camera would have the higher detection rate and take the shorter time to track the drone. The drone(s) were flew in different scenario with a varying distance. Initial results show the webcam (Logitech C370 HD) has the almost the same detection rate but at lower number of pixels compared to fisheye camera (FLIR Chameleon3 PointGrey). However, there is no different in drone tracking ability after detection. Therefore, both cameras are suitable for drone detection and tracking as long as the drone are in the cameras’ fields of view. Index Terms—Monocular camera, fisheye camera, drone detection and tracking.

Keywords: Signal and/or Image Processing, Remote Sensing, Ubiquitous Healthcare
Abstract: Radar sensors have a great potential for human remote sensing. However, due to the complexity of these sensors, it is hard to predict radar behavior in complex environments. In this research, we present a simulation framework for radar human motion estimation. The simulation was validated using the data obtained in an experiment were the participants walked while using a cane with a millimeter wave radar. The proposed simulation could be used for the simulation of radar sensor systems for human movements estimation such as, balance assessment, fall detection or pedestrian detection without the need the obtain data in real environments.

Keywords: Signal and/or Image Processing, Robotic and Automation Systems, Intelligent Systems
Abstract: In this study, we propose "Salient SLAM," a real-time 3D environment maps generation with saliency information based on human visual characteristics. We used a Fine-Grained technique, which mimics human visual characteristics, to generate a saliency map on the spatial domain. Then, we integrated this saliency map to a real-time feature based VSLAM technique. As a result, we archived a three-dimensional feature map with saliency information, and enabled the ability to use this saliency information in robots' autonomous and exploration. Salient SLAM map point is leveled into four levels, and the higher the level, the higher the score of saliencies. In the experiments, we investigated and analyzed the characteristics of each saliency level in global feature maps according to the type of objects and visual patterns.

Keywords: Signal and/or Image Processing, Sensors and Transducers
Abstract: Long time of data encoding with software encoding mechanisms might become a significant problem when transferring digital data from cameras of mobile robots. At the same time, processor manufacturers claim that an encoding process is significantly accelerated by using a hardware encoding. This work is dedicated to a hardware and a software video encoding comparison of two state-of-the-art codecs, which were selected due to their high popularity in computer vision and robotics fields - a hardware encoding with h264_vaapi and a software encoding with FFmpeg API libx265 codec. We encoded six video sequences of different resolutions and sizes with the two codecs and evaluated obtained video quality using the Structural Similarity Index, the Peak Signal-to-Noise ratio, and the Video Multimethod Assessment Fusion metrics. Software and hardware encoding processes were also compared by CPU and memory usage, and time that was taken by the encoding process. Our results demonstrated that the hardware encoding with h264_vaapi was 5 times more memory efficient and 6 times more time-efficient relatively to the software encoding with libx265, with an insignificant difference of an output video quality.

Keywords: Signal and/or Image Processing, Sensors and Transducers
Abstract: In this paper, the authors propose a method of window frame detection using deep learning for localization by an image sensor for small window cleaning robot. Although the authors have decreased detection errors of straight lines by using bilateral filter and adjusting parameters in image processing, due to the brightness, lines including false targets were detected in three places mainly. The first one was the part of border of window frame and window glass. The second one was the part of window frame edges. The third one was the part of objects of glass transparency and reflections. They were detected randomly. Therefore, the authors introduced a deep learning method to judge which the straight line is window frame. They used 1000 images for the learning. They showed that the method could judge window frames with 66 % accuracy rate. Even though it is still not an acceptable level, the authors show that this method is more effective than the image processing only method.

Keywords: Signal and/or Image Processing, Temperature Measurement, Remote Sensing
Abstract: In this paper, a remote anomaly monitoring and notification system is proposed for various types of production lines including, e.g., machining systems, conveyer lines and so on. The monitoring application is developed on a Matlab platform. Two approaches are applied to anomaly detection. One is a frequency analysis based on Fourier transform, in which a main sampling period and a target frequency range are considered to efficiently catch undesirable anomalies. The other is a temperature analysis based on thermography technique using an infrared camera. If some anomaly is detected by each of those systems, then an alarm message with detailed information and captured images are sent to the user's smartphone. The usability and validity of the proposed system are evaluated through long-time experiments of additive manufacturing using a 3D printer.

Keywords: Identification and Estimation
Abstract: Because of the smaller space margin of the lateral direction than the longitudinal direction, the lateral maneuvers can cause severe accidents. Especially, the lane change maneuver is very important because the lane change is hard to predict. To predict the lane change trajectory, we proposed the data-based prediction model. The model has a cascade structure with one classification model to predict the occurrence of the lane change, one regression model to predict time to lane change and the other regression model to predict lateral deviation of the vehicle. With this structure, the cases which are irrelevant to the lane change can be filtered out by the classification model, so the complexity of subsequent regression models can be reduced. The performance of the proposed model is validated by simulation with the train set. The results show better performance compare with the kinematics-based model. In the future, optimization of the hyperparameters and comparison with other researches should be performed.

Keywords: Identification and Estimation, Biological and Physiological Engineering, Sensors and Transducers
Abstract: Sight is important for controlling the musculoskeletal system and maintaining posture because it is a sense that collects external environmental information away from the body. In daily life and sports, humans obtain most of their external environmental information from sight. Therefore, analyzing the gaze during movement is expected to clarify the relation between gaze and falls among elderly people and to improve performance on the sports field. This study measured the gaze and upper limb posture of a catcher while receiving a basketball with both hands. Results clarified the visual–motor coordination during catching. Singular value decomposition was applied to the observation matrix constructed using the upper limb joint angles and the pixel coordinates of gaze point obtained in the experiment. We evaluated the cooperative relation between a gaze and upper limb posture quantitatively using the space basis vector of the first mode, which has a high contribution ratio. Results demonstrated that the movement of upper limb was coordinated with the gaze while receiving a ball. Visual-motor coordination in various sports can be clarified using the proposed method.

Keywords: Identification and Estimation, Intelligent Systems, Remote Sensing
Abstract: In this paper, we propose two different scanning methods, drone scanning method, and mobile scanning method, using a stereo camera for estimation of the tree's diameter at breast height (DBH). The mobile scanning method is widely used in mapping forest environments and for forest inventory due to its cost-effectiveness and accessibility. Drone scanning has gained wide attention in recent years because of the development of unmanned aerial technology. This study evaluates two existing fitting algorithms to determine the most suitable method for DBH extraction, namely, the least-squares ellipse fitting algorithm and the random Hough transform (RHT) circle fitting algorithm. A stereo camera is used to generate 3D point cloud maps of the pine forest area. The trees in the study area are segmented using clustering algorithms before performing DBH extraction. A three-dimensional point cloud map of pine trees in the study area is generated, and DBH of each individual tree is estimated. From the experimental results, the drone scanning method was found to be more appropriate than the mobile scanning method for the application of forest mapping. From our experimental results, the drone scanning method with the RHT circle fitting algorithm achieved an average precision accuracy of 92.67 %.

Keywords: Identification and Estimation, Intelligent Systems, Sensors and Transducers
Abstract: Localization technology is mostly achieved through Global Navigation Satellite Systems(GNSS). However, in mountainous areas where leaves and trees block signals from satellites, getting an appropriate location with GNSS positioning sometimes becomes difficult. In this paper, a localization algorithm based on altitude profiles is proposed. It is a kind of landmark based localization algorithm in which altitude profiles are treated as landmarks. The effectiveness of the algorithm is presented with several experimental results obtained with real data. In recent years, it has become possible to easily use altitude data. Specifically, altitude is estimated from the pressure information obtained from a pressure sensor. The proposed algorithm attempts to identify the current position by matching altitude reference data prepared in advance with altitude data currently acquired.

Keywords: Identification and Estimation, Intelligent Systems, Signal and/or Image Processing
Abstract: This paper presents the prediction system of wind speed and direction one hour ahead using encoding-forecasting network with convolutional long short-term memory (ConvLSTM). The input of prediction system is wind speed and direction which are represented as image data on the 2D coordinate and provided by Automated Meteorological Data Acquisition System (AMeDAS) in Japan. Performances of the proposed prediction system are evaluated based on root mean square error (RMSE) between observed and predicted value. The goal of the proposed prediction system is to improve prediction accuracy and it is confirmed by comparing the result of the prediction system of four seasons.

Keywords: Identification and Estimation, Micro and Nano Devices
Abstract: This paper deals with the angle estimator of a MEMS electrostatic rotary actuator with 3-phase and top-drive type. The electrostatic actuator is driven by the electrostatic force generated by the electric charge. In order to drive this actuator, an appropriate 3-phase input voltage is required depending on the angle of the rotor, but it is difficult to attach an angle sensor for MEMS equipment. Therefore, the angle estimator using capacitance was studied. The estimator is designed based on the Kalman filter, and the system and measurement model is obtained by COMSOL software. As a simulation result, it confirmed that the angle estimation shows good performance and it has small error.

Keywords: Identification and Estimation, Nonlinear Control, Signal and/or Image Processing
Abstract: Optical flow is a vector that represents the motion of objects in images. The dynamical system approach to optical flow estimation was proposed as one of the optical flow estimation methods. Most of the optical flow estimation methods are based on the intensity conservation law. Therefore, these estimation methods might not be able to estimate correctly if the intensity conservation law does not hold by various factors. This paper proposes a new estimation model for images in which the image intensity is not maintained after moving objects in images. The proposed method can estimate not only the optical flow and its spatial gradients but also the intensity variation and its spatial gradients that do not follow the intensity conservation law. In addition, this paper focuses on the possibility of the optical flow estimation from the perspective of the observability. The effectiveness of the proposed model is demonstrated through numerical examples.

Keywords: Identification and Estimation, Remote Sensing
Abstract: Present vehicles need state information about surround vehicles. For example, distance and relative velocity used for the emergency brake system or adaptive cruise control. This paper suggests a relative state estimation method about surround vehicle of ego vehicle using V2V signals, Radar signal, and IMU. Already we know that these signals have signal defects. So, it uses the unscented Kalman filter and nonlinear dynamics for removing signal defects.

Keywords: Identification and Estimation, Robotic and Automation Systems
Abstract: Particle Filters (PFs) are used for the discrete approximation of dynamic and non-Gaussian probability distributions using numerous particles. Maximum a posteriori (MAP) estimation, which is a point estimation method to extract a unique state value from the probability distributions formed by a PF, functions appropriately against multimodal distributions. However, MAP entails an enormous calculation cost. Therefore, we propose a method to perform MAP estimation with a low calculation cost by compressing the information configured by PF, using adaptive vector quantization. For MAP estimation with 900 particles, the proposed method reduced the computational cost by approximately 96% compared to the conventional method and maintained the same estimation accuracy during simulation.

Keywords: Identification and Estimation, Transportation Systems, Intelligent Systems
Abstract: The terrain is distinguishable ground features for vehicle positioning and estimable using only an inertial sensor. This paper proposes a vehicle positioning method using terrain features transformed into the position-frequency domain, which is less dependent on the vehicle dynamics, more robust against noise, and richer than time domain-based features. As using the position-frequency domain-based terrain features, a terrain map is able to be reconstructed statistically with respect to representative features extracted from all features of the map for the storage efficiency. The position information of the representative feature most similar to the given current feature becomes the current position candidates. For extracting the current position in the candidates, the prior information with respect to the past position obtained from the Kalman filter is reflected in the statistical information of the position candidates and the position candidate having the highest probability and its variance become the measurement and the noise covariance of the Kalman filter. The proposed algorithm shows the positioning possibility with shorter-query sized terrain features, therefore, it can be an alternative of GPS when all external signals are interrupted.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics
Abstract: The spindle is 12-18 Hz oscillation component of EEG observed mainly in sleep, that synchronized with hippocampal sharp wave ripples related to memory consolidation. However,there is less understanding to the contribution of spindles for memory function. To clarify the interaction between spindle and memory consolidation, EEG was recorded in sleep just after performing maze task in human subjects. Subjects who had a large number of the spindle during sleep showed high performance of the maze task. These results suggest that the number of spindle during sleep was related to the maze performance before and after sleep.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics, Ubiquitous Healthcare
Abstract: It has been reported that the complexity of brain activity reflects the cognitive function and pathology of the brain.

However, so far, no study has applied the complexity of electroencephalogram (EEG) signals in a brain machine interface (BMI). In this study, we hypothesized that the complexity of brain signals involves the applicable information for a BMI, rather than that for a conventional power spectrum analysis. To validate this hypothesis, we focused on estimating the brain's thinking state based on the complexity of an EEG. Concretely, we compared the multiscale entropy (MSE) profiles between EEG data acquired while subjects listened to relaxing music and those acquired while subjects listened to exciting music to estimate the brain state based on the complexity of EEG data. Consequently, the complexity of the brain reflects the inherent brain information process that cannot be detected through power analysis. The complexity of the EEG demonstrates the possibility for obtaining more information in comparison to the methods conventionally used in a BMI for power spectrum analysis.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics, Ubiquitous Healthcare
Abstract: The quantitative-based human behavior finding is one of the most challenging problem to access the human preference in neuromarketing. The humans’ subconscious response to a decision making relevant to the process of brain electrical signal using electroencephalogram (EEG). The EEG signals are related to the positions of the electrode in the international 10 – 20 system. In this research, the EEG of five participants are recorded while they are choosing the product which is preferred and unpreferred from the nearly identical composition and flavor of the two brands of snack. The signal was normalized and extracted using the power spectral density (PSD) and asymmetry index to observe behavioral differences between the two cerebral hemispheres. According to the experiments, the left frontal with asymmetry index can potentially illustrate the preferred product. To further emphasize, by using the root mean square error calculated from the support vector machine (SVM), the results show that the error in the group using asymmetry feature is less than using the traditional normative PSD feature. According to these results, a systematic human preference prediction can be potentially done using the EEG.

Keywords: Biological and Physiological Engineering, Nonlinear Control, System Engineering
Abstract: In the present paper, we consider Butlerov type chemical reaction networks (BTCRNs) involving the formation of sugars from formaldehyde. By generalizing BTCRNs based on characteristic network structures of them, we theoretically obtain the productivity of sugars in BTCRNs in the batch reactor. We analyze the ordinary differential equation (ODE) describing the dynamics of concentrations of species in the generalized BTCRNs with a general type of kinetics, and show that any positive solution to the ODE converges to a non-negative equilibrium point, at which the concentrations of formaldehyde and some sugars are zero. This implies that the BTCRNs in the batch reactor cannot obtain various sugars as end-products. From a viewpoint of chemical industry, they have low productivity of sugars at an equilibrium state.

Keywords: Electrophysiology and Kinesiology, Biological and Physiological Engineering, Medical and Welfare Systems
Abstract: Muscle evaluation is essential in rehabilitation and muscle training for muscle recovery. In general, rehabilitation tasks are based on physical therapy and muscle training is based on sports theory; the common issue for both is that they must consider the muscle fatigue caused by the muscle load. However, although there are reports on quantification of muscle fatigue within individuals, there are no reports on quantification methods for evaluating muscle fatigue among individuals. This study is focused on electromyography (EMG) that can be measured from the skin surface according to muscle activity, and a method to quantify muscle strength and fatigue from their amplitude and frequency is proposed. In addition, the authors propose a method for estimating muscle fatigue from the activity rates of muscle-fiber types through EMG frequency analysis. However, the estimation accuracy shows complications due to individual differences in the activity rates of muscle-fiber types. Therefore, to evaluate the muscle condition, in this study, the authors hypothesize that changes in electrical properties caused by physical characteristics affect the activity rates of muscle-fiber types, and investigate the relationship between four types of physical characteristics and muscle-action potentials in six subjects. The results indicate that body-fat percentage is related to the activity rates of muscle-fiber types.

Keywords: Integrative Biophysical Engineering and Informatics, Electrophysiology and Kinesiology, Biological and Physiological Engineering
Abstract: Sound imagery has been studied in past decades with techniques such as fMRI, PET, MEG, or tDCS. However, sound imagery phenomenon in EEG signal has not been widely studied. Use of deep learning in EEG applications is increasing in popularity due to the ability to learn EEG data without rich data pre-processing. In contrast to typical classification models, with the input-perturbation network prediction technique used here, we visualized the learned features from the trained model in terms of the correlation between the change in input frequency and the change in network prediction to better understand the features the model used for decision making. In this study, we recorded EEG signals from three subjects who were asked to perform a sound imagery task. In the first phase, subjects were asked to listen to and remember a generated sound; in the second phase, subjects were asked to imagine a sound of the same pitch. One-fourth of trials had no sound generation; EEG signals were labeled with the no imagery class. EEG signals from the remaining trials were labeled with the sound imagery class for model training. The best accuracy of 71.41% was obtained by the shallow model for subject 1, and an average accuracy of 61.00% was achieved between subjects. The model’s decision to classify EEG data into the sound imagery class was based on decreases in the delta, theta, and low beta bands in the frontal lobe and corresponding increases in the in the right temporal lobe of the brain.

Keywords: Ubiquitous Healthcare, Rescue Systems, Integrative Biophysical Engineering and Informatics
Abstract: With the aggravating trend of aging of population, the population aged over 60 years is growing increasingly　faster than any other generation groups. Under these circumstances, the number of elderly people in a solitary life is increasing. Therefore, there is increasing expectation of monitoring services for elderly which can detect their daily motions or emergency situations such as having a fall. Keeping the basic daily motions is also essential for the elderly to prevent some accidents hereafter. Therefore, in this paper, from the perspective of general versatility and privacy, a motion detection method using a low-resolution infrared array sensor is proposed. The system can overcome the limitation of exist methods such as an invasion of privacy and have a wider application. A long short-term memory classifier is applied to this system in order to improve the accuracy. Experiments show that the system successfully achieves the aim of higher-precision human motion detection.

Keywords: Biological and Physiological Engineering, Virtual Reality Systems, Mechatronics Systems
Abstract: Insects have the ability to find the odor source by integrating multiple sensory information. However, this multimodal information processing has not yet been elucidated because it is difficult to measure the relationship between input environmental information and output behavior based on free walking experiments. In this paper, therefore, we constructed a virtual reality (VR) system that can measure the relationship between the multiple sensory input timing and behavioral output for analyzing the insect odor source behavior.

Keywords: Social Systems
Abstract: Weighted network analysis deals with both the topology of connection and its weights. The primary interest of the analysis is the relationship between topology and weights. For instance, scaling-law between degree and strength have been widely observed. However, the relationship between middle range topology and weights has not been explicitly investigated because conventional analysis was biased toward global or local range topology. In this study, we clarify the relationship between middle range topology and weights in a social network that is a face-to-face communication network in a corporate organization. As results, we found that the specific trend of the relationship appears in the range of two degrees of separation. This indicates the middle ranges of a weighted network have a non-trivial trend that cannot be derived from local or global ranges, and the trend also depends on the number of steps in the middle ranges.

Keywords: Social Systems
Abstract: In this study, we build the bid-rigging norms model in the application of Axelrod’s norms game model and run simulations. We demonstrate that the auction system itself, which is mainly used in public procurement, has a structure that invites bid-rigging. Besides, we clarify that meta norms are not necessary for establishing bid-rigging norms, though Axelrod pointed out the necessity of meta-norms for establishing social norms. We also reveal that the enforcement, which means punishment for deviation from bidding rings, has a critical role in establishing and maintaining bid-rigging norms.

Keywords: Social Systems
Abstract: The purpose of the study is to analyze the behavior of foreign tourists. We used the conversational data collected by mobile translators distributed in the city of Kobe while many analyses have been conducted on social networking services and blog entries in past studies. We extracted conversation data between foreign tourists and Japanese people from the mobile translators and analyzed the data based on such as word frequency, co-occurrence relationships, and word vector representation. As results, the following aspects were revealed: In shopping streets, foreign tourists traveled in small groups. In tourist facilities, there was not much communication with Japanese people. On public transportation, foreign tourists tended to ask staff for help.

Keywords: Social Systems, Intelligent Systems
Abstract: The purpose of this study is to design a learning environment and clarify its learning effects. And we propose a teaching simulation on the complex doubly structural network with adaptive learning and verify the effect of the adaptive learning environment on real classroom and on online classroom. Then, the following considerations were obtained. The adaptive learning with coaching teacher model tends to have better learning efficiency than the adaptive learning model. It has been shown that adding the support of teacher to adaptive learning contributes to improving learning efficiency. When performing collaborative learning in a learning environment with adaptive learning and teacher support, learning efficiency tends to be improved when the seats are arranged in a concentrated manner than when the seats are dispersed. This was the opposite result from the case where the teachers performed collaborative learning in the simultaneous learning environment. The Adaptive Learning with Matching Teacher on Collaborative Learning model has better learning efficiency than the Adaptive Learning with coaching Teacher on Collaborative Learning model. The learning design incorporating the adaptive learning system and the online lesson are compatible. The results of this study show that the combination of teacher support and collaborative learning improves the efficiency of classroom learning more than the adaptive learning system alone.

Keywords: Social Systems, Intelligent Systems, Identification and Estimation
Abstract: In daily power supply, it is important to accurately forecast the electricity demand for each time of the next day. Electric demand at a certain time is affected not only by weather conditions at that time but also by weather conditions and electric power demand in the past several hours. The dynamic characteristics of electricity demand are nonlinear and vary with time of day, day of week, and season. We focused on the frequency spectrum of the electricity demand curve as a feature of the electricity demand with dynamic characteristics which change with time. We propose a method of pattern classification of dynamic characteristics by clustering frequency spectrum, and a method of forecasting frequency spectrum by nonlinear regression based on kernel method. The former is an approach that emphasizes explanatory, and the latter is an approach that emphasizes forecast accuracy. We confirmed the usefulness by the experiment using the open data.

Keywords: Social Systems, Modeling, System Identification and Estimation
Abstract: Recent studies perform complexity analysis, such as multiscale entropy (MSE) on financial time series because the evaluating complexity can elucidate new aspects of financial time series and capture their characteristics. Machine learning is also widely used to predict financial time series. However, it requires a large amount of data and is not very responsive to changes in markets. Therefore, features that allow for efficient extraction of time series data are necessary. In this context, we hypothesize that the complexity index is a method to extract financial crises. To prove this hypothesis, we analyze and compare the average Dow Jones stock prices using MSE analysis and multifractal analysis. The results show that the multifractal analysis can extract a financial crisis more accurately than the MSE analysis can. In the future, we aim to devise a prediction model by combining the complexity index and a machine learning method, such as the Long Short-Term Memory model, and compare and evaluate it with the prediction methods in the generalized autoregressive conditional heteroscedasticity and autoregressive moving average models.

Keywords: Social Systems, Modeling, System Identification and Estimation
Abstract: The understanding of the pedestrian behavior is indispensable for evaluating the safety of the urban layout. In this paper, a pedestrian behavior in the stairs is introduced from the observation data with model predictive control. The features of the crowd behavior model are examined in terms of the pedestrian flow coefficient.

Keywords: Simulation of Large Systems, Social Systems, Discrete Event Systems
Abstract: An important subject in the evolutionary game theory is to understand how cooperation arises. The subject has been extensively studied especially in Prisoners' dilemma game(PD) but the emergence of cooperation is also an important subject in public goods game(PGG). In this article, we consider coevolutionary PGG on complex networks where both the topology of the networks and strategies under the influence of game dynamics varies. The cooperators contribute a fixed amount per member of the group in PGG of this article. We study what effects on the evolution of player's strategies and the average payoff do the interaction between the game dynamics and the network topology bring. Moreover by comparing the models that do not depend on game dynamics to the models without topology changing, we intend to uncover the effect of the topology chnage and the game dynamics on the promotion of the cooperation. Furthermore we study how do the topology of initial networks have an influence on the average payoff and degree distribution.

Keywords: Network System Integration, Social Systems, Intelligent Systems
Abstract: There are various types of networks, and networks are constructed by nodes that represent places and by edges that represent connections. Among them, it is well known that most real-world networks consist of scale-free network including aviation networks, power networks, world wide web(www) and citations for academic papers. Since the scale-free network has a complicated structure, network exploration is important to understand the structure. Random walk is a method for network exploration since the entire network is invisible. However, random walkers may visit the same node multiple times, which results in the inefficient search. To solve that problem, the self-avoiding model was proposed in which the visited nodes were temporarily not visited. Although the self-avoiding model has better exploration efficiency than the random walk model, the walker may not be able to return to a hub node with a high degree. Therefore, we propose the self-selection model. Since the walker in the self-selection model depends on the degree of the current node, it easily returns to the hub node. In this paper, we evaluate the performance of the self-selection model and compare it with the self-avoiding model on the scale-free network using the Barabási-Albert (BA) network.

Keywords: Mechanical Systems Control, Virtual Reality Systems, Mechatronics Systems
Abstract: Hemiplegia of the fingers, which is a typical sequela, reduces the quality of life of those affected. We aim to realize effective rehabilitation by implementing a soft actuator using air pressure and two mechanisms using electro-adhesive gel in a force display. The present study investigated the response performance of force control with internal pressure to guide the design of a hybrid force-display glove. In addition, force control through internal pressure was achieved using a balloon actuator. However, the response performance and accuracy required for haptic presentation were not achieved. It was therefore thought that the response time and the precision of the force control corresponding to internal pressure can be improved by changing the structure and material of the balloon actuator. The new model output the same force with an internal pressure less than half that of the old model. Although the response was not better than that of the old model, it could be improved by changing the structure in the new model.

Keywords: Adaptive and Optimal Control
Abstract: Unicycle robots or bicycle robots are more suitable for avoiding obstacles than four-wheeled robots because of smallness of contact areas with the ground. Also, these robots can carry objects more efficiently than humanoid robots. On the other hand, they are not suitable for stepping over a trench or a step. In this paper, we consider a wheeled acrobot as an example of such robots. We improve the wheeled acrobot's mobility to realize a jump with bending and streching the robot's upper body and lifting the wheel. The jump trajectory over a trench is generated by a direct collocation method, and the efficiency will be demmonstrated by a numerical simulation.

Keywords: Social Systems, Mechatronics Systems, Entertainment Systems
Abstract: In this paper, we propose a jump robot platform for interacting with a child through a jump. The proposed robot platform consists of a jump robot, a sensor for detecting rapid vertical motion of a child, and a ROS-based controller for robot jumps which are triggered by the child motion. One of the most distinguishing features of the proposed robot platform is to utilize jumps as non-verbal behavioral cues in interacting with humans which are different from conventional social signals such as pointing, nodding or facial expressions. The jumping robot used on this platform can respond to a child’s rapid vertical movement within a quarter of a second due to the high performance of a repetitive jump of approximately 2 Hz that jumps 117 times in 1 minute. An experiment was conducted in which three children from the age of 5 to 7 interacted with a jumping robot using the proposed robot platform. It became possible to jump the robot with the jumping motion of the child. This paper shows that the proposed platform has the potential to perform dynamic interaction with children using jumping.

Keywords: Modeling, System Identification and Estimation
Abstract: In this paper, we proposed an algorithm for identifying the elastic property of an elastic rod along its trunk. The proposed algorithm estimates the stiffness matrix for bending and torsion along the backbone curve of an elastic rod based on the measured information on its shape and the forces and torques applied at its base by making full use of the discretized Kirchhoff elastic rod model and using Newton’s method. We propose a system for identifying the elastic property as well. Experimental result using an elastic strip with a known elastic property shows that the proposed method can estimate the elastic property of the rod with 0.14% accuracy.

Keywords: Mechanical Systems Control, Robotic and Automation Systems, Nonlinear Control
Abstract: This paper proposes a control method to achieve the palm circle task for a ball-beam system in a planar space, where the beam is actuated by a 3-DOF manipulator. The palm circle means the situation where the spatial position of the ball is stationary at a point while the beam is moving cyclically. The manipulator dynamics is firstly linearized with respect to the beam's orientation and the contact force on the object. This technique makes the linearized system simplified while the direct linearization for the control objective variables of the palm circle task results in too complex. Then, the tracking controller for the control objective variables can be easily designed with the simplified linearized system based on LQR servo control. The proposed controller is verified in numerical simulation examples. Some conclusions and further research are finally mentioned.