Keywords: Cybersecurity, Industrial Automation and Control Systems
Abstract: Eric provides consulting and advisory services in the management of information technology solutions in operations and engineering. He is a past vice president of standards and practices at ISA and is currently a member of the ISA executive board and co-chair of the ISA99 committee on industrial control systems security.

Keywords: Identification and Estimation, Flow Measurement and Control, Process Control
Abstract: This paper addresses the modeling of a compressed air system, which supplies compressed air from air compressors through a pipe network to end-use points. To save energy in this system, it is effective to reduce the discharge　pressure of the compressor while ensuring that the pressure of end-use air is larger than the minimum level necessary to　drive the　equipment. To this end, this paper identifies an ARX model that represents the dynamic characteristics of the end-use pressure based on experimental data of pressure and flow rate, and develops a method to estimate the amount of air consumption (flow rate) at the end of the system based on this model.

Keywords: Mechanical Systems Control, Robotic and Automation Systems, Adaptive and Optimal Control
Abstract: Hydraulic excavator is one of the most widely-used construction machines, whose typical work process composed of digging the soil and load it onto the dump trucks, where the physical property of the soil severely varies from site to site. To realize automatic digging system, it will be necessary to adjust its control parameters for each site. In this study, we propose a method to search and refine the control parameters using Bayesian optimization in a pre-scheduled experiment based on multiple trials. The proposed method is verified it on a medium-sized hydraulic excavator on soft and flat soil.

Keywords: Advanced Computics, System Engineering, Computational Intelligence
Abstract: Return maximization and risk minimization are important issues in stock trading owing to the complexity of stock price fluctuation. In recent years, deep reinforcement learning has become a promising approach to solving this problem. In stock trading, many investors have different investment strategies for short, medium, and long periods, and may implement different actions for different periods even if they have the same information and assets at hand. However, few studies have considered trading periods in stock trading using deep reinforcement learning. Therefore, in this study, we propose a method to apply deep reinforcement learning to stock trading considering the trading period. We evaluate the effectiveness of the proposed method by conducting evaluation experiments.

Keywords: Modeling, System Identification and Estimation, Nonlinear Control
Abstract: This research extends our previously proposed deep neural network-based system identification method. The approach directly composes neural network-based state estimators and output predictors from input/output data, allowing for efficient computation when applied to Model Predictive Control (MPC). However, reducing the uncertainty of the identified model remains challenging. To address this issue, we propose an enhanced output predictor structure that incorporates an autoencoder for the input data. By utilizing the autoencoder's decoder part, trained with the system identification data, to generate control inputs during MPC, the model is encouraged to operate in well-represented regions. This approach is expected to improve computational efficiency by reducing the dimensionality of optimization variables and prevent the model from operating in regions with insufficient data during MPC.

Keywords: Modeling, System Identification and Estimation, Transportation Systems
Abstract: In contemporary society, various plans based on past experiences have been adopted. These plans contain substantial knowledge, however reconstructing this knowledge can be challenging, a phenomenon generally referred to as `knowledge transfer failure.' This research focuses on the optimal transport problem as an attempt to restore this knowledge. Optimal transport problems on a graph have been applied in the path planning of multi-agent and logistics systems. In addition, optimal transport problem is equivalent to Schrödinger bridge in certain situations and can be solved efficiently. We investigate an inverse Schrödinger bridge and propose a method for deriving the cost that represents the implicit knowledge used in existing path planning.

Keywords: Adaptive and Optimal Control
Abstract: A higher EGR rate is used to reduce NOx and improve the engine thermal efficiency. However, there is a trade-off between the improvement of the thermal efficiency and the combustion cyclic variations. Furthermore, the combustion cyclic variations cause the torque fluctuations and it have a bad influence on the engine performance. In general, CA50 is used as an indicator of the combustion cyclic variations and controlling CA50 is effective in reducing the torque fluctuations. In this paper, we aim to suppress the torque fluctuation, even under high EGR rate condition by achieving output tracking for the reference signal with CA50 as output and ignition timing as input. In addition, we verify the effectiveness of the adaptive output feedback method with robust output predictive FF Input through numerical simulations in the Spark ignition (SI) engine model with low-pressure EGR considering the combustion cyclic variations.

Keywords: Adaptive and Optimal Control
Abstract: Adaptive output feedback control based on almost strictly positive real (ASPR) boasts a simple structure and formidable robustness against disturbances and system uncertainties. However, meeting the conditions of ASPR is a challenge for most systems. With this in mind, It is proposed to introduce a suitable parallel feedforward compensator (PFC) to build an ASPR augmented system as the controlled system. However, the effect of the introduced PFC output hinders the actual output from achieving perfect following the reference signal. Among the methods to solve this problem, it is known that it is effective to incorporate feedforward (FF) inputs to establish two degrees of freedom (2-DOF). In this paper, the design of appropriate FF inputs using neural networks (NN) is considered. Furthermore, to address the possibility of input disturbances, we provide a method to realize disturbance compensation in order to reduce disturbance interference. Ultimately, the efficacy of the proposed design strategies is validated through numerical simulations.

Keywords: Adaptive and Optimal Control, Modeling, System Identification and Estimation, Nonlinear Control
Abstract: This paper proposes a new design method for state estimators in a temperature control system using a Peltier device. Due to the physical location, some temperatures between the Peltier device and the heat sink aren't easy to measure with sensors. The proposed estimators solve this problem and estimate the above temperatures accurately. Furthermore, using the estimators, an event-triggered PI control with two different event-triggering mechanisms on the input and output sides is developed. Numerical simulation results show its effectiveness.

Keywords: Adaptive and Optimal Control, Multivariable Control, Modeling, System Identification and Estimation
Abstract: In this study, a leader-following problem for multi-agent systems with non-uniform dynamics is solved by using simple adaptive control. For non-almost strictly positive real systems (ASPR), ASPR-ness is achieved by adding a parallel feedforward compensator. Furthermore, since an adaptive control system is designed using a reference model with the same dynamics as a leader, followers follow the leader.

Keywords: Adaptive and Optimal Control, Robust Control, Nonlinear Control
Abstract: This paper presents a new event-triggered control system that detects network disconnection in two-way links. A controller connects to a plant via a network through two event-triggering mechanisms introduced on both sides of the plant and the controller. To detect network disconnection in the uplink and downlink, the proposed system uses two integrators with auxiliary signals installed in front of the plant and the controller.

Keywords: Adaptive and Optimal Control, Robust Control, Nonlinear Control
Abstract: Event-triggered control is one of the most effective networked controls for reducing communication costs. However, communication restriction often makes it difficult to find network disconnection. This paper presents a new event-triggered tracking system with an anomaly detection function that utilizes an integrator and an auxiliary signal to detect disconnections within a prespecified time.

Keywords: Guidance and Flight Control, Robust Control, Simulation of Large Systems
Abstract: This paper reports the results of an investigation into an orbit determination method that improves the stability of orbit determination in cases where uncertain acceleration is constantly occurring, and the object ascends while orbiting a gravitational object in an elongated elliptical orbit

Keywords: Nonlinear Control, Guidance and Flight Control, Intelligent Control
Abstract: This paper investigates a distributed differential game strategy for the gravitational wave observatories (GWO) formation control subjected to nonlinear dynamics. In this proposed method, three spacecraft attempt to reduce its own performance cost, while the GWO formation tries to optimize policy based on inner communication topology. A formation control strategy based on Nash strategy has been proposed. In the differential game, a worst/case Nash strategy against disturbances defined as a player is proposed. Finally, GWO formation initialization and configuration reconfiguration are realized.

Keywords: Guidance and Flight Control, Intelligent Control, Nonlinear Control
Abstract: Trajectory control laws based on neural ordinary differential equations (ODEs) were proposed by the authors in a previous study. This paper extends the trajectory control laws by integrating a reservoir computing architecture. The addition can significantly reduce the number of design parameters to be optimized and thus the computational cost of training while maintaining the structure of the control laws. This enhancement allows general-purpose nonlinear optimization algorithms to be used, extending the application of neural ODEs to operation design optimization beyond the design of control laws. This study verifies the effectiveness of the proposed framework by constructing a control law to achieve the target conditions for a rendezvous operation in a low Earth orbit. Multi-objective design taking advantage of reservoir computing is demonstrated by assessing the Pareto optimal front with respect to orbital transfer time and control accuracy.

Keywords: Guidance and Flight Control, Robust Control, Multivariable Control
Abstract: This paper deals with the preview gust alleviation control system of an aircraft, which mitigates dangerous vertical acceleration due to wind gust using the forward wind velocity measured by the LIDAR (LIght Detection And Ranging). In order to evaluate the performance of the whole system including the LIDAR, airflow estimation algorithm and preview control law, a simulation tool was developed to analyze aircraft motion in a wind field based on vortex model. This paper shows the simulation results using a vortex wind field model.

Keywords: Signal and/or Image Processing, Transportation Systems, Remote Sensing
Abstract: Traffic monitoring was strengthened using high altitude camera images for counteracting traffic accidents. Vehicle extraction is indispensable for estimating the speed and distance between vehicles. Studies have been conducted on vehicle extraction during the day and at night. However,vehicle extraction in images captured at dusk has been rarely studied.Here, we propose a method of vehicle extraction using elevated camera images captured at dusk. Many environmental changes occur during dusk hours. To accommodate such changes, we generated a background image with reduced headlight effects and integrated moving objects extraction and vehicle lights extraction. The proposed method was successfully applied to video images of the Satta Pass in Shizuoka. As a future issue, a method of accurately extracting a single vehicle needs further research.

Keywords: Transportation Systems, Adaptive and Optimal Control
Abstract: The dynamic characteristics of a truck change significantly as the weight of the cargo changes. In the truck platooning driving problem, it is important to deal with this change in dynamic characteristics. In this study, we consider a control method that can withstand the effect of vehicle weight change by constructing constraint conditions using a control barrier function. Computer simulations were conducted to confirm how much vehicle weight change can be tolerated during platooning.

Keywords: Adaptive and Optimal Control, Nonlinear Control, Mechanical Systems Control
Abstract: Powertrain energy management is crucial for improving the fuel efficiency of hybrid electric vehicles (HEVs). Series hybrid electric vehicles (SHEVs) distinguish themselves from ordinary HEVs by allowing the engine operating point to be freely adjusted to minimize total fuel consumption without considering the vehicle power demand. In conventional research, we developed an energy management method for SHEVs based on optimal control using a simplified model of the generator for formulation simplicity. However, this simplified model constrained the operating point of the generator to a smaller set compared to the physical limit of the generator. In this study, we first enhanced the generator model to align with the physical limits of the generator. Subsequently, we revised the optimization problem formulation to enable the simultaneous and direct optimization of the engine ignition timing and the operating points of powertrain components, including that of the generator, without the involvement of binary optimization variables. The effectiveness of the proposed method is validated and compared with the conventional approach.

Keywords: Identification and Estimation, Embodied-Brain Systems Science, Social Systems
Abstract: As a proposition of this study, we attempted to investigate how work environment and information improve employees' well-being. These efforts were made to visualize and activate categorical questionnaires such as surveys. We were using the Bayesian approach of data-processing the options and developed an approach for visualizing the level of activation from the data based on this using Structural Equation Modeling：SEM. Additionally, we have verified that to develop an approach for visualizing the level of activation from the data. Visualizing means between variables based on SEM; a multi-group analysis was also considered. In this paper, it was indicated how important a key for preprocessing is for SEM prediction. Where SEM conducted by extracting the questionnaires data according to a Bayesian approach, the major results obtained would report on.

Keywords: Intelligent Systems
Abstract: The AI Formula is a technical challenge in which three-wheeled mobility vehicles drive autonomously through a racecourse. This challenge features different race modalities in which participants will compete for speed and intelligence in a real-world environment. We aim to provide an opportunity for rising engineers to acquire the skills and technology necessary for next-generation mobility research. AI Formula will begin in 2025 at JSAE and SICE.

Keywords: Transportation Systems, Mechatronics Systems, Simulation of Large Systems
Abstract: In order to promote industry-academia collaboration, JSAE-SICE Joint Committee of the Automotive Control and Model Research set benchmark problems that apply the latest machine learning and control theory to automobile control. As one of them, an optimal control problem for the motion and energy of a four-wheel in-wheel motor vehicle was settled. The object to be controlled is an autonomous vehicle equipped with a four-wheel in-wheel motor and a steer-by-wire system. Tasks are set to start, accelerate, decelerate and stop on rough roads, and double lane change on rough roads. The vehicle model is a full vehicle model with a three-dimensional multi-body mechanism written in Modelica, and the body movement can be controlled by the suspension reaction force of braking/driving force. In two tasks, challengers are required to solve an optimal control problem that minimizes the power consumption of a four-wheel in-wheel motor while suppressing body vibration below a predetermined value. Challengers will be provided with a Modelica model, a Simulink model using its FMU, and a free license for the Modelica tool (Modelon Impact).

Keywords: Transportation Systems, Modeling, System Identification and Estimation, Intelligent Control
Abstract: SICE-JSAE Automotive Control and Modeling Committee and Post Advanced Innovation powered by Mathematics Platform have conducted benchmark problem activities in order to educate students and young engineers by providing research subjects and by supporting their research activities. The new benchmark problem of “Optimization Problem of Mobility Service in Smart Satellite City” was proposed by them. It provides the simulator that can simulates the behavior of mobility service using automated-driving electrical vehicles in a satellite city. In other words, it calculates time series data of mobility demands of residents in the city. The challengers for the benchmark problem optimize multi-objective cost functions by altering the setting parameters of the mobility services, by newly designing mobility-dispatch algorithms and by applying machine learning to algorithms of the mobility services.

Keywords: Adaptive and Optimal Control, Nonlinear Control, Modeling, System Identification and Estimation
Abstract: Model-free predictive control distinguishes itself from traditional methods by utilizing solely measured input/output data instead of mathematical models. The effectiveness of order selection in model-free predictive control, achieved through the integration of Singular Value Decomposition (SVD) along with the Bayesian Information Criterion (BIC), has already been well-documented. This paper explores alternative probabilistic model selection methods, such as the Akaike Information Criterion (AIC) and the Generalized Information Criterion (GIC), in comparison with the previously proposed BIC technique from earlier research. It also analyzes the limitations in terms of the number of data and noise levels for each probabilistic model selection method. Our numerical simulations show that GIC exhibits a notable proximity to BIC compared to AIC. However, BIC demonstrates superior performance compared to other methods, particularly as the number of data and noise levels change.

Keywords: Power Systems Control, Multivariable Control, Adaptive and Optimal Control
Abstract: Energy management system (EMS) is an important tool for energy efficiency, reliability, and resilience of the power system. In the Mae Hong Son (MHS) microgrid, we implement a nonlinear model predictive control (NMPC) of EMS with a consideration of the optimal reactive power dispatch (ORPD) technique along with optimal power flow (OPF). The EMS was previously designed with the multiple objectives including total operating cost (TOC) and total CO2 emission (TCOE). Power loss has not been considered which results in subsided efficiency in an electrical system and the shorter lifetime of equipment due to the excessive heat. Besides TOC and TCOE, we introduce total power loss (TPL) as a new objective function. This paper aims to reduce power loss in the MHS microgrid by applying the ORPD technique which incorporates tap changing in the transformers and reactive power compensators. The microgrid utilizes NMPC to optimally dispatch energy with a multi-objective function at each timestep by solving second-order cone programming (SOCP). We formulate the design problem as a SOCP and utilizes convex optimization as a solver. The simulation covers a 7-day period across three seasons: rainy, winter, and summer. The numerical results illustrate that the proposed technique is deemed to reduce power loss effectively in designated transmission lines and improve the overall energy efficiency.

Keywords: Power Systems Control, Adaptive and Optimal Control, Components and Devices
Abstract: The proliferation of space technology and the decreasing cost of launching services have spurred the development of components for CubeSats. Among these components, the power storage subsystem, which is responsible for distributing power stored in batteries to the system, is of utmost importance. However, ensuring battery functionality in the harsh space environment, characterized by low temperatures, poses a significant challenge. Traditional external heating systems that heat batteries are often complex and unsuitable for resource-limited environments like CubeSats. Therefore, this paper proposes AC internal heating based on charge pump topology to generate AC current and validate battery aging analysis via Electrochemical Impedance Spectroscopy (EIS). This method offers efficiency in terms of space utilization and rapid heating rates. The results of our study demonstrate that the proposed heating system achieves a heating rate of 7.5 degrees Celsius in Panasonic NCR18650 cells with an RMS current of 4.37C while consuming only 1.85% of the cell's energy. Moreover, after 60 heating cycles, no signs of battery degradation were observed, making it suitable for non-serviceable battery systems.

Keywords: Modeling, System Identification and Estimation
Abstract: In this study, we consider a design method of the control system to attenuate the periodic disturbances. Yamada et al. proposed a design method of the control system to attenuate the periodic disturbance with the disturbance observer for the periodic disturbance. However a difficulty remains with their method. Their method cannot design all stabilizing controllers. In this study, we clarify the parameterization of all stabilizing controllers.

Keywords: Intelligent Control, Simulation of Large Systems, Factory Automation
Abstract: The learning rate is one of the important parameters for optimizing the performances  of reinforcement learning. In this study, a cyclical learning rate in an off-policy algorithm  is adopted and its performances are experimentally evaluated against an actual refrigeration  system. A model-based development approach is employed to create a detailed simulation  model by utilizing MATLAB Simulink. The temperature followability with the reduction of electrical  energy usage is precisely evaluated. The simulation results show the efficiency of cyclical  learning rate.  

Keywords: Intelligent Control, Robotic and Automation Systems, Intelligent Systems
Abstract: This paper deals with Rapid Control Prototyping (RPC) of controller mounted on robotic lawn mower by using Model Based Development method. The controller optimizes the rotation speeds of two motors, one is for mowing the lawn grasses and another is for travelling the lawn mower by estimating whether the ground is with long lawn grasses or short lawn grasses, or without lawn grasses. A random forest model executes the estimation with high accuracy, and it is implemented on a Raspberry Pi by using the combination of Python and C++ language programs. The details of RPC are shown, and the time reduction for lawn mowing is estimated by simulation. The time reduction amounts to 27% compared with that of conventional controller against a real lawn field with the area of 31,170 m2.

Keywords: Transportation Systems, Standard of Measurement, Analytical Measurement
Abstract: Recently, Advanced Air Mobility (AAM) becomes to be researched and developed both flying technological and social acceptance point of view. Considering of inclusive social service, accidental fall of AAM must not be scary but delight for the passenger. In order to change it from scary to delight, it is necessary to improve both aerodynamical safety technology and social responsibility system. In this paper, we discuss about social responsibility with autonomous flight system and fall terminal velocity measurement using wind tunnel equipment.

Keywords: Remote Sensing, Signal and/or Image Processing, Social Systems
Abstract: Landslide disasters have become more frequent in recent years owing to the increase in heavy rainfall. At disaster sites, information is collected by remote sensing. Our objective is to detect sediment regions in aerial images taken from the sky. Generally, detecting sediment regions in the images requires time and manpower, which, however, are not available at disaster sites. It is necessary to automate the detection process using a computer. There are not only sediments but also buildings in the sediment regions. Removing them from the images is expected to improve the detection accuracy. In this paper, we propose a method for detecting sediment regions without buildings in the images. In this method, an orthoimage and a Digital Surface Model(DSM) image are generated from 3D point cloud data, and the sediment region detection process is performed on each image. Buildings are identified from the difference in elevation between the roof and the ground, and they are excluded from the sediment region by mask processing. As a result, sediment regions are detected from the combined result of the two processes.

Keywords: Remote Sensing, Signal and/or Image Processing, Flow Measurement and Control
Abstract: Technologies for observing early post disaster conditions are being developed. However, the utilization of data obtained by such technologies is still in the discussion stage. In this study, we pay particular attention to aerial survey data. We simulate a mudslide disaster in Atami City using aerial survey data to reproduce sediment transport during a disaster. In addition, in this study, we analyze the requirements and issues for future disaster simulations on the basis of the results obtained.

Keywords: Human-Machine Systems
Abstract: This paper presents a comparative analysis on driver behavior while navigating a curve. The topic of driver behavior while navigating a curve is particularly in manual driving still relevant due to the fact of its critical importance in road safety and accident prevention. Also, current research shows that understanding manual driving behavior could enhance the driver support system as well as automated vehicle performance. The interaction between age and environmental effects while navigating the curve is not well documented. Therefore, this research aimed to examine the relationship between age (young and middle-aged) and environmental factors, focusing on both normal and foggy conditions. The study involved 36 participants and utilized a driving simulator for the investigation. From the preliminary results, we found that driving conditions, speed and steering angle significantly impact driving behavior while the age of the drivers does not significantly affect the behavior. This is likely due to the similar level of driving experience among the participants. Additionally, these findings suggest that environmental conditions has a significant impact in influencing driving behavior compared to age differences. It is crucial to understand these dynamics as this knowledge is essential for developing Advanced Driver Assistance Systems (ADAS) in order to enhance overall system safety and performance.

Keywords: Medical and Welfare Systems, Intelligent Systems, Human Interfaces
Abstract: In the realm of cognitive rehabilitation, Adaptive Cognitive Training (ACT) tailors interventions to individual needs. Critical to ACT's success is the accurate, real-time assessment of cognitive states, a challenge that our innovative system addresses through interactive question-and-answer sessions. Utilizing natural language processing, our system dynamically evaluates and adapts training based on cognitive assessments. This paper presents our system's development and its integration with ACT, highlighting a pilot study that showcases its effectiveness in enhancing cognitive training personalization and reducing healthcare burdens. Our findings suggest significant potential for improving cognitive rehabilitation outcomes, setting a promising direction for future research in intelligent cognitive assessment.

Keywords: Innovative Systems Approach for Realizing Smarter World, Social Systems, Analytical Measurement
Abstract: A simple motion analysis aims to evaluate and assess activities of daily living (ADLs) is newly presented. Euler angle motion analysis based on simply scoring joint range of motion is introduced to address the difficulties of ADL motions due to joint limits such as arthropathy. It turns out the new scoring provides sufficient ratings to indicate similarity of movements in a graphical way. Recommendations for standing up motion based on the proposed analysis is exemplified.

Keywords: Autonomous Decentralized Systems, Intelligent Systems, Entertainment Systems
Abstract: In interpersonal sports competitions, players learn various strategies to defeat other players, and their inter-personal motor skills grow through mutual interactions. However, learning mechanisms underlying interpersonal motor skills remain unclear. To address this problem, we focused on a simple sumo-wrestling-like task in which two agents with circular bodies push each other in a circular field, and the agent that remains in the field for the longest duration wins. Despite the simplicity of the task, various interpersonal motor skills exist such as attacking collisions and dodging opponent attacks. Herein, we propose a simple reinforcement learning algorithm based on the Monte Carlo Method with a small number of state and action variables. Through simulations, we demonstrate that the win rates of the agents fluctuate when both agents continue learning, whereas one of the agents constantly wins at a high rate when the other agent stops learning, suggesting that the agents acquire strategies to defeat other players through learning.

Keywords: Autonomous Decentralized Systems, Intelligent Systems, Social Systems
Abstract: This paper presents a novel algorithm for social cooperation in a scramble-crossing simulation. The proposed algorithm is inspired by human social behavior and employs imitation strategies to improve overall performance and adaptivity. Exploiting the inherent robustness of swarm systems in adapting to environmental changes, the method aims to maintain divergence among agent strategies. This is achieved by evaluating and comparing local performance indices. The effectiveness of the imitation algorithms in various swarm simulations, spanning different density scenarios, is validated by evaluating flow rate as a measure of global performance. Finally, the simulation results indicate that the proposed social cooperation algorithm suggests high flow rate even under environmental change.

Keywords: Autonomous Decentralized Systems, Robotic and Automation Systems, Mechatronics Systems
Abstract: This study introduces the Weak Constraint Swarm (WCS) robot system, a novel swarm robotic system that utilizes weak constraints such as soft materials, passive joints, or contact forces to physically connect the individuals in the swarm. Bridging the gap between discrete swarm robots and modular swarm robots with hard connections, which can also be perceived as an "individual", WCS combines the flexibility of the former with the enhanced mobility of the latter, without the typical loss of softness and adaptability. Leveraging insights from research on implicit control and soft centipede-type robots, WCS represents an innovative multi-robot system that holds promise for advancing the capabilities of swarm robotics. This paper provides an overview of the WCS concept, its theoretical foundation and inspiration, its potential and limitations, and future research plans.

Keywords: Robotic and Automation Systems, Agricultural and Bio-Systems, Modeling, System Identification and Estimation
Abstract: Shepherding, in which a small number of sheepdogs drive a large number of sheep to a destination, has been widely studied by herd biologists and swarm robot engineers. In previous studies on shepherding, there are few studies on the actual behavior of sheepdogs and sheep, and the comparison between the proposed navigation controller and actual sheepdog behavior data is insufficient. In contrast, this study aims to model shepherding based on actual behavioral data using a nonlinear system identification method. In this paper, the shepherd's navigation controller is obtained using GMDH, which can self-select a highly accurate estimation function from a wide range of possible input variables. By reducing the number of terms in the estimation function, a simpler controller was obtained while maintaining the estimation accuracy. The results show that the reduced estimation function reflects the shepherd's dynamics without the estimator's intention.

Keywords: Robotic and Automation Systems
Abstract: In this study, we address the guidance control problem of the sheepdog system, which involves guiding a flock using the characteristics of both the sheepdog and the sheep. Sheepdog systems require strategies for guiding sheep agents to a goal area using a small number of sheepdog agents. We have proposed a novel guidance algorithm that designs an efficient route for guiding a flock of sheep by incorporating the traveling salesman problem and evolutionary computation. In this study, experiments with mobile robots were conducted to confirm the feasibility of guiding and controlling sheep flocks using the developed guidance routes.

Keywords: Robotic and Automation Systems, Factory Automation, Manufacturing Systems
Abstract: This paper addresses efficient path planning for articulated robots. Traditional methods suffer from high computational costs and tuning parameter issues, worsened by frequent kinematic parameter changes. We propose a novel path generation approach independent of these parameters, i.e., a path reuse method. Typically, robot paths are planned in task space (T-space) and robot configuration space (C-space), with collision checks dominating computational costs. Prior methods such as random sampling and gradient search are inefficient for dynamic environments. To address this, a novel encoding method for paths in C-space is introduced, capturing key path characteristics. This encoding facilitates path reconstruction, and the fast-decoding technique enables adaptation to different start/goal positions, significantly reducing collision checks and computational costs.

Keywords: Robotic and Automation Systems, Laboratory Automation, Mechatronics Systems
Abstract: Autonomous vehicles stand as a burgeoning technology, evolving across various nations and industries. This research endeavors to develop an automated charging infrastructure for electric vehicles, complementing the smart ecosystem for autonomous vehicle systems. The experimental framework involved a KUKA LBR iiwa 7 robot equipped with a Type-II connector and an Intel Realsense D435i depth camera for detecting the electric vehicle (EV) inlet. The EV inlet detection process initiated with 2D image detection, followed by end effector alignment to the target inlet's center point. Subsequently, depth image information facilitated the forward movement essential for the charging process. This technique's efficacy heavily relies on precise horizontal and vertical alignment with the EV socket, as minor misalignments could potentially compromise the robot's functionality. To ensure safety and prevent any instances of collision, torque sensors integrated into the KUKA robot played a pivotal role. Their implementation served as a safeguard against harm to the robot, emphasizing the criticality of safety measures in this automated charging system.

Keywords: Robotic and Automation Systems, Process Control, Process Automation
Abstract: In this study, we propose a unique framework that combines the Elite Genetic Algorithm (EGA) with Iterative Learning Control (ILC) to enhance the trajectory-tracking accuracy of robotic arms. This framework harnesses the collective intelligence of the Genetic Algorithm (GA) and the precision learning capabilities of ILC to optimize control parameters, thereby reducing tracking errors dynamically. By this integration, our approach enables automated adjustment of control parameters, significantly improving the performance and robustness of robotic arms. Through mathematical modeling and empirical testing on the WAM Arm, we demonstrate that our methodology surpasses classical ILC and GA-ILC strategies in speed and accuracy. Additionally, we propose a new complexity metric called the "Computational Burden Index (CBI)" for a more effective evaluation of different ILC algorithms.

Keywords: Robotic and Automation Systems, Safety, Environment and Eco-Systems, Transportation Systems
Abstract: This paper focuses on developing a new path-planning navigation algorithm for mobile robots, limiting their motion to two basic modes: in-situ turning and linear movement. The algorithm is specifically designed for a differential wheel drive mobile robot to implement this path-planning waypoint navigation algorithm. A differential wheel drive robot enables in-situ turning with a self-direction change with zero radius. Using these two simple motion modes provides several benefits for path planning navigation. Obstacle avoidance maneuvers are only required during linear movement, as the position of obstacles does not need to be considered in-situ turning mode. If a mobile robot detects an obstacle while driving in straight mode, it only needs to perform a stopping action, without the need for complex path planning involving curved movements. After stopping, the mobile robot acts in-situ turning mode to change the forward direction of the mobile robot, continuing its linear movement until it is in a position sufficient to avoid obstacles, then resumes to its original waypoint course. The validity of the proposed new waypoint navigation algorithm for differential wheel mobile robots was confirmed through a simulation environment.

Keywords: Robotic and Automation Systems, Signal and/or Image Processing, Opto-Electronic Measurement
Abstract: The Intelligent Ground Vehicle Competition (IGVC) is an annual robotics competition held in the U.S. for teams of undergraduate and graduate students. The competition focuses on autonomous mobile robots, and one critical aspect is the autonomous lane-following function. Traditionally, lane line recognition relies on image sensors like cameras, but these have limitations due to changes in outdoor lighting conditions. To address this, a novel approach is proposed: using 3D LiDAR to detect lane lines and obstacles based on reflection intensity. By combining point clouds and intensity, this method overcomes lighting challenges. The resulting lane-following algorithm is safer and more robust, operating solely with 3D LiDAR data. It estimates lane line direction, detects obstacles, and determines a local target destination, ensuring reliable control regardless of lighting conditions. The approach was evaluated in a simulation environment.

Keywords: Robotic and Automation Systems, System Engineering, Guidance and Flight Control
Abstract: This paper develops an aquatic environment testbed with multiple unmanned surface vehicles (USVs) based on Robot Operating System 2 (ROS 2). Despite the high need for multi-USVs operation, a limited number of testbeds have been reported in the literature. Since commercial USVs do not provide an open software development kit, we build three original USVs, named Karugamot, with two controllable thrusters attached to both sides of the body. The onboard microcomputer is connected to the main PC through WiFi. We also deploy an optical motion capture system to track the USVs. The present testbed coordinates the onboard microcomputer, sensing module, and control module on ROS 2. We finally show demonstrations of the Line-of-sight guidance control and circular formation control on the developed testbed.

Keywords: Mechatronics Systems
Abstract: One of the issue for ultra-compact mobility vehicle is the deterioration of ride comfort due to vibration transmitted from the road surface to the passengers. Vibration transmitted from the road surface includes vertical vibration caused by road surface irregularities and pitch vibration generated when vibration is input to one of the front or rear wheels, and these have a significant affect on ride comfort degradation. Therefore, in this study, we are investigating the improvement of ride comfort using a two-degree-of-freedom active seat suspension. This system consists of a voice coil motor and other parts is mounted under a seat to control vibration. In this study, the vibration characteristics of the active seat suspension in the vertical and pitch directions were investigated.

Keywords: Mechatronics Systems
Abstract: In recent years, there are many studies focusing on magnetic levitation, one of which is a study of a magnetic levitation system for thin steel plates. Thin steel plates are mainly transported using rollers in the manufacturing process. However, contact between the rollers and the thin steel plate may scratch the surface of the steel plate, resulting in deterioration of the surface quality. Therefore, a non-contact gripping and conveying method using magnetic levitating technology has been proposed. In conventional magnetic levitation systems, deflection of a thin steel plate during levitating has been a problem. A magnetic levitation system for thin steel plates we proposed can suppress the deflection of thin steel plates better than conventional systems. However, there are still concerns about stable levitating due to factors such as heat generation, and further investigation of the characteristics of the proposed the magnetic levitation system is needed. Therefore, the control characteristics of thin steel plates during levitating were investigated.

Keywords: Mechatronics Systems
Abstract: In the conveying process in steel plate production lines mainly uses rollers for contact conveying, which causes deterioration of surface quality and peeling of the plating. To solve this problem, a technology that grips and transports thin steel plates in a non-contact method by magnetic levitation using electromagnetic force has been proposed. Therefore, conventional studies on magnetic levitation of thin steel plates have focused on suppressing deflection of thin steel plates. Therefore, we proposed a magnetic levitation method in which electromagnets are placed horizontally at the edge of a rectangular thin steel plate and pulled horizontally to suppress deflection. Since the method of electromagnet placement is thought to affect the attractive force generated to the thin steel plate, it is necessary to investigate the effect on the attractive force and control. Therefore, in this study, the influence of the electromagnet arrangement method on the levitation characteristics of steel plates was investigated.

Keywords: Mechatronics Systems
Abstract: Among the studies on magnetic levitation, we are conducting research on magnetic levitation of thin steel plates. Thin steel plates are widely used in various industrial products. Currently, contact conveyance using rollers is mainly used in the conveyance process of thin steel plates; however, this method sometimes causes minute scratches on the contact surface, leading to quality deterioration. Therefore, non-contact conveyance by magnetic levitation is being considered. However, thin steel plates have low bending rigidity and deflection during levitation, resulting in large modeling errors and making levitation difficult. In this report, we used our proposed magnetic levitation system in which two electromagnets are placed at each long edge of a thin steel plate. The characteristics of the magnetic levitation control of thin steel plates during non-contact gripping are experimentally investigated.

Keywords: Mechatronics Systems
Abstract: Thin steel plates used in the manufacture of a wide variety of products are required to have a high surface quality. However, the manufacturing process involves contact conveyance using roller, which causes deterioration in quality. In recent years, research has been conducted on non-contact transport using magnetic levitation technology. In particular, the authors have proposed a system in which electromagnets are arranged only in the horizontal direction. Although this system achieves levitation, it is difficult to maintain stability during levitation. This issue is likely caused by flexibility of thin steel plates and modeling errors in magnetic levitation system. In this study, a method to improve the levitation stability by control system is examined.

Keywords: Mechatronics Systems, Robotic and Automation Systems
Abstract: A new electro-hydrostatic actuator (EHA) consisting of 2 single-rod cylinders has been proposed. The EHA can exert the same force in both directions, which is difficult to achieve with a single-rod cylinder; it provides high flexibility in implementation and has overcome the problem of a double-rod cylinder where the unused rod takes space. With the circuit formed in a closed loop, the extension and retraction of the 2 cylinders are highly synchronous. The EHA produced by joining 2 cylinders mechanically has twice the stroke of a cylinder. An oil leakage compensating circuit has been considered for refilling oil in the 2 divided chambers. An experiment on the fabricated EHA found that it has approximately the same speed on extension and retraction under the same input voltage.

Keywords: Robotic and Automation Systems
Abstract: G.Yamasaki, S.Tokunaga and K.Osuka Osaka University, Creative Design Studio on Technology, Japan

"Mechanism" is one of the fundamental studies of manufacturing, and some say that it has matured as a discipline because the mechanisms created so far have been used in a variety of machines, from the Karakuri dolls of the past to the various machines of today. However, we believe that there is still an opportunity to create new mechanisms by combining them with modern technologies such as 3D printers. With the aim of conveying the appeal of such mechanism and stimulating interest in manufacturing, we held our own seminar for high school students. The seminar included a lecture on the relationship between Karakuri dolls and robots, as well as the creation of a robot using a link mechanism and observation of its mechanism. In addition, various mechanism models created using 3D printers were displayed to help visitors better visualize their movements. According to a questionnaire survey, many participants said that they became interested in mechanism by observing the mechanisms of Karakuri dolls and robots, and it seems that we were able to convey some of the appeal of mechanism to them. If more students become interested in the field of mechanical engineering because of this seminar, our activities will become more meaningful. 

Keywords: Robotic and Automation Systems
Abstract: As labor shortages become more pronounced, the deployment of service robots as an alternative workforce is advancing. Autonomous mobile robots, such as AGVs and AMRs, have already been widely implemented in dedicated environments like warehouses. Furthermore, service robots are being introduced into human activity spaces for tasks such as product serving in restaurants and security in commercial facilities. In this context, the integration of manipulators is increasing the versatility of service robots. Given their potential to perform diverse tasks, there is a growing expectation for service robots to possess extensive mobility, including the capability to open and close doors within human environments. However, doors exhibit significant diversity in opening mechanisms, handle types, sizes, and textures, posing challenges for manipulator-based door operation. Although several studies have addressed these challenges, a universal scheme has yet to be developed. This paper focuses on swing doors with lever-type handles, which are prevalent in human activity spaces. We propose a novel door-opening operation scheme for service robots equipped with manipulators, featuring a hook-type end-effector that does not require actuators, and utilizing visual information.

Keywords: Robotic and Automation Systems
Abstract: The installation of robotic handling systems has been steadily increasing. However, these systems face significant limitations in three-dimensional measurement capabilities, especially when accurately measuring specular and transparent objects, which limits the range of objects that can be handled. To address this problem, we focus on the fact that segmentation is relatively easy for all objects regardless of their optical properties. We perform three-dimensional measurement by employing Shape from Silhouette (SfS) based on the segmented images. SfS reconstructs the object's three-dimensional shape by integrating its silhouettes from multiple viewpoints. Additionally, we employ a hand-eye system to capture scenes and perform 3D measurements during the approach and transportation of objects. This integration of three-dimensional measurement with path planning enables the handling of objects with diverse optical properties, while also minimizing task execution time. The results of this study will be presented in detail in the poster session.

Keywords: Robotic and Automation Systems
Abstract: As the backdrop of a global labor shortage, the introduction of service robots is progressing. Utilizing the mobility functions of service robots, examples of their use include performing tasks such as security, transportation, and cleaning. However, the environment in which service robots can be introduced is limited due to obstacles such as doors installed between rooms, and the majority of tasks are based on mobility functions. To address the worsening labor shortage, it is necessary to increase the environments in which service robots can be introduced and diversify the tasks they can perform. To achieve this diversification, this research focuses on door opening operations. Enabling door opening will allow for movement between rooms and can increase the environments where service robots can be introduced. Additionally, by opening storage space doors, the number of objects that can be grasped will increase, thereby expanding the variety of tasks that can be performed. Traditionally, door opening operations have been implemented primarily for swing doors and sliding doors, whose trajectories are relatively simple. This research proposes model predictive reinforcement learning for the opening operations of folding doors, which have not been previously addressed.

Keywords: Robotic and Automation Systems, Process Automation, Factory Automation
Abstract: Under the global labor shortage, the demand for robotic systems using manipulators is increasing in factory manufacturing and tasks like cooking in stores and conducting chemical experiments. The design of these manipulator-based robotic systems is largely divided into two parts: the design of operation facility and the design of facility layout and manipulator paths. The latter has become a bottleneck in system design, and methods for optimization without human intervention have been proposed. However, many of these methods rely on approximate evaluation of operation time to reduce computational costs, failing to compute optimal solutions. In this context, this paper proposes a method that simultaneously optimizes both the paths and layouts in manipulator-based robotic systems using C-space, without relying on approximate evaluations of operation time.

Keywords: Robotic and Automation Systems, Autonomous Decentralized Systems
Abstract: In times of disaster, it is crucial to understand the ground conditions in advance to ensure that construction machinery can immediately reach the site. This study aims to develop a system in which a group of drones cooperatively and distributively follows multiple routes to the destination to assess the terrain of these paths. This would enable the detection of obstructions and the exploration of navigable routes. This paper describes the initial step in which a single drone measures the undulations along a route, enabling the subsequent determination of the passability for construction machinery.

Keywords: Robotic and Automation Systems, Remote Sensing, Sensors and Transducers
Abstract: The Flexible Mono-tread mobile-Track (FMT) has a single track that can flex in three dimensions, consisting of multi-jointed vertebrae. This flexible structure enables the FMT to adapt its body shape according to the moving terrain. Utilizing this special feature, we have proposed an odometry method, which estimates the trajectory based on the changes in the robot’s posture and displacement. This method estimates the movement direction and global position of the robot by analyzing the relative angles between the joints and the rotation of the crawler, thereby reconstructing the robot’s posture over time and obtaining its trajectory. In experiments within a two-dimensional plane, we moved the FMT approximately 3 meters with turns and compared with the odometory and the trajectory to that of a motion capture system measured. The results demonstrated that the error in the trajectory estimated by the proposed method remained within 0.15 meters from the trajectory measured by the motion capture system. In this report, we aim to reduce the error in the movement trajectory by integrating acceleration obtained from an IMU with the measurements of posture and displacement. Specifically, we investigate whether integrating displacement from the incremental rotary encoder and the accelerometers using the Extended Kalman Filter improves the accuracy of the movement direction estimation.

Keywords: Robotic and Automation Systems, Signal and/or Image Processing
Abstract: The authors discuss a recognition function of a robotic system for automating the book arrangement process.

A straightforward method for this purpose is to recognize the call number which controls the order of books in a library. However this strategy has difficulty because call number stickers are often wider than the spine then only a part of the stickers are seen in the book shelf.

As a solution, the authors have developped a combined recognition sub system that cut out the book spine from the bookshelf image taken, cut out the book label from the cut out spine image, apply OCR to each image of the spine and the book label to obtain the call number and title, then collates the text similarity with a repository csv file that contains the titles and call signs of the books in the　bookshelf, and the one with the highest similarity is considered to be the true title and call number.

For this sub system, the authors has verified the correctness of the title and the call number for 42 books in a bookshelf with experiment.

The results shows that the percentage of correct answers was 88% for the title and 100% for the call number.

Keywords: Simulation of Large Systems, Modeling, System Identification and Estimation, Robotic and Automation Systems
Abstract: To ensure accurate self-localization in autonomous driving, verifying positioning precision before operation is crucial. However, there is a lack of simulators that can easily generate realistic environmental errors, especially for GNSS/IMU systems. This research aims to develop a GNSS/IMU simulator capable of generating such errors for pre-operational positioning accuracy verification. For GNSS, we focus on predicting positioning solutions based on the number of visible satellites. Using a 3D map simulation, we determine visible and non-visible satellites and predict positioning accuracy based on whether the visible satellite count exceeds a threshold. For the IMU, we simulate gyro errors using Allan variance. Previous studies have shown that environmental factors like vibrations degrade Allan variance performance during driving[1]. Therefore, we add a simplified vibration effect model to the Allan variance-based gyro error model to generate errors considering performance degradation. Finally, we validate positioning accuracy using actual vehicle data and simulator sensor data. The results showed similar trends in positioning estimates, confirming the potential for pre-operational verification of positioning accuracy.

[1] Yoshiki ATSUMI, et. al, “Evaluating the relationship between gyro performance and vehicle trajectory accuracy in a dynamic environment”, The 7th International Conference on Advanced Mechatronics (ICAM2021), 2021.7.GS7-3

Keywords: Simulation of Large Systems, Robotic and Automation Systems, Network System Integration
Abstract: The Moon's surface is covered with unique sand, the use of which could be a possible stepping stone for human expansion to other celestial bodies, especially as international competition for sand containing water has begun to intensify. The nonlinear behavior of sand is difficult to model analytically. In addition, a large amount of sand for experiments is required for the robot to act on the sand. In this study, an artificial sand was created by mixing inexpensive sand available on the earth at an optimum mixing proportion based on the grain size distribution of actual lunar sand. As an evaluation experiment of the artificial sand, we compared the grain size distribution of the artificial sand with that of the lunar sand using a permeation machine. As a mixing experiment of the artificial sand, the mixing condition of the artificial sand was made uniform. The behavior of the artificial sand was analyzed. This time, we report the behavior analysis of the sand when the robot hand grasps the artificial sand.

Keywords: Virtual Reality Systems, Electrophysiology and Kinesiology, Human Interfaces
Abstract: This study aimed to enhance the efficiency of motor learning by developing a new motor learning system utilizing virtual reality (VR) technology. Specifically, a task environment was constructed in VR using a haptic device to generate force fields. The system was designed to facilitate the consolidation of new motor skills and the suppression of incorrect motor actions. Participants were divided into three groups with varying degrees of learning: normal overlearning with 96 trials, half overlearning with 48 trials, and overlearning with retrieval suppression also consisting of 48 trials. The learning effects were assessed through a reaching task performed in the VR environment. The results indicated that the group with retrieval suppression showed learning effects comparable to the normal overlearning group and superior to the half overlearning group. These findings demonstrate the efficacy of the system in enhancing motor skill consolidation and suppressing incorrect actions. The outcomes contribute to technological advancements in motor learning and elucidate the impact of retrieval suppression in the processes of learning and memory.

Keywords: Components and Devices, SCADA and Network Systems
Abstract: The DX transformation of information control systems for social infrastructure such as electricity and water supply is progressing by incorporating rapidly evolving digital technologies such as IoT and AI. In order to provide advanced applications utilizing digital technologies, not only software updates but also high-performance hardware on the edge side are essential. However, the conventional control systems is composed of specially designed LSIs and specific electronic components, making it difficult to easily reconfigure or improve performance. Especially in the case of processr changes, differences in processor endianness and register size require significant modifications to exisiting software Therefore, in this report, we focus on processors, which have a significant impact on redesign cost during configuration changes, and propose an upgradable architecture that allows processors to be easily changed without modifying existing software and increase the cost required for redesign. by utilizing FPGA and hardware-based endian conversion. By using processors with different endianness and evaluating the amount of software modification required when changing processors, we confirmed that the upgradable architecture can reduce the amount of modification by more than 90% compared to simple configuration changes.

Keywords: Network System Integration, Signal and/or Image Processing, Intelligent Systems
Abstract: This paper focuses on the design and verification of a deep learning model combining AutoEncoder and image recognition for IoT applications. The research aims to evaluate the integration of AutoEncoder and image recognition models, particularly assessing the performance of image compression and reconstruction using different kernel sizes and pooling methods. Furthermore, the study explores the classification accuracy of an image recognition model using latent variables obtained from the AutoEncoder. The proposal is structured to include an introduction to the research background and objectives, a detailed explanation of convolutional neural networks (CNNs) and upsampling techniques, experimental design and results for deep learning models, and an evaluation of GPU capabilities for enhanced performance. The conclusion summarizes the research findings and discusses future research directions and potential applications. This work contributes to the practical development of efficient image recognition systems by evaluating the effectiveness of integrating AutoEncoder and image recognition technologies.

Keywords: Network System Integration, System Engineering, Safety, Environment and Eco-Systems
Abstract: We show a coil-patterned waveguide sheet designed for magnetic coupling in a two-dimensional waveguide power transfer system (2DWPT). In the context of magnetic resonance-based wireless power transfer, the coupling coefficient diminishes due to flux leakage. Our design extends the length of the transmitter (Tx) waveguide sheet without diminishing the coupling coefficient. This is achieved by modulating the group velocity in the waveguide sheet to manage leakage flux. The viability of this waveguide power transfer approach is substantiated through straightforward theoretical analyses. Both our simulation and experimental results align with the theoretical predictions, confirming that the waveguide sheet maintains operational efficiency over extended distances.

Keywords: Simulation of Large Systems
Abstract: This is a study of diesel engine control methods using the Shioji Maru, a training ship of the Tokyo University of Marine Science And Technology.

The main purpose of this research is to develop a new control method to improve incomplete combustion conditions caused by load change on the engine, and to improve fuel efficiency.

When a ship is subjected to waves, the load torque of engine would change. Then the unnecessary engine speed adjustment occurs. Finally, fuel consumption will increase.

As a first step, an accurate model to reproduce engine speed, vessel speed, and fuel consumption was constructed using MATLAB Simulink

Now we are studying a new control method to improve fuel efficiency by changing the control parameters of this model.

Keywords: Medical and Welfare Systems, Electrophysiology and Kinesiology, Integrative Biophysical Engineering and Informatics
Abstract: Fishermen experience occupational injuries due to manual handling such as lifting and pushing. Investigation of the relationship between musculoskeletal loads and strategies for manual handling will contribute to preventing occupational injuries among fishermen. The objective of this study was to compare full body musculoskeletal loads between lifting and pulling strategies. Joint angles on sagittal plane in lifting and pulling postures (total 80 postures) of fishermen were collected from public image dataset for tuna fishing activity on the ship. Collected joint angles were applied for musculoskeletal simulation using Chaffin et al.’s biomechanical model and BlessPro2 software. Compression and anteroposterior shear forces of L5-S1 (lumbar) and joint moments (neck, shoulder, lumbar, hip, knee, and ankle) were calculated as musculoskeletal loads via simulation. Calculated musculoskeletal loads between lifting and pulling were compared by Mann-Whitney U test (significant level: p < 0.05). The results showed that musculoskeletal loads at lumbar and hip parts of lifting tasks were significantly larger than pulling. Furthermore, musculoskeletal loads at shoulder and ankle of pulling tasks were significantly larger than lifting. These results recommend that fishermen need to switch lifting and pulling strategies depending on the body part should be reduced loads or fatigue.

Keywords: Process Automation, Discrete Event Systems, Information Management Systems
Abstract: An audit trail of measuring instruments subject to legal regulation is used to provide the evidence of and the prevention from intervention. Checking of the audit trail according to legal guidance is likely to be performed in the future. To prepare for this, previous studies have suggested using a verification technique called runtime verification (RV) for the audit trail check. The audit trail check using RV is to let the RV tool determine whether or not records in audit trail meet the legal requirements described as check formulas. In order to demonstrate the applicability of RV to the audit trail check, we are aiming to perform the actual check. However, measuring instruments that this method can apply to were not easily available for us. Therefore, we perform the audit trail check for a virtual measuring instrument instead of an actual measuring instrument. For this purpose, we have developed a design for a non-automatic weighing instrument that the audit trail check using RV can apply to. In addition, by analyzing the design, we have derived the check formulas which are inputted into RV tool. In the poster presentation, we introduce this design and these check formulas.

Keywords: Process Automation, Robotic and Automation Systems, Factory Automation
Abstract: The amount of electronic waste (e-waste) is increasing every year. Automation of disassembly processes is required to promote resource recycling. Unlike manufacturing, disassembly deals with diverse small-batch e-waste, making traditional teach-and-playback methods unsuitable. Therefore, a robust and high-precision recognition system for the disassembly of objects is essential. This study aims to automate the removal of wires, a significant part of e-waste disassembly, by developing a system for wire recognition. The system must achieve high prediction accuracy despite variations in wire shape, lighting, background, and partial occlusion. Additionally, it should be capable of recognizing various challenging objects within the disassembly process using the same framework. To meet these requirements, we build with models that score well on the semantic segmentation task. The results of this study will be presented in detail in the poster session.

Keywords: Century of light, invisible light
Abstract: The 21st century is often referred to as the “Century of Light”; however, it does not mean that the light has been used up. In particular, the invisible light, such as “ultraviolet light”, “infrared light”, and “terahertz light”, has different characteristics from “visible light” and therefore has even greater potential. Such invisible light has a potential to lead to the creation of photonic innovations that are essentially different from the past. In this presentation, I will introduce a potential of the invisible light.

Keywords: Identification and Estimation, Electrophysiology and Kinesiology, Mechatronics Systems
Abstract: This paper is concerned with the estimation of wrist impedance based on its relation to the EMG and angular position information. A neural network model is employed to describe the relationship. To obtain training data, we developed a desktop system consisting of a direct-drive DC motor, potentiometer, and EMG sensor. Data acquisition was conducted with four healthy male participants, and considering practical use, we divided the participants into training and evaluation groups. The proposed method was verified in that the preliminary trained neural network would be able to give an appropriate estimate to the user who had not been involved in the pre-training. The results indicate the feasibility of the proposed impedance estimation method based on EMG and angular position information.

Keywords: Identification and Estimation, Factory Automation
Abstract: This paper addresses the challenge of accurately estimating the posture of objects for tasks like industrial robot operations where measurement data may be incomplete. Existing methods using SVD offer fast 3D pattern matching but require preprocessing to avoid bias. The proposed solution introduces MaskNet, an artificial neural network model, to overcome this limitation. MaskNet assists in matching approximate shapes of point cloud datasets (PCDs), enhancing the generality and accuracy of posture estimation, crucial for tasks like grasping and assembly. Comparative experiments with existing methods revealed that manual extraction of PCDs became unnecessary, and the proposed method reduced the pure execution time by one-third.

Keywords: Identification and Estimation, Factory Automation, Human Interfaces
Abstract: In modern smart manufacturing, machine condition monitoring is essential. To achieve the goal, the associate status monitoring system needs to have several features such as data acquisition from sensors, edge computing for generating effective index, and user interface to display information and communicate with the host. To accomplish these abilities, this work hires a M487 microcontroller (MCU) and a R-Pi single-board computer (SBC) to develop the associate status monitoring system for both computational versality and cost-saving considerations. The microcontroller is responsible for data acquisition and edge computing, while the single-board computer would handle the data transmission and user interface issues. The entire system is modulized for ease of carry and modification in any factory equipment. The module could extract key sensor indexes in both time and frequency domains per 30 seconds for signals from 12 channels and has been operated for approximately one month continuously in a local LCD panel manufacturing company for diagnosing possible abnormal conditions. In comparison to other modules, the designed module is far more flexible and powerful for practical industrial needs and should be very useful for cyber-physical system realizations.

Keywords: Identification and Estimation, Integrative Biophysical Engineering and Informatics, Human Interfaces
Abstract: Modeling individualized facial happiness assessment is essential for personalizing recommendations and conversions of facial expressions. We propose an efficient comparison-based method for estimating and optimizing individualized facial happiness assessment in a situation where the number of interactions with the individual is limited. The method combines the black-box function optimization framework, called Bayesian optimization (BO), with list-wise Gaussian process preference learning, which learns a model from list-wise comparison data. In each interaction, the individual ranks a given set of facial images in order of how happy they appear, and the model is updated for each interaction. By using BO, the model allows us to efficiently determine which facial images should be presented to the individual in the next interaction. By using the ranking information represented by list-wise comparison data, the amount of information obtained from a single interaction is increased, which reduces the number of interactions and the time required for training. Experiments on modeling the individualized facial happiness assessment of photo-realistic virtual characters show that the proposed method can construct a more accurate model and find near-optimal facial expressions with fewer interactions than the conventional method.

Keywords: Identification and Estimation, Modeling, System Identification and Estimation, Mechanical Systems Control
Abstract: In this paper, we try to detect the anomaly in systems by using parameter estimation via deep learning for low-order controlled models in the closed loop system.

Keywords: Identification and Estimation, Signal and/or Image Processing, Analytical Measurement
Abstract: We analyzed the relations among foot plantar pressure measured by a Pedar insole with 99 sensors and 3D coordinates of body keypoints extracted by MMpose applied to a video of a person normally walking on a treadmill. Each pressure of the foot plantar was sufficiently predicted by the 13 major keypoints in the preceding 20 frames in the video. In one-leg heel and knee joint-fixed walking, we observed asymmetrical pressure patterns between left and right feet, and they were also well-predicted by the same regression model. The difference between the normal walk and the asymmetrical walk was indicated by the trajectory of the Center of Pressure (COP) predicted by the model.

Keywords: Identification and Estimation, Social Systems, Transportation Systems
Abstract: The increased integration of renewable energy, which is known to fluctuate owing to weather conditions, has necessitated power supply-demand adjustments using virtual power plants (VPPs) that utilize vehicle batteries. Predictions of the available adjustment capacity of a VPP can prevent excessive power generation. Therefore, the state of charge (SOC) of individual vehicles participating in the supply-demand adjustment market must be predicted up to one week in advance. Because vehicle owners' actions determine SOC behavior, considering their periodicity and uncertainty is essential. This study proposes a novel probabilistic model called the matrix-variate Gaussian process (GP) with structured kernels, while focusing on the periodicity. To achieve high accuracy through recursive multi-step-ahead prediction using GP, large computational resources are required. However, the proposed method can handle a matrix with rows for the time of day and columns for the day of the week, which facilitates the production of a one-week-ahead predictive distribution through a single calculation. Furthermore, the proposed method directly captures the periodicity using separate kernels to express the characteristics of rows and columns, making the parameters easier to interpret. Finally, we evaluated the performance of the proposed method using real data from 100 vehicles over up to nine months.

Keywords: Identification and Estimation, Standard of Measurement
Abstract: The Kalman filter is indispensable for state estimation across diverse fields but faces computational challenges with higher dimensions. Approaches such as Riccati equation approximations aim to alleviate this complexity, yet ensuring properties like bounded errors remains challenging. Yamada and Ohki introduced low-rank Kalman-Bucy filters for continuous-time systems, ensuring bounded errors. This paper proposes a discrete-time counterpart of the low-rank filter and shows its system theoretic properties and conditions for bounded mean square error estimation. Numerical simulations show the effectiveness of the proposed method.

Keywords: Adaptive and Optimal Control
Abstract: This paper presents a discrete-time, linear state feedback control strategy rooted in Q-learning, one of Reinforcement Learning (RL) approaches, to address an adaptive Linear Quadratic (LQ) problem under stochastic disturbances. The Q-learning optimizes the state-action policy by estimating the Q-function iteratively. However, in the case where stochastic disturbances are present, a recursively defined Q-function is definitely affected by stochastic disturbances and a standard Least Square (LS) method for Q-function estimation suffers from bias-error. Therefore, this study proposes a novel idea of Q-Learning algorithm that modifies recursively defined Q-function by adding a disturbance influenced term and utilizes an Instrumental Variable (IV) technique to resolve the bias-error issue. Furthermore, an extended parameter representation is also introduced to estimate the disturbances influenced term simultaneously. The efficacy of the proposed method is substantiated by comprehensive simulation results.

Keywords: Adaptive and Optimal Control, Modeling, System Identification and Estimation
Abstract: This paper proposes a data-driven PID gain updating method based on model matching to a desired reference model which is given as the transfer function of the closed-loop system from the reference signal to the output. The new model matching error introduced in this paper allows us to find a PID gain achieving the model matching, using the least squares method with the closed-loop data obtained from the poorly tuned closed-loop system. Although the proposed method has several design parameters including those of the reference model, they can be easily set using the frequency

Keywords: Discrete Event Systems, Adaptive and Optimal Control
Abstract: This paper discusses a data-driven design method of dynamic quantizer. In particular, we consider a parameter tuning of noise-shaping filter under an assumption that the input-output data are available but the target plant itself is unknown. We first focus on the input-output relationship of the noise-shaping filter in the optimal dynamic quantizer. Based on this relationship, a data-driven parameter tuning method which makes the noise-shaping filter similar to the optimal one is proposed. The effectiveness of the proposed method is demonstrated through a numerical example.

Keywords: Modeling, System Identification and Estimation
Abstract: Schemes for predicting the reference signal response when the controller or controlled system changes have already been proposed, but these schemes do not consider the disturbance response. This paper proposes a method for generating disturbance response utilizing a FIR filter. Specifically, an FIR filter is designed utilizing reference signals and assumed disturbance signals. The FIR filter and the output signal are then used to generate a closed-loop disturbance response.In numerical simulations, it was confirmed that the closed-loop disturbance response is generated well even for control systems that include time-delay.

Keywords: Adaptive and Optimal Control, Nonlinear Control
Abstract: In this paper, we propose a reinforcement learning based algorithm to learn input-output history feedback controller from initial experimental data. The effectiveness of the proposed method is verified through a numerical simulation.

Keywords: Intelligent Control, Adaptive and Optimal Control, Computer Aided Design
Abstract: This paper proposes a hysteresis model for control using the rectified linear unit (ReLU) function, which is a type of activation function for neural networks (NNs). When nonlinear elements such as hysteresis are represented with general-purpose NNs, many layers and units are generally required, and lead to long computation time for learning. Also, it is to difficult to explicitly obtain an inverse model of such an NN-based hysteresis model, which can be used for hysteresis compensation in control systems. The proposed ReLU-based hysteresis model resolves these problems, and its effectiveness is illustrated through numerical examples.

Keywords: Sensors and Transducers
Abstract: We have developed a variable-sensitivity force sensor that uses a shape-memory polymer (SMP) sheet with an embedded electric heating wire, the stiffness of which varies depending on temperature. In our previous study, we evaluated a structure with a beam fixed at both ends. However, because there was a difference between the measured and theoretical values, in this study, we modified the structure of the SMP force sensor. Moreover, we applied the prototype sensor to the measurement of the compressive elastic modulus of several objects and evaluated its performance.

Keywords: Sensors and Transducers, Adaptive and Optimal Control
Abstract: This is the first paper that an optimization method on a manifold is applied to the generation problem of multiple focal points by the ultrasonic phased array. For generating multiple high-intensity ultrasonic focal points, it is rational to keep the output of the phased array at the maximum. Thus, the only independent variable of the optimization problem is the phases of the ultrasonic transducers. This is equivalent to an optimization problem on a manifold called a complex circle. We evaluate the sound pressure variation on the focus when multiple focal points are generated and show an example of the generation ultrasound distribution.

Keywords: Sensors and Transducers, Identification and Estimation
Abstract: To generate a focal point accurately using an ultrasonic phased array, the phase offsets for each transducer must be estimated in addition to the position and orientation of the apparatus. In this article, we present a method to estimate these variables based on Time-of-Flight (ToF) measurements. In particular, we first measured ToF at multiple observation positions and then optimized the device's position, orientation, and phase offsets to align with the measured ToF data. We conducted numerical simulations and experiments using actual devices to validate the effectiveness of our method.

Keywords: Sensors and Transducers, Intelligent Systems, Agricultural and Bio-Systems
Abstract: This paper presents the design of an extendable pneumatic actuator constructed from soft materials, inspired by the well-known Yoshimura origami cylindrical patterns. The study undertakes a comprehensive analysis and proposes a modified structure of Yoshimura origami based on previous literature. The manipulator can extend up to 4.9 times its folded length. The prototype was fabricated using soft elastomer materials, and its performance was thoroughly evaluated. The challenges encountered during the fabrication process, along with the performance issues, are identified, and potential solutions are proposed.

Keywords: Sensors and Transducers, Mechanical Measurement
Abstract: We propose a fabric touch panel and a detection algorithm for human stroking motions, with their application to a stuffed animal robot. Stuffed animal robots are often used as an effective therapy. Fabric touch panels are applicable to stuffed animal robots for interfaces. The sensor of the proposed fabric touch panel is made of cloth sewn with conductive threads. We applied the needle thread adjustment function of a sewing machine to fabricate the sensor of a fabric touch panel. Then, we constructed an algorithm discriminating human finger stroking motions based on frequency analysis. We found that ten human stroking motions could be discriminated. Finally, we applied the fabricated fabric touch panel and stroking recognition algorithm to a stuffed animal robot. We confirmed that the stuffed animal robot could perform the assigned movements successfully according to ten stroking motions.

Keywords: Sensors and Transducers, Mechatronics Systems, Robotic and Automation Systems
Abstract: In recent years, robots have been introduced into various aspects of society. When robots are asked to perform a wide variety of tasks, it is necessary to know what kind of object they are touching by means of sensors in order to prevent the destruction of their own hands and objects. However, such tactile and force sensors are expensive. To solve this problem, we have been developing inexpensive optical tactile sensors that reproduce the higher-order functions and flexibility of human hands and skin. This paper describes the development of the optical tactile sensor and performance evaluation.

Keywords: Sensors and Transducers, Opto-Electronic Measurement, Signal and/or Image Processing
Abstract: This paper introduces a home-made FPGA-based instrument for quantum physics experiments. It is implemented as a special photon counter to detect trapped ions. The design of the instrument is optimized by splitting the real-time tasks to the FPGA and floating-point calculations to the PC. The system is fast iterated and evolved according to the users’ feedback.

As programmable hardware, an FPGA has the merit that it can be reprogrammed like software, while it is good at real-time applications. These features make it a desirable choice for the home-made instruments to support our special and fast-developed research ideas. The best practices in software development, such as fast iteration, feedback, and evolution, apply to the development of an FPGA-based system. In our photon counter, the core logic is abstracted from the system to only count the PMT pulses separately in two periods: P1 and P2. This core logic is implemented in the FPGA and shared by the entire system to support three different experiments. The PC is responsible for calculating the results and draw the plots in separate way according to the experiment type. This optimization makes the development simpler and faster and promises the robustness of the final product.

Keywords: Sensors and Transducers, Signal and/or Image Processing, Identification and Estimation
Abstract: In a typical rugby game, the scrum is formed about 20 times per game, and when various factors disturb the balance of power, the scrum collapses, subjecting the front-row players to significant biomechanical loading and resulting in head and neck trauma. The aim of this study is to propose a new method of analysis using IMU + GPS sensors to predict serious accidents and improve practice efficiency. The horizontal acceleration and spring constants and viscous damping coefficients between rugby players were calculated using electrical signals measured by IMU sensors output attached to players. The measurement targets were Japanese professional league players, and the dynamics of the six front-row players in the scrum were analyzed. The development of this technology will lead to the practical application of scrum monitoring systems, improve the quality of scrum practice, and contribute to the understanding of the mechanism of scrum collapses.

Keywords: Sensors and Transducers, Signal and/or Image Processing, Ultra-High Precision Measurement
Abstract: Azbil Corporation (hereinafter referred to as Azbil) has developed and marketed the Model SPG sapphire capacitance diaphragm gauge equipped with a micro electromechanical systems (MEMS) sensor chip that uses single-crystal sapphire as the base material for the diaphragm, ensuring exceptional corrosion and heat resistance. Recently, Azbil developed the new Model V8 sapphire capacitance diaphragm gauge, which offers additional features and enhanced performance. For the Model V8, the circuitry has been thoroughly re-engineered to create a separated model in which the gauge head and control unit are separated, achieving a faster response speed. This paper focuses primarily on the development items related to its circuitry.

Keywords: Information Management Systems, Manufacturing Systems, Factory Automation
Abstract: Improving energy efficiency and reducing fossil energy-based operations are required to mitigate global warming. Industrial fields consume large amount of energy and are expected to play significant roles in the measures to mitigate global warming. Several industrial facilities are operated intricately in their production activities. Multiple products are manufactured concurrently to adhere to their quality, product cost, and due date. Their energy consumptions affect each other. Therefore, industrial facility energy management system (FEMS) is required and sold by many suppliers. Because energy consumption of plants is depended on their products, FEMS are constructed to meet their specific requirements; thus, it is difficult to develop international standard for FEMS. Recently, use cases with energy interchange between multiple plants are increasing; thus, common understanding of energy management process with FEMS and information exchange between the factories are needed. So, Japan Electronics and Information Technology Industries Association led a development project and suggested an FEMS international standard. In this paper, development international standard process including clarification of background and current issues; additionally, a consensus in IEC discussion and outline of the published standard are described.

Keywords: Manufacturing Systems, SCADA and Network Systems, Information Management Systems
Abstract: The paper reports a case study for realizing carbon neutral society by smart industrial energy management systems. Importance of identification of energy-waste and energy-loss is demonstrated. We have introduced a concrete solution using optimization of production schedule which uses data coordination between FEMS (Factory Energy Management System) and BEMS (Building Energy Management System), which consists of 1) a technology which optimize the energy resources, 2) a testbed which generalize concrete application, and 3) FEMS with coordinated information model, and a draft of a relevant international standard.

Keywords: Information Management Systems, Standard of Measurement, Identification and Estimation
Abstract: Many countries have set targets for energy efficiency improvement and greenhouse gas emission reduction as measures against global warming. To achieve them, organizations are required to achieve their own targets. Therefore, energy management systems are becoming important for organizations. ISO TC 301 developed ISO 50001:2018 to establish requirements for improving energy performance and energy management system (EnMS) with a plan-do-check-action (PDCA) cycle. In this PDCA-cycle, organizations are required to improve systems before/after ISO 50001 implementation. To maintain organizations’ motivation for the improvement activities, methods for assessing their improvements and showing their efforts are required. Therefore, the Japan Electronics and Information Technology Industries Association (JEITA) suggested an international standard for assessing the energy management of organizations and has led the development project. In this study, the development of the international standard process including clarification of the background and current problems are explained. Furthermore, a consensus in ISO discussions is discussed and the outline of the published standard is described.

Keywords: Components and Devices
Abstract: In recent years, demand has grown for smart manufacturing, which aims to network the entire supply chain, from the equipment and product level, to streamline everything from design and production to maintenance. To achieve this, it is important to carry out a digital transformation that creates new value by leveraging a large amount of data from the field. To utilize the large amount of information generated on-site as data, sending all the data collected by sensors to the cloud via the internet would result in enormous data volumes and increased network load. Therefore, edge computing is gaining attention as it aims to reduce network load by processing data with components installed on-site and sending only valuable data to the cloud system. Components used for edge computing need high processing performance to reduce the load on the cloud system, as well as reliability and robustness to withstand use in manufacturing sites and similar environments. To support this, Toshiba Infrastructure Systems & Solutions Corporation has developed the rack-mount type industrial computer FR2100TX model 700, which improves processing performance using a high-performance CPU architecture.

Keywords: Factory Automation, Manufacturing Systems, Process Automation
Abstract: In recent years, the environment surrounding manufacturing and social infrastructure has faced an era where it is difficult to predict the future and has various challenges. In such a situation, the OODA (Observe, Orient, Decide, Action) loop, a framework that can quickly respond to sudden external changes, has attracted attention in manufacturing to improve quality and production efficiency in a timely manner. On the other hand, the OODA loop requires simulating the situation on site for situation judgment and analysis. One of the solutions to construct a simulation environment is to use the cloud. To control the site using the results of the simulation in the cloud, a common control platform that can be used on both the site and the cloud is one of the solutions. Toshiba Group has released a cloud-based PLC typeN1 that controls the site from the cloud environment. In this paper, we explain the technology of typeN1 and its application examples to improve quality and production efficiency in a timely manner in manufacturing. By applying the analysis tools provided on the cloud to typeN1, it is possible to realize the automation of the appearance inspection process of the production line on site at a low cost. This contributes to the timely improvement of quality and production efficiency in manufacturing.

Keywords: Network System Integration, System Engineering
Abstract: The Society of Instrument and Control Engineers (SICE) technical committee on industrial networks and system in the industrial application division conducts surveys of control system users as part of its activities to help improve control system security. This paper discusses the challenges users face in implementing cybersecurity measures for industrial control systems, as identified in a recent survey. It then introduces initiatives aimed at addressing these challenges.

Keywords: SCADA and Network Systems
Abstract: This study proposes an innovative approach to enhance the security of existing industrial control systems without system reconfiguration. By leveraging ARP spoofing and network scanning for asset identification, our method introduces security enhancements within edge networks without altering network configurations. Our findings in this study indicate that this proposed method can activate security measures such as asset management and authentication on insecure existing edge networks. The effectiveness of this method in strengthening the security of existing edge networks is confirmed, presenting a viable solution for enhancing ICS security with minimal impact on existing infrastructure and operations.

Keywords: SCADA and Network Systems, Information Management Systems
Abstract: Knowledge across multiple disciplines, including control, communication, software, and signals, is essential to operate cybersecurity technologies for control systems properly. Unfortunately, engineers with such interdisciplinary knowledge do not exist. In other words, reskilling of engineers is imperative. This program is a preliminary report on the trial and error involved in developing a training program intended to familiarize engineers who possess elementary knowledge of control systems with the basics of cybersecurity, focusing primarily on control communications.

Keywords: SCADA and Network Systems, Network System Integration, Information Management Systems
Abstract: Cyber threats that disrupt the normal operation or cause accidents of critical infrastructure systems and industrial automation control systems have become an urgent issue. The governments around the world are advancing regulations and providing political support for OT (Operational Technology) cybersecurity. In addition, several international organizations promote standardization and its training. Also, SICE holds study groups on OT cybersecurity to conduct technical discussions and research. At this year's SICE FES, the Industrial Applications Division (IA-Div) and the International Standardization Committee (ISC) will hold a Special Panel Session (SPS) on OT cybersecurity standardization and regulation. Experts will discuss the state of government policy and international standardization in Europe, North America, and Japan. This paper provides an overview of that panel session.

Keywords: Adaptive and Optimal Control, Power Systems Control, Computer Aided Design
Abstract: In this paper, we propose a method for tuning a regularization parameter in data-driven predictive control considering noise. The proposed method uses the error between the predictions obtained from the corresponding optimization problem and actual measurements as an indicator, and adjusts the regularization parameter while controlling the system. The effectiveness of the proposed method is confirmed through numerical examples.

Keywords: Adaptive and Optimal Control, Nonlinear Control
Abstract: This paper proposed a driver assistance control algorithm with an active front steering and velocity control system. This algorithm evaluates road and input constraints in real time to avoid a collision while maintaining a consistent driving performance. The control algorithm is based on a quasi-linear parameter varying model predictive control law with state constraints, and is reduced to a convex quadratic programming problem. Simulations using the CarMaker platform are used to validate the efficiency of the algorithm.

Keywords: Adaptive and Optimal Control, Nonlinear Control, Mechanical Systems Control
Abstract: The paper suggests a fast NMPC computation method using the nondimensionalization of tolerance error by which no approximations such as linear approximation and singular perturbation for the nonlinear nominal hydraulic cylinder dynamics are used. The average computation time just before applying the suggested method is 1.24 [ms] but is reduced to 0.75 [ms] due to the suggested method. The effectiveness of the suggested method is numerically confirmed via an experimental hydraulic cylinder.

Keywords: Adaptive and Optimal Control, Signal and/or Image Processing
Abstract: Dithering is widely used in signal processing as a quantization method to mitigate the negative effects of quantization. Utilizing its probabilistic properties, stochastic model predictive control, which combines dithering and model predictive control, has been developed. This paper applies this stochastic model predictive control to image processing and proposes an optimization method for image halftoning.

Keywords: Adaptive and Optimal Control, Modeling, System Identification and Estimation, Multivariable Control
Abstract: This extended abstract proposes dynamic positioning (DP) control of a quadmaran vessel using Koopman model predictive control (MPC). The nonlinear model of the quadmaran vessel is approximated by a higher-dimensional linear model through Koopman linearization. This linearization gives a linear MPC problem, which has less computational complexity than a nonlinear MPC problem. The effectiveness of the proposed method is verified through simulations.

Keywords: Adaptive and Optimal Control, Modeling, System Identification and Estimation, Nonlinear Control
Abstract: The nonlinear nature of quadcopter dynamics makes it difficult to apply control methods, so linearization expands the applicable control method. In this study, we use Koopman linearization, a linearization method using Koopman operators. Koopman linearization is a method for estimating a linear model from quadcopter input-output data, and the accuracy of the model depends on the choice of observables and the variety and amount of the data. In this paper, the observables are constructed from the terms in the nonlinear dynamical model, and the Koopman model is generated from the data gathered by actual experiments. The generated model's validity is examined via the actual experiment.

Keywords: Multivariable Control, Modeling, System Identification and Estimation
Abstract: This paper considers model predictive consensus control (MPCC) with receding horizon consensus estimation (RHCE) for multi-agent systems subject to disturbances. The disturbance degrades the control performance, which requires filtering to reduce its effect. Integrating MPCC and RHCE allows for the natural introduction of distributed optimization. That is, each agent executes smaller-size optimization with the information of the neighboring agents. For the distributed problem, this paper discusses the alternating direction method of multipliers. A numerical example illustrates the effectiveness of integrating RHCE and MPCC.

Keywords: Signal and/or Image Processing, Robotic and Automation Systems, Guidance and Flight Control
Abstract: In this study, we investigate a 3D measurement system without blind spots using omni-directional cameras for an UAV. In order to fly for an UAV safely, it is necessary to measure the 3D positions of surrounding objects during flight and to detect objects that obstruct the flight. Here, we consider a tetrahedron surrounding an UAV and propose a method to measure the 3D position around the UAV by attaching omni-directional cameras at each of the four vertices of the tetrahedron. We conduct measurement experiments and show the effectiveness of using multiple omni-directional cameras.

Keywords: Medical and Welfare Systems, Temperature Measurement, Signal and/or Image Processing
Abstract: Infrared thermography, which has widely spread particularly during the COVID-19 period, has been effectively used for research on health monitoring and emotion estimation. Nevertheless, detecting minute temperature changes with thermography is challenging as it is disturbed by not only noise but also outside temperature surrounding the object. In this study, we demonstrate detecting face temperature variation by implementing lock-in thermography using heartbeat signals as a reference. It allows us to detect minute temperature changes, as low as ~10 mK, on the forehead with a commercially available thermal camera. The proposed approach enables stable measurement of body temperature variation, showing potential for non-contact emotion estimation.

Keywords: Signal and/or Image Processing, Sensors and Transducers, Computational Intelligence
Abstract: Motion-blurred images are the result of an integration process, where instant light intensity (which can also be understood as an instant scene) is accumulated over the exposure time. Unfortunately, reversing this process faces two main challenges. Firstly, integration destroys the temporal ordering of motion, resulting in ambiguity in the motion direction within a single motion-blurred image. Secondly, restoring sharp texture is a blind deconvolution task, which is highly ill-posed. To address the first challenge, we leverage a crucial clue: the correlation image, generated by the three-phase correlation image sensor (3PCIS). This image effectively expresses motion information over the exposure time, which is essential for determining the motion direction of moving objects. To tackle the second challenge, we adopt a supervised deep-learning scheme to address the ill-posedness of deblurring. Overall, we propose a two-stream network that restores a sequence of sharp frames from a pair of motion-blurred images and their corresponding correlation images. Experimental results demonstrate that our proposed method surpasses previous state-of-the-art methods on the GoPro public dataset and in real scenes.

Keywords: Signal and/or Image Processing, Agricultural and Bio-Systems
Abstract: In agricultural production, the automatic segmentation of green pepper images is challenging due to the similar color between the peppers leaves, and stems. This study proposes a hyperspectral data augmentation technique that combines low-pass filtering (LPF) with Mixup, aiming to improve the accuracy of green pepper segmentation. We conducted two sets of experiments to evaluate the effectiveness of this method: 1) A binary classification of fruit and non-fruit pixels on a multilayer perceptron(MLP); 2) Six-fold cross-validated image segmentation on CNN. The experimental results on the test set showed that the proposed method improved the average F1 score by 15.9% in the classification task and 2.1% in the convolutional neural network compared to traditional methods. It also helped to enhance model stability and generalization ability.

Keywords: Signal and/or Image Processing, Agricultural and Bio-Systems
Abstract: In Japan, the need for automation and streamlining in agricultural work has been increasing in recent years, due to the decreasing number of farmers. Fruit detection is one of the most important technologies for enhancing automation and streamlining. Typically fruit detection is performed using RGB images, however, in scenarios where fruit and foliage exhibit similar color profiles, reliance solely on color information proves insufficient. Consequently, there is a pressing need for supplementary cues to enhance detection accuracy. To meet this requirement, our paper introduces a novel approach that utilizes both RGB and thermal images simultaneously as inputs within the framework of Mask R-CNN, an object detection and segmentation technique. We demonstrate the effectiveness of this approach in enhancing fruit detection performance by comparing average precision (APs) obtained from inputs with 4-channels and RGB image only configurations.

Keywords: Remote Sensing, Sensors and Transducers, Innovative Systems Approach for Realizing Smarter World
Abstract: We propose a method for remotely estimating the direction of indoor gaseous sources based on the use of electronically steerable ultrasound-driven narrow airflows that create midair gas transportation paths to a spatially fixed sensor. Localization of gas sources have been investigated for several decades, where use of mobile robots equipped with sensors is a current dominant method. However, there are difficult cases to apply the moving robots, where workers are present or footing condition for the robots is bad, for example. Our proposed method employs narrow airflow paths that conveys the gas on the path to the sensor, hereby is free from those problems. In this paper, we experimentally assessed the possibility of the direction-estimation principle, where the sensor output is temporally related to the scanning angle of the ultrasound-driven airflows. It was confirmed that the sensor responded significantly when ultrasound-driven acoustic flow passed near the gaseous substance source. At the same time, significant sensor response was also observed occasionally for the case where the flow path was not close to the source. We give a brief discussion on the possible causes for this and propose several solutions.

Keywords: Signal and/or Image Processing
Abstract: This paper introduces the novel concept of infrared few-shot open-set recognition (IFOR), tailored for effective nighttime wildlife surveillance using a limited number of infrared images to classify animals and identify previously unseen species. The investigation assesses the proficiency of ResNet18 (a CNN model) and vision transformers (ViT) in classifying infrared images without color information. Then, we compares transfer learning models pre-trained with monochromatic fractal images based on formula-driven supervised learning (FDSL) against those trained on ImageNet. Furthermore, the research examines the role of meta-learning in augmenting feature extraction capabilities to achieve a balance between few-shot learning (FSL) and identifying unseen species. The outcomes indicate that ViT, augmented with ImageNet transfer learning and meta-learning, demonstrates superior accuracy, thereby highlighting the effectiveness of ViT and meta-learning within the IFOR framework.

Keywords: System Engineering
Abstract: This paper proposes a method for the numerical design of vector field that binds desirably configured unstable periodic orbits (UPOs) into a chaotic attractor with a reasonably large attracting domain. The proposed method extends our previous approach comprising the following four basic steps: (a) constructing a chaos-generating template that consists of a set of attractor-forming trajectories and explicitly embedded UPOs, (b) deforming the template in a topology-preserving manner and according to the desired configuration of UPOs, (c) deriving the target velocity vector that locally conforms the deformed template, and (d) obtaining a vector-field function of a continuous-time dynamical system via neural-network learning. To enlarge the size of the attracting domain while maintaining the modular and systematic procedure, we here propose to introduce a multi-stage learning, where the resulting vector field in step (d) is used as a "natural" component of the target vector incorporating natural and directed (inward) components for the next learning stage.

Keywords: System Engineering, Autonomous Decentralized Systems, Computational Intelligence
Abstract: A swarm robotics system (SRS) is a multi-robot system inspired by the self-organizing capabilities of social organisms like ants and bees. One of the methods for constructing SRS control systems is the evolutionary robotics (ER) approach. This approach often deals with homogeneous SRS, i.e., a swarm in which all robots have the same artificial neural network (ANN) controller. However, our study seeks to generate swarm behavior using robots equipped with heterogeneous ANN controllers to explore behaviors within a swarm. In this study, the ER approach is a method that automatically designs controller ANN parameters. Specifically, we delve into the evolutionary process and swarm behavior during the cooperative exploration task. Our findings reveal that by reducing the size of the hidden layer of ANN, we observe outcomes demonstrating that the heterogeneous SRS with a smaller hidden layer performs better than the homogeneous SRS. It is important to note that the robots in both cases are the same in terms of hardware. It has been discovered that an ANN topology is crucial for enhancing evolvability; however, the systematic design technique has not yet been discovered.

Keywords: System Engineering, Intelligent Systems, Information Management Systems
Abstract: Reliable development of next-generation data analytics toolboxes (N-GDATs) requires robust underpinning theories, which cannot necessarily be inductively generated. Based on the axiomatic theories fusion (ATF) methodology we deductively developed a comprehensive theory supporting the development of a N-GDAT for white goods design based on middle-of-life data. Accordingly, theories about designer’s needs, advanced technologies, data analytics, creative problem- solving, decision-making, and interoperability were fused following the ATF steps: (i) selection of component theories, (ii) axiomatic discretization of foundational theories, (iii) establishing relationships among axioms and postulates, (iv) transcription of system of axiomatic propositions into a textual format, and (v) validation of explanatory theory. The obtained new theory provides a robust basis for the targeted knowledge platform. It provides (i) decision-making, (ii) algorithmic concepts, (iii) learning, (iv) data management, (v) interfacing, (vi) reasoning, (vii) data types and characteristics, (viii) design issues, (ix) analytics techniques and methods, and (x) outputs requirements to develop N-GDATs.

Keywords: System Engineering, Remote Sensing, Innovative Systems Approach for Realizing Smarter World
Abstract: This paper reports on a method to classify hand movement patterns that is input for a 3D capacitive proximity sensor unit with multiple sensing electrodes on the basis of machine learning models.The results show that hand movements in eight directions and two levels of movement speed can be classified with high accuracy, particularly with Support Vector Classification (SVC) that achieves an accuracy of approximately 96%.

Keywords: System Engineering, Robotic and Automation Systems, Adaptive and Optimal Control
Abstract: Various algorithms and risk assessments to prevent ship collision caused by human error and the operation of autonomous vessels have been researched. Most of the algorithms in previous studies are methods of avoiding risk areas in proximity in the presence of multiple vessels and obstacles. In this study, we aim to build a support system for large vessels navigating in the ocean to avoid a target ship within a sufficient distance from that ship economically while maintaining a designated safe distance. To evaluate the optimal distance, the propeller fluid force was estimated with the maneuvering motion models of the three types: a bulk carrier, container ship, and crude oil carrier by searching a route using A-star algorithm. Due to simulations, when a target ship approached 17 knots and was in a head-on position, the optimal distance is 18 nautical miles (NM) for the bulk carrier and 16 and 18 NM or longer for the other two ships, depending on the limit of detection by a RADAR.

Keywords: Human Interfaces
Abstract: Law violations by pedestrians are a major cause of traffic accidents. To prevent accidents while walking, it is important for pedestrians to be aware that they are approaching dangerous areas (crossroads, crosswalks, bends). The purpose of this study is to clarify how to change pedestrian behavior by directly applying physical stimulation to somatosensory sensations. We propose a method to develop a shoe-type device that supports walking during normal walking, but gradually reduce support before the stop position, and then stop at the stopping position. In order to achieve the proposed method, we employ a mechanism with springs and cams for making the shoe-type device. Through the experiment, we evaluated the effect of wearing the proposed shoes, and then confirmed that it was possible to change walking behavior intuitively.

Keywords: Human Interfaces, Information Management Systems, Computer Aided Design
Abstract: Abstract: The objective of this paper is to develop an Optimized Scheduling Decision and Control System (OSDCS) for edible oil pipeline based on a path planning algorithm to assist in decision making. The OSDCS will assist managers in issuing decision commands to solve the problem of incomplete task scheduling and planning in edible oil factory production. The OSDCS utilizes both A* and Dijkstra’s algorithms to realize optimal path planning. At the same time, through the friendly 3D visualization technology, it simulates a 3-dimensional environment to present the specific production line of the edible oil pipeline. Then, the operator can check the situation of the edible oil pipelines by this human-machine interface of the well development OSDCS. This paper uses the C# programming language to integrate path planning algorithms and assist in task scheduling decisions. The decision results are simulated using computer-aided design (CAD) models to replicate the actual edible oil transfer. The OSDCS can reduce human intervention and increase production efficiency.

Keywords: Optimized Scheduling Decision and Control System (OSDCS), Edible Oil Pipeline, A*, Dijkstra’s, CAD

Keywords: Virtual Reality Systems, Innovative Systems Approach for Realizing Smarter World, Entertainment Systems
Abstract: Live concerts that synthesize three-dimensional images with performers and real environments still leaves much room for improvement. In this paper, we propose MRlive, a system that combines instance segmentation and Mixed Reality (MR) technology to provide viewers with a performance that synthesizes Virtual Reality (VR) with arbitrary objects in real space. The evaluation experiments on the prototypes revealed several improvements. At this point, the system achieved the fastest operation time of 55.99 ms/frame and demonstrated the provision of MR video experience through object extraction and VR synthesis.

Keywords: System Engineering, Agricultural and Bio-Systems, Signal and/or Image Processing
Abstract: The purpose of this study is to do experiments to capture swimming behavior of a small school of fish and to evaluate quantitative differences in swimming patterns by many types of environment. However, using that the conventional method of manually extraction of the swimming trajectory of each individual fish in which the processing and time costs are to be increasing. Here, we try to building up the automatically tracking method for the swimming trajectories based on the fish body detection. This algorithm is consisted of deep learning model for small size fish schooling in a water tank. The results show that our automatic tracking method can extracts the schooling trajectories under the strict conditions and some kinds of measurement parameters of the complexity of fish schooling behavior.

Keywords: Medical and Welfare Systems, Human Interfaces, Virtual Reality Systems
Abstract: "Going where you want to go when you want to go" is one of the most important activities for quality of life in an aging society. Personal vehicles (PVs) are electric mobility devices that extend the range of activities for older people, both indoors and outdoors. Due to the complex traffic environment and rules of PVs, it is difficult to drive fully autonomously like a car, and it is necessary to combine automatic operation with manual operation. Therefore, a visual assistance system using MR devices has been proposed to reduce manual operation errors. However, being accustomed to the visual assistance system through training does not eliminate errors. For elderly people with declining cognitive and motor abilities, it is not safe to operate PVs by operating a joystick while looking at the environment. Brain-computer interfaces (BCIs), which use brain signals for human-machine interaction, have been investigated. A steady state visual evoked potential (SSVEP), which detects the user's intention, is attracting attention as a non-invasive BCI. The SSVEP has been studied as a method for controlling wheelchairs. In this paper, an automatic driving system with an MR is proposed that detects the intention of the PV user using the SSVEP. As an experimental result, the PV drives smoothly along the user's desired path without stopping.

Keywords: Medical and Welfare Systems, Identification and Estimation, Analytical Measurement
Abstract: Patients with stroke may have significant asymmetry due to paralysis or other adverse effects. Rehabilitation therapy has been used to improve asymmetry, and several orthotics have been developed. However, it is difficult to accurately evaluate the improvement in asymmetry resulting from these efforts. Although ankle stiffness evaluation in the center of mass feedback balance control is desirable, differences in static vertical forces between the two feet affect stiffness, as reported in previous studies. The purpose of this study was to evaluate the asymmetry of left and right ankle joint stiffness, excluding the vertical load difference between the two feet. We measured the response of a subject standing on a cart swaying back and forth horizontally with a small amplitude, using force sensors on the soles of the feet. We identified individual balance models to satisfy the assumed feedback model, and derived proportional gains for both ankles using the identified parameters. The experiments were performed on 16 young healthy subjects, and it was confirmed that the asymmetry of the ankle joint stiffness could be accurately evaluated. This method could contribute to the evaluation of asymmetry of the left-right ankle joint response in the medical field.

Keywords: Medical and Welfare Systems, Integrative Biophysical Engineering and Informatics, Biological and Physiological Engineering
Abstract: Sensory attenuation, which is the brain's mechanism to differentiate self-generated actions from external ones, identifies self from non-self, and is impaired in disorders such as schizophrenia and Parkinson's disease. This impairment can result in hallucinations and delusions owing to the misattribution of self-produced stimuli as external stimuli. Considering the limitations of traditional diagnostics in detecting these perceptual changes, sensory attenuation is a promising assessment and intervention tool. This study examined the effect of auditory and visual feedback on sensory attenuation in healthy adults, proposing its enhancement as a rehabilitative strategy for psychiatric disorders. Through experiments, we assessed the impact of feedback on sensory attenuation and its relationship with psychological state. Auditory feedback was found to significantly influence sensory attenuation. However, combined feedback and long-term effects were not observed. The link between sensory attenuation and psychological indicators suggests the potential for targeted modulation in individuals with schizophrenia-like mental states. This study highlights the potential of feedback-based modulation of sensory attenuation in the development of rehabilitation techniques for neuropsychiatric conditions, and sets the stage for further research on its long-term application in clinical settings.

Keywords: Medical and Welfare Systems, Integrative Biophysical Engineering and Informatics, Components and Devices
Abstract: In order to promote rehabilitation of patients with lower leg fractures, which are often difficult to treat in many cases, we developed a mechanism for an unloading device equipped with a function that automatically adjusts the unloading rate. Experiments were conducted using this mechanism to measure changes in the height of the board while varying the amount of load applied, and the proposed mechanism was confirmed to provide correct unloading.

Keywords: Medical and Welfare Systems, Integrative Biophysical Engineering and Informatics, Human Interfaces
Abstract: The objective of this study is to develop a human simulator that can reproduce human physiological reactions. While the national medical examination is a prerequisite for becoming a doctor, it does not include a practical skills test. Consequently, the development of a simulator for medical practice is being conducted to address the lack of technical skills resulting from a lack of practical training experience. The objective of this study is to develop a system that can reproduce human body temperature fluctuations by improving the mechanism and accuracy of reproducing body temperature, which is one of the vital signs of humans.

Keywords: Medical and Welfare Systems, Mechatronics Systems, Robotic and Automation Systems
Abstract: Disuse syndrome refers to various declines in mental and physical functions caused by prolonged periods of rest. Disuse syndrome is often seen in elderly people and people who are bedridden due to cerebral infarction, etc., and as it progresses, it can affect life prognosis due to a decline in the will to live. Therefore, in this study, we focus on wheelchairs with reclining and lifting functions and propose a mechanism that will lead to the prevention of disuse syndrome. Using multibody dynamics, we develop a user-friendly electric reclining wheelchair that has a reclining mechanism that reduces slippage and a lift mechanism that allows the seat to be raised, lowered, tilted forward, and tilted back.

Keywords: Medical and Welfare Systems, Sensors and Transducers, Human Interfaces
Abstract: Evaluating stress daily is crucial for maintaining a healthy lifestyle. In this paper, we introduce a straightforward approach for daily stress assessment using a tactile sensor sheet placed on a bed. This method extracts heartbeat data from pressure time series recorded by the sensor. The sensor is thin, flexible, and does not interfere with sleep in everyday life. We also present an experiment comparing the results of our proposed method with those obtained from an ECG.

Keywords: Medical and Welfare Systems, Virtual Reality Systems, Entertainment Systems
Abstract: We have evaluated motivation by measuring electroencephalograms (EEGs) in previous studies, but we can not measure motivation directly. There is literature on the relationship between motivation and posture, and focusing on praise and posture, we used electromyography (EMG) as a new indicator to evaluate motivation. In the result, motivation effects average, standard deviation and coefficient of variation of EMGs. Thus, evaluating motivation by measuring EMG can be expected.

Keywords: Virtual Reality Systems, Medical and Welfare Systems, Ubiquitous Healthcare
Abstract: As people age, their ability to balance declines. This leads to an increased risk of falls, which often results in loss of independence due to the need for nursing care. The earlier we are able to detect this decline and appropriately assess the risk, the better we will be able to mitigate the risk of falling. To date, however, visual control of balance is often only assessed under two conditions: with the eyes open and closed. Understanding how human postural balance changes when more complex visual stimuli are applied can provide a more detailed picture of a person's balance capability. This study aims to develop a fall risk assessment system for the elderly using visual stimuli as a new indicator for balance assessment. To this end, the effects of various parameters of visual stimuli on postural control using a VR headset were investigated. The results showed that when oscillating a virtual scene, the mediolateral oscillations tended to have less influence than the oscillations in the other direction, while the increasing frequency of the oscillations resulted in more postural sway in the anteroposterior direction. However, increasing amplitude appears to increase postural sway in both the anteroposterior and mediolateral directions. Our results suggest that scene oscillation in a VR headset may be able to provide a more detailed understanding of a person's balance capability; however, more work is required to verify these effects.

Keywords: Robotic and Automation Systems
Abstract: This paper proposes the greedy algorithm for the optimization of excavation process with the automation of the pneumatic caisson method. In this paper, the pneumatic caisson method is replaced by a soil transport problem, and the excavator operation is optimized to minimize the time required to remove the soil from the workspace to aboveground. The excavatable area is divided into excavation cells in a grid-like pattern, then the volume of soil in each excavation cell, the distance from the excavation cell to the earth bucket, and the available capacity of the earth bucket are used to calculate the cost of each excavation cell. Depending on whether the earth bucket is installed in the workspace, the best excavation cell is selected by the greedy algorithm based on the cost of each. To confirm the effectiveness of the proposed method, we compare the operation of the proposed method with that of the actual construction.

Keywords: Robotic and Automation Systems
Abstract: This paper presents the development of a force feedback algorithm for remote control of the heavy-duty manipulators of the ITER blanket remote handling system (BRHS). In the process of inserting the gripping fingers into a first wall (FW) panel during the FW maintenance campaign, the deflection of the remote handling system caused by the weight of the manipulator and use of the radiation-hardened sensors in a high-radiation environment pose technical challenges in terms of available control methods. We approach these challenges by implementing a force feedback algorithm in the control system for the FW gripper having two gripping fingers. In this 2-peg-in-hole system, we installed a 5-axis force torque sensor at the base of each gripping finger for precise force detection. The sensor signals are linked with 6-degrees-of-freedom movement commands, which are based on the tool center point (TCP) at the master gripping finger. Movements to avoid excessive loading include rotation and Cartesian movement. We implemented the force feedback algorithm in an integrated control PC that operates an industrial robot. Using this approach, the gripping fingers were successfully inserted without excessive loading. Verifications to further optimize the force feedback algorithm are planned with our test equipment. The implementation of the force feedback algorithm in successful trials is an important step towards using the algorithm for the BRHS.

Keywords: Robotic and Automation Systems, Intelligent Systems
Abstract: This study proposes a deep learning-based manipulation method for dense objects. In the dense objects, the objects in direct contact with a robot as well as the other objects move variously and complexly via the interaction between objects. A deep learning-based prediction model for such dense objects' behaviors was developed. Using the prediction model, a robot motion planning method was constructed to realize desired distribution of dense objects. The proposed prediction model employs a latent vector extraction by Variational Autoencoder (VAE) for expressing the global state of dense objects and a unit type architecture with Long Short-Term Memory (LSTM) for a long-term prediction. Owing to the above two features, the proposed prediction model has an advantage in the prediction of global distribution transitions with respect to long-term robot motions. Finally, the proposed method was experimentally validated. After confirming the performance of the prediction model, the manipulation tasks for realizing desired distributions were demonstrated.

Keywords: Robotic and Automation Systems, Intelligent Systems, Signal and/or Image Processing
Abstract: A mechano-optical material-based tactile sensor was fabricated using a strain-sensing polymer, which is a polymer material that changes its optical reflection characteristics depending on the magnitude of strain. The pressure distribution on the sensor was estimated with high spatial resolution by converting the hue of the output image into pressure. The estimated pressure distribution was used for grasping control of objects of various shapes by the robot hand.

Keywords: Robotic and Automation Systems, Nonlinear Control, Adaptive and Optimal Control
Abstract: This paper addresses the scenario of tracking a moving target by drones. By employing a so-called persistent coverage control strategy, drones efficiently search and monitor the target in the field. Expanding upon previous work utilizing the concept of constraint-based control to keep the target within the field of view of the drone, this research extends the approach by dynamically adjusting drone's altitude and adapting the object detection model based on the moving target's velocity. Experimental validation demonstrates the superior efficacy of the proposed algorithm in tracking a moving target compared to its predecessor, highlighting its potential for enhanced performance in dynamic target tracking scenarios.

Keywords: Robotic and Automation Systems, Signal and/or Image Processing, Identification and Estimation
Abstract: Abstract: Monitoring of robot manipulators in factories is an indispensable task to ensure their proper and precise operations, because unexpected vibrations in robots may lead to decrease in production efficiency and quality with significant financial loss, and become a risk factor in incidents that threaten the safety of workers in factories. Robot vibration analysis has been conventionally conducted using multiple contact sensors and has limitations of the number of measurement points and installation costs. High-frame-rate (HFR) cameras with subpixel video analysis at hundreds or thousands of fps enable audio-frequency-level vibration measurement at many points without any contact. This study conducted HFR-video- based multi-point vibration analysis for a multi-jointed robot manipulator in a vibration test sweeping a sine wave from 1 Hz to 100 Hz, and spatially quantified its vibration property that involves different amplitudes and phases at each robot part by analyzing frequency responses of sub-pixel displacements at multiple points, which were estimated from 500-fps video sequences with digital image correlation.

Keywords: Robotic and Automation Systems, Mechanical Systems Control, Intelligent Systems
Abstract: In this paper, we present the development of a new SLAM (Simultaneous Localization and Mapping) algorithm tailored for differential drive robots equipped with 3D LiDAR. Conventional SLAM algorithms typically utilize scan matching techniques such as ICP (Iterative Closest Point), which determines point-to-point correspondences, or NDT (Normal Distributions Transform), which assesses point-to-distribution correspondences. These methods, however, necessitate iterative optimization that can converge to local optima, resulting in inaccurate position and orientation estimations. To address these limitations, we introduce a new SLAM algorithm that capitalizes on the movement characteristics of differential drive robots, specifically their capability for in-situ rotation and linear movement. By applying these movements sequentially, our approach circumvents the challenges associated with ICP and NDT matching, transforming the problem into one of linear estimation. This method facilitates rapid and stable determination of position and orientation, significantly reducing computational costs and improving the robustness of map construction within the SLAM navigation. We validate the fundamental principles of our proposed algorithm through simulations with a 3D robot simulator platform.

Keywords: Robotic and Automation Systems, Human Interfaces, Mechatronics Systems
Abstract: A cable-driven spherical parallel mechanism (CDSPM) is a 3-degrees-of-freedom (3-DOF) mechanism driven by four cables. The mechanism can be considered as one of the cable-driven parallel mechanisms (CDPMs). CDSPMs have been utilized for shoulder and wrist joints of anthropomorphic manipulators because of their flexibility. On the other hand, a versatile approach to its complex kinematics has not been established. This paper analyzes the kinematics of CDSPM and derives a numerical solution using the Newton-Raphson method. Experimental results confirm that the orientational motion with real-time numerical computation has succeeded.

Keywords: Robust Control, Robotic and Automation Systems, Nonlinear Control
Abstract: This paper presents a robust control algorithm using integral sliding surface (ISS)-based sliding mode control (ISMC) and sliding perturbation observer (SPO). ISMC eliminates the reaching phase by enforcing the sliding mode on the entire system response. Consequently, robustness is guaranteed from the initial time instance by compensating the matched uncertainties. Furthermore, SPO estimates the system perturbation that includes unmatched uncertainties, nonlinearities, and external disturbances and eliminates their effect on the system response. The efficiency of the proposed controller is validated through the simulation environment of a linear transfer function and a two-axis robot manipulator. The comparison results of PID, SMC, and ISMC on the transfer function show that ISMC outperforms PID and SMC in terms of easy parameter selection, better performance, and reduced chattering effect. Whereas the results of the two-axis robot with nonlinear dynamics exhibit the superiority of ISCMSPO as compared to ISMC by compensating for the perturbation and ensuring robust performance.

Keywords: Autonomous Decentralized Systems, Robotic and Automation Systems
Abstract: In a multi-robot system, robots receive information from their own sensors as well as from other robots. By combining this information, they can enhance their environmental perception, however, the challenge lies in how to combine unknown types of information obtained by other robots. The purpose of our study is to propose a system that unifies information exchange among multiple robots into natural language and leverages foundation models to appropriately combine and process information according to the instructed actions. We report on our investigation into resolving the ambiguity influenced by the embodiment existing between natural language and numerical values through fuzzy inference based on human-designed propositions and membership functions, as well as fuzzy rules created by foundation models

Keywords: Autonomous Decentralized Systems, Robotic and Automation Systems
Abstract: In open environments, systems where multiple robots collaborate to perform tasks are anticipated. The authors have previously demonstrated that utilizing foundation models for robot's decision-making enables cooperative adaptation to unexpected events. However, we also confirmed that relying solely on the camera images mounted on the robots lacks the 3D spatial awareness necessary to make an inference about where obstacles should be removed. In this paper, we employ a visual prompting method to enhance the foundation models' capability to perform 3D spatial tasks. Our approach involves creating a 3D map of the robot's surroundings and annotating multiple images from various viewpoints with coordinate axes and scales. We evaluate whether the foundation models can make more appropriate decisions by understanding the positional relationships and relative sizes between the robot and surrounding objects.

Keywords: Biological and Physiological Engineering
Abstract: In this study, we consider linear chemical reaction networks (LCRNs) with time delays. Here, all complexes, which corresponds to a reactant and a product of each reaction, in the LCRN consists of a single chemical species. The LCRN is represented by a weighted directed graph, in which each node and directed edge represent a chemical species and a reaction, respectively. We try to analyze the stability of a linear delay differential equation (LDDE) describing the time evolution of concentrations of species in the LCRN. By decomposing the LCRN into some strongly connected components (SCCs), we show that the number of zero-eigenvalues of the characteristic equation derived from the LDDEs is equal to the number of terminal SCCs in the LCRN, and the real part of all other eigenvalues is negative, irrespective of time delays.

Keywords: Biological and Physiological Engineering
Abstract: Straight arm press to handstand is a basic technique in gymnastics. It requires a large postural change while keeping balance with the center of pressure under the palms. The purpose of this study was to construct a motor control model of this movement. The body was modeled as a rigid three-link mechanism with a hand joint, shoulder joint, and hip joint. The straight arm press to handstand was assumed as a movement from an initial static posture to a handstand posture, and the control objectives were to achieve inter-joint coordination represented as linear combinations of the three joint angles and the position of the center of mass, and so on, based on measured data of gymnasts. Simulation results show that the model can generate a motion qualitatively similar to the straight arm press to handstand performed by gymnasts.

Keywords: Biological and Physiological Engineering, Autonomous Decentralized Systems
Abstract: Insects adaptively alter their gait according to their environment, enabling them to move on both ground and water. The control of leg movements for such adaptive gait is significantly influenced by the exchange of information between motor and sensory signals in the three thoracic ganglia. In this study, to understand how the information exchange between thoracic ganglia influences gait pattern generation, we measured the gait patterns of crickets (Gryllus bimaculatus DeGeer) after surgically disconnecting their thoracic ganglia using a floating ball-type gait measurement system. We compared the speed of leg movement, the phase difference of inter-legs, the range of leg motion, and the direction of movement during walking between these crickets and intact ones to investigate the characteristics of gaits with surgically disconnected thoracic ganglia. Based on these features, we discuss the role of information exchange between the thoracic ganglia in generating gait patterns.

Keywords: Biological and Physiological Engineering, Identification and Estimation, Electrophysiology and Kinesiology
Abstract: During the acceleration phase in sprinting, it is necessary to obtain a large acceleration with control of pitch angle of the body. However, because the definition of pitch angle is unclear, the relationship between the foot contact position, body acceleration, and pitch angle has not been discussed. The purpose of this study is to derive the pitch angle of the body in the sprinting motion from the angular momentum in the sagittal plane and to clarify the relationship with the false step. The motion was measured by optical motion capture to estimate the angular momentum in the sagittal plane. The pitch angle was obtained by integrating the angular velocity obtained from the angular momentum divided by the moment of inertia. Comparing the pitch angle to the theoretical angle obtained from the equilibrium between gravity and inertial force, the pitch angle was close to the theoretical angle from the second step. This result suggests that sprinters may maintain a pitch angle that makes it easier to control their posture after the second step in the sprinting motion.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics, Electrophysiology and Kinesiology
Abstract: The responsive neurostimulation (RNS) involves the placement of intracranial surface electrodes, which monitor electrical neural activities for detecting seizures, in drug-resistant epilepsy patients. Because the hardware resource for signal processing in the RNS implant is limited, the number of electrodes connected to the implant is limited to a few to several channels even in a state-of-the-art device. In the previous study, we proposed a systematic method of selecting a few to several electrodes that are most useful for detecting seizures from the electro-cortico-gram (ECoG) electrode array by utilizing a three-dimensional convolutional neural network (3dCNN). In the present, the inference performance was compared between the 3dCNN model, which was trained with the recording data from all 24-64 channels in the ECoG electrode array, and a 2-dimensional CNN (2dCNN) model, which was trained with the data from the four electrode channels selected by our proposed method. The seizure detection accuracy of the 2dCNN model was comparable to that of the 3dCNN model for all five patients we tested. This suggested that our previously proposed method can be useful for systematically reducing the hardware resources required for deploying the seizure detection algorithm based on the CNN model.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics, Electrophysiology and Kinesiology
Abstract: A recent clinical study has verified the feasibility of intra-cortical visual prostheses, in which electrical microstimulation applied to the primary visual cortex (V1) of the cerebrum can evoke spatially-localized light perception, called phosphene, in a blind human subject. The stimulation is to deliver the pulsed current from the tip of penetrating electrodes to extracellular space inside the tissue, eliciting transient changes of extracellular potential, and thus, of cellular membrane voltage near the electrode tip. Hence, the stimulus activates, in a non-specific manner, not only the somata or the axon initial segments (AIS) but also the passing axons of which somata/AIS is located away from the electrode tip. This might evoke phosphene in unintentional locations that deviate from the visuotopic coordinates. Since those different neuronal compartments are considered to exhibit different excitabilities in response to the extracellular potential change, we hypothesize that, by applying current pulses with subthreshold amplitude prior to a main suprathreshold current pulse, either of the compartments can be primed to be less excitable upon the main stimulus pulse. In this study, by the voltage-sensitive dye imaging, we showed that a preceding kilohertz-frequency pulse train lowered the following stimulus-elicited neural activation in V1 of mouse brain slice preparations.

Keywords: Biological and Physiological Engineering, Integrative Biophysical Engineering and Informatics, Electrophysiology and Kinesiology
Abstract: A recent clinical study in patients with acquired blindness has verified the feasibility of intra-cortical visual prostheses, which bypass retinal functions in the eyes and utilize the electrical microstimulation to directly drive neural circuits in the cerebral primary visual cortex (V1) to evoke artificial light perception called phosphene. In such clinical as well as psychophysical studies in humans or non-human primates, the perceivable phosphene was evoked by delivering the stimulus current pulses repetitively at a constant rate of 100-200 Hz for more than a few hundred milliseconds. On the other hand, our previous physiological experiments with the voltage-sensitive dye imaging in the mouse brain slices demonstrated that the single microstimulation pulse triggers a synchronized firing of the action potentials in neurons near the stimulation site in V1. This thereby enabled us to control the temporal pattern of neural population activities using the microstimulation pulses delivered in neuromorphic spike-timing sequences. In the present study, we analyzed cross-correlation functions between the light stimuli used to elicit/simulate the retinal spikes and the cortical neural responses to the microstimulation based on those retinal/retinomorphic spikes. The results appeared to suggest that the temporal feature of light stimuli encoded into the retinal spikes could be decoded as the microstimulation-elicited neural activities in V1.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used to solve expensive multi-objective optimization problems (EMOPs), but most algorithms are designed for low-dimensional problems. In the present work, we propose an extension of the existing SAEA, i.e., SFA/DE, to improve its scalability against high-dimensional problems. Our main idea is to identify and intensively explore decision subspaces, where good solutions are likely to exist. To this end, we integrate a rough set theory into the SFA/DE framework, where common patterns of good solutions, i.e., decision subspaces, are estimated using the rough set theory. Experimental results on EMOPs in their high-dimensional settings show that the proposed SAEA successfully improves the performance of SFA/DE.

Keywords: Computational Intelligence, Intelligent Systems, System Engineering
Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) are popular approaches for solving expensive multi-objective optimization problems (EMOPs). To effectively solve EMOPs, utilizing prior knowledge, i.e., a set of pre-evaluated solutions, is crucial because it significantly affects the search performance. However, certain successful SAEAs, such as MCEA/D and SFA/DE, are not designed to effectively use pre-evaluated solutions. In the presented work, we introduce an effective utilization method of pre-evaluated solutions and integrate it into MCEA/D and SFA/DE. Experimental results show that our utilization method significantly improves the performance of MCEA/D and SFA/DE. Moreover, we empirically compare the proposed approach with alternative utilization approaches.

Keywords: Human-Machine Systems, Human Interfaces
Abstract: Market expansion of Internet advertising has led to increased interest in how to attract more viewers to advertisements and achieve conversion goals. Traditionally, banner advertisements have been extensively utilized within online content. However, in recent years, there has become a widespread use of in-feed ads seamlessly integrated with the content. In-feed ads are suited for use in a tiled layout where contents are arranged in a grid format. In a tiled layout, advertisements can naturally blend in and be flexibly positioned alongside the main content. Instrumented data were segmented by position indicating where an observer looked at, including advertisements, visual search targets, and other elements. As the results of comparison, it found that both the duration and frequency of fixations were highest on searching targets and lowest on advertisements.　It indicates that observers could detect advertisements in the peripheral field of vision　and possibly inhibit gaze to them. Therefore, we conclude that to attract more viewers to advertisements and achieve conversion goals, we should use advertisements that do not stand out but blend in more naturally and have internal appeal.

Keywords: Integrative Biophysical Engineering and Informatics, Biological and Physiological Engineering, Electrophysiology and Kinesiology
Abstract: The vertebrate retinas encode various information in the visual fields into spatiotemporal patterns of neuronal spikes. To capture/explain the dynamics of the information encoding, previous computational studies employed the cascades of spatiotemporal linear filtering and static nonlinear scaling (‘L-N cascade’) in combination with spiking models. To simulate the spike firing, the stochastic Poisson process or the leaky integrate-and-fire model has been widely used, although either of those models has shortages in the spike timing precision and/or the spike count reliability when compared with the intrinsic mechanism of spike generation in biological retinal ganglion cells. These could be resolved by inserting the spike-dependent linear feedback filtering in the model, which improves the spike timing precision, lowers the variance-to-mean ratio of spike counts in a time window, and hence increases the encoded information per spike. However, little has been known if such a difference in the spiking model affects the decodability of the spike sequences into natural images. Therefore, by utilizing pre-trained three-dimensional convolutional neural networks as the decoders of retinomorphic spatiotemporal spikes, we compared the decoding accuracy of the spike sequences which are computed with an L-N cascade followed by different spiking models.

Keywords: Intelligent Systems, Computational Intelligence, Transportation Systems
Abstract: In this study, we analyze the factors causing localization errors in autonomous driving across various environments. Scan Matching, a self-localization method that utilizes high-precision three-dimensional point cloud maps and LiDAR point clouds, is gaining attention in autonomous driving. However, it is known that the accuracy of self-localization significantly deteriorates and can even fail in environments with few distinctive features or high similarity. To prevent failures in Scan Matching, it is crucial to understand the characteristics and similarities of the surrounding environment before autonomous driving in such areas. Previous research [1] focused on three-dimensional point cloud maps before autonomous driving, proposed various error factor indicators, and confirmed their correlation with localization errors. However, these studies only validated Scan Matching in favorable conditions, and while correlations were confirmed, the specific conditions under which localization errors increase were not clarified. Therefore, in this study, we analyze the factors causing localization errors in autonomous driving across various environments. Our analysis results indicate that the error factor indicators proposed in previous research [1] can quantitatively determine the feasibility of localization based on their magnitudes. [1]E. Javanmardi, et al. : “Factors to Evaluate Capability of Map for Vehicle Localization”

Keywords: Opto-Electronic Measurement, Mechanical Measurement
Abstract: The flexibly transparent substrates have been widely adopted as substrate materials in flexible relating applications such as displays, energy, biomedicine, etc. Since the aforementioned products may be subjected to severely mechanical loading to result in large deformation, it is an important issue to develop a measurement technique to measure their mechanical response subjected to external loading. The published works mainly focused on the deformation measurement of flexibly transparent substrate while the static mechanical and/or thermal loadings are exerted. Seldom research was paid the efforts on their vibration behaviors characterization but the flexible relating application does experience the dynamic loading. Therefore, this study aims to use an optical interferometry, namely electronic speckle pattern interferometry (ESPI), to investigate the vibration behaviors of flexibly transparent substrates. In this study, the vibration behaviors of polyethylene terephthalate (PET) substrate were measured by ESPI. The out-of-plane optical arrangement was established to simultaneously measure resonant frequencies and their corresponding mode shapes. In spite of high transparence of PET substrate, the experimentally obtained result show ESPI provides an appropriate measurement employed to characterize the full-field vibration behaviors of PET substrates.

Keywords: Remote Sensing, Sensors and Transducers, Networked Sensor System
Abstract: The measurement of sea water temperature is crucial for building a sustainable future for the global environment. The authors are constructing a citizen-participatory monitoring system to increase public interest in atmospheric carbon dioxide, particularly among young people, by visualising atmospheric carbon dioxide concentrations using a simple sensor network and virtual space at a non-profit organisation that examines global warming. In addition, a wireless sensor node using LoRa and LoRaWAN, a type of LPWA, was created, and experiments were conducted on remote continuous acquisition of sea water temperature data and other data, as well as on the visualisation of the data. The experiment used the Research Institute of Marine Bioresources, Fukuyama University, Innoshima, Onomichi City, Hiroshima Prefecture, as the base station, and continuously acquired data for five days at five-minute intervals from a group of sensors connected to a child unit installed on a floating pier at Ohama Port, located 560 m on the coast at a linear distance from there. Although the parent and child units were positioned out of sight, measurements were made without any data loss. It is hoped that correlative information based on various sensor data will be obtained.

Keywords: Sensors and Transducers, Identification and Estimation, Mechatronics Systems
Abstract: This presentation reports on the theory and experimental results of a shape sensor, the Flexible Sensor Tube (FST), which combines potentiometers and Inertial Measurement Units (IMUs). The FST has a serial link structure consisting of multiple links connected by 1-DoF passive rotary joints. Each joint contains a potentiometer to measure the rotation angle. Previously developed FSTs used these measured joint angles to solve forward kinematics, thereby obtaining the positions of all link endpoints and reconstructing the overall shape of the FST. However, there is a problem of position errors at the endpoints due to measurement inaccuracies of the potentiometers. These inaccuracies lead to accumulated errors in the link orientations, since the angle measured by the preceding link is used to determine the orientation. To address this problem, we propose a method to accurately estimate the joint angles of the FST by performing sensor fusion on the measurements from the potentiometers and IMUs. Since IMUs measure the orientation angles relative to gravity, they can provide the link orientations without the accumulation of measurement errors. In this presentation, we will demonstrate the high-precision reconstruction of the FST's overall shape by applying forward kinematics to the estimated joint angles, and showcase the operational performance of the system.

Keywords: Sensors and Transducers, Signal and/or Image Processing, Identification and Estimation
Abstract: In this research, we propose non-destructive method to detect fake batteries. Due to global regulation on carbon dioxide emission, use of electric vehicles (EVs) is promoted instead of gasoline automobile. Accordingly, the demand for lithium-ion batteries (LiBs) are rapidly increasing because LiBs are energy source of EVs. However, while LiBs with high capacity is demanding for EVs, there is a risk that fake batteries are manufactured and spread in the market because genuine LiBs with high density of power contain rare materials and the price tends to be high. Indeed, fake LiBs already exist among consumer products and are widely sold online. Therefore, we assume similar problem may occur in EVs and anti-counterfeiting measures for LiBs is pressing challenge to ensure safety. The research focuses on magnetic flux density generated from a LiB which is individually different due to assembly error in internal structure. As fundamental study, we developed sensing device to measure magnetic flux density around LiBs to see if a battery generates unique magnetic flux density. Throughout the experiment, we found that measured magnetic flux density varies according to batteries and this principle may be used to detect fake batteries.

Keywords: Signal and/or Image Processing
Abstract: We deal with subspace clustering for matrix completion. A method based on mathematical optimization for subspace clustering-based matrix completion has proposed. This method assumes that the subspaces spanned by the data within each cluster are low-dimensional, and the paper has proposed a mathematical optimization problem to partition the given matrix appropriately so that the resulting submatrices are low-rank. However, this method tends to depend on initial values, and there are cases where the optimal solution cannot be obtained depending on the initial values. Additionally, this subspace clustering method is weak against the noise, the method is unsuitable simultaneously performing subspace clustering and matrix completion. Therefore, we propose a method reducing the dependency on initial values and improving robustness against noise. Numerical simulations demonstrate that the proposed method achieves high accuracy for subspace clustering and completion compared to the conventional method.

Keywords: Signal and/or Image Processing, Components and Devices, Identification and Estimation
Abstract: Recently, omnidirectional cameras have attracted attention as indoor/outdoor surveillance devices and robot vision because of their 360-degree field of view. In this paper, we describe the design of a monocular stereo omnidirectional camera with a cut hemispherical mirror superimposed on the camera optical axis. A ray tracing method and a look-up-table-based method are used for depth estimation, and the results are evaluated in experiments with a real camera and a simulator.

Keywords: Medical and Welfare Systems, Sensors and Transducers, Identification and Estimation
Abstract: Caregivers experience lower back pains due to patient transfer. Foot position adjustment is known as useful to reduce lumbar loads. Thus, we have been developing wearable monitoring system using inertial sensor of smartphone to guide suitable foot position for preventing lower back pains. The proposed method recognized foot positions by trunk inertial data (acceleration, gyro, and magnetic) and machine learning. However, feature selection for machine learning were not sufficiently investigated. Feature selection will contribute to develop simple foot position recognition for implementing smartphone application. The objective of this study was to explore effective features for foot position recognition using inertial data during patient transfer. Effective features were explored via if-then rules generated by decision tree. The proposed method using decision tree recognized anteroposterior (AP) and mediolateral (ML) foot positions during patient transfer via cross-validation for 100 trials performed by 10 participants. The results showed that decision tree using inertial data recognized AP and ML foot positions with greater than 0.9 accuracy. Furthermore, if-then rules of decision tree indicated that gyro of pitch rotation and 3-axis magnetic values were effective in recognition. These results suggest that these features are useful for simple and accurate foot position recognition during patient transfer.

Keywords: Signal and/or Image Processing, Intelligent Systems
Abstract: In recent years, visual simultaneous localization and mapping (vSLAM) has become popular for use in robotics, autonomous vehicles, and augmented reality. However, vSLAM often requires high memory usage to achieve accuracy, which limits the operational duration of robots. One of the potential solutions to this problem is image quantization. This research evaluates the performance of a state-of-the-art vSLAM system called ORB-SLAM3 using quantized images created by three quantization methods. We focused on the detailed components of ORB-SLAM3, including tracking, local mapping and loop closing, to identify areas where memory usage can be reduced due to image quantization. The study examines how image quantization, especially a dynamic method called error diffusion quantization, affects the accuracy and efficiency of vSLAM algorithms. By using dynamic quantization, which reduces the number of distinct pixel values while keeping visual information loss low, it is possible to decrease memory usage with minimal impact on localization accuracy compared to the current quantization methods.

Keywords: System Engineering, Mechanical Measurement, Safety, Environment and Eco-Systems
Abstract: Recently rapid increase in space debris has become a major threat to human space activities. One measure to counteract this is utilization of laser ablation induced impulse where targeted debris satellite receives pulsed laser irradiation resulting in a portion of space flight material such as outer shell aluminum being ablated from the surface. Therefore, momentum change is produced, propulsive thrust opposite to the direction of the laser changes the velocity of satellite and finally the satellite would deorbit after sufficient irradiation. In order to design efficient debris removal system, momentum coupling coefficient Cm, the efficiency of converting laser energy to impulse is measured for aluminum, copper and CFRP using a compact pendulum stand. The stand consists of pendulum bob, laser target mounted at the bob, pendulum wire, supporting cage, eddy current damper and displacement sensor. The stand is set in a vacuum chamber with inside pressure of approximately 10-2 Pa. The investigated pulsed laser are basically 10ns Q-switched Nd:YAG laser (1064nm), its SHG (532nm), THG (355nm) and FHG (266nm). For the pulsed laser operation with enough interval the pendulum shows dumped oscillation with a recovery time of approximately 0.5 sec. Cm has been calculated using displacement impulse response and laser fluence. The result is presented. This work was supported by Innovative Science and Technology Initiative for Security Grant Number JP004596, ALTA, Japan.

Keywords: Temperature Measurement
Abstract: Recently, ammonia has been attracting attention as a hydrogen-energy carrier and carbon-free fuel. One of promising applications is as fuel for a gas turbine power generation system. When ammonia is used as fuel in the gas turbine systems, a part of combustion chambers can be exposed in every moment to various atmospheres, namely, reducing, nitriding and oxidizing conditions. The exposed temperature can also vary from low temperature to above 1000 °C. For developing such power generation system, precise temperature measurement above 1000 °C is consequently important. The measurement of temperature of the gas turbine and the combustor in a power generation system is generally carried out by utilizing thermocouples. In the case of power generation system that uses ammonia as fuel, ammonia-proved protection tubes are essential components for the thermocouples for measuring temperature precisely. There is still, however, limited information on the stability of the protection tube materials under ammonia containing environment at elevated temperature. In the present work, five types of metals and three types of ceramic pieces were prepared as the thermocouple protection-tube materials. Reactivity tests were then conducted in a flowing of ammonia and nitrogen mixing gas at the temperature of 1000 °C.