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1st International Conference on robotics, Engineering, science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Zaid, Mohd Ahmad, Nadeem Aljamal, Mohammed Patel, Sarosh Khan, Asif Dept. of Computer Science and Engineering Integral University Lucknow India Dept. of Electronics and Communication Engg. Goel Institute of Technology and Managment Lucknow India School of Engineering University of Bridgeport United States Integral Robotics Lab Dept. of Computer Application Integral University Lucknow India

ISBN: (纸本)9798350374933

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people. © 2024 IEEE.

关键词： Wi-Fi

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State of Charge Algorithm Considering Open Circuit Voltage of LiFePO4 Battery Cells for Battery Energy Storage System

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Transactions of the Korean Institute of Electrical Engineers 2025年第5期74卷 969-976页

作者： Na, Kunwoo Choi, Keun Ha Kim, Wooyong Dept. of Biomedical & Robotics Engineering Incheon National University Korea Republic of Dept. of Mechanical Engineering Korea Advanced Institute of Science and Technology Korea Republic of

Lithium iron phosphate (LFP) batteries are increasingly adopted in electric vehicles (xEVs) and battery energy storage systems (BESS) due to their thermal stability, safety, and cost-effectiveness. Despite these advantages, the inherently low electrical conductivity and flat charge-discharge voltage profile of LFP batteries present significant challenges for accurate state estimation. To address these limitations, this study employs a Sigma-Point Kalman Filter (SPKF), which demonstrates superior performance compared to the conventional Extended Kalman Filter (EKF) in handling nonlinear system dynamics. Furthermore, to account for the hysteresis behavior characteristic of LFP cells, quasi-open circuit voltage (qOCV) measurements are utilized. A Dual Sigma-Point Kalman Filter (DSPKF) is proposed to enhance the estimation of both state of charge (SoC) and hysteresis states. The experimental results validate the effectiveness of the proposed method, offering a high-precision state estimation framework that improves the reliability and performance of LFP batteries in xEV and BESS applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： State of charge

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Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biomarker Detection Using Machine Learning Algorithms 3

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biom...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Balaji, P. Devasena, D. Rithick, P. Karthi, T. Rishmitha, K. Roopa, R. Sri Ramakrishna Engineering College Dept. Of. Electronics and Instrumentation Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Dept. of Robotics and Automation Engineering Tamilnadu Coimbatore India Kongu Engineering College Dept. of Automobile Engineering Erode India Science Arjun College of Technology Dept. of Artificial Intelligence and Data Tamilnadu Coimbatore India

ISBN: (纸本)9798331528201

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector Machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices. © 2024 IEEE.

关键词： Polydimethylsiloxane

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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Poster Abstract: Reachability and Controlled Invariance for Human Stability during Sit-to-Stand 26

Poster Abstract: Reachability and Controlled Invariance for ...

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26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Raz, Daphna Yang, Liren Umberger, Brian Ozay, Necmiye Dept. of Robotics University of Michigan Ann Arbor United States Huazhong University of Science and Technology Wuhan China School of Kinesiology University of Michigan Ann Arbor United States

ISBN: (纸本)9798400700330

Stable human movement is often defined as movement that does not lead to falling. The set of such movements is too broad to be encompassed by traditional notions of stability in control theory, such as stability about equilibria or trajectories. We propose framing the region of stable human movement, which we call the stabilizable region, as the backward reachable set of a controlled invariant set. We focus on sit-to-stand, which requires a high level of coordination and is a common setting for falls. Using tools from the hybrid systems community, we compute the stabilizable region for sit-to-stand under varying environmental and physiological conditions. We validate our results with a dataset of humans performing perturbed sit-to-stand. © 2023 Copyright held by the owner/author(s).

关键词： Stability

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Autonomous Water Quality Monitoring Surface Vehicle

Autonomous Water Quality Monitoring Surface Vehicle

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Recent Advances in Systems science and Engineering (RASSE), IEEE International Conference on

作者： Manushri Saljith Nambiar Arjun Ramakrishnan Anirudh Lakshman Abhiram P V Girish P Dept. of Robotics and Automation Toc H Institute of Science and Technology

Water pollution is the contamination of water sources by contaminants that render the water unfit for drinking, cooking, cleaning, swimming, and other uses. Pollutants include chemicals, waste, bacteria, and parasites. Traditional monitoring methods entail manually collecting water samples from vari- ous water resources, followed by testing and analysis in the laboratory. This procedure is frequently ineffective because itis time-consuming and does not produce results in real-time. Water quality should be continuously monitored to ensure a safe supply of water to end users from any water resources or water bodies. Designing and creating a cost-effective system for real- time monitoring of water quality utilising the Internet of Thingsis now required. A water vehicle is employed here to house the water quality monitoring system. The vehicle is self-contained, and set locations for measuring water quality can be provided. Using IoT devices and a Wi-Fi module, the proposed system aids in the continuous monitoring of water quality. The Wi-Fi moduleenables internet connectivity and sends sensor data to the Cloud. Various sensors are used to measure various factors in order to assess the water quality of water resources

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Deformation Analysis and Prediction of Drop-Stitch Reinforced Inflatable Robot Link for 1DOF and 2DOF Motion

Deformation Analysis and Prediction of Drop-Stitch Reinforce...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Gubbala, Gangadhara Naga Sai Nagashima, Masato Mori, Hiroki Seong, Young Ah Sato, Hiroki Niiyama, Ryuma Suga, Yuki Ogata, Tetsuya Waseda University School of Fundamental Science and Eng. Dept. of Intermedia Art and Science Tokyo165-8555 Japan Waseda University Institute for AI and Robotics Future Robotics Organization Tokyo165-8555 Japan Hosei University Faculty of Eng. and Design Dept. of System Design Tokyo102-8160 Japan Miyagi University School of Project Design Miyagi981-3298 Japan Meiji University School of Science and Technology Tokyo214-8571 Japan Tokyo165-8555 Japan Tokyo100-8921 Japan

ISBN: (纸本)9798331531614

In this study, we observe the dynamic behavior of an inflatable robot arm with an internally reinforced drop-stitch structure. We examine the deformation during motion of 1 and 2 degrees of freedom (DOF) for an inflatable body. The inflatable robot arm has a soft inflatable body as links and rigid servo actuators as joints. We implemented a sinusoidal motion for inflatable links for various payload conditions and analyzed them using a Motion Capture system. To estimate the dynamic deformation of the balloon in motion, we have defined a Deformation Index (DI) metric. Angle, current of the actuator (servo), and DI are used as input to polynomial regression to predict the end effector position. With this analysis, we can understand the complexity of modeling the nonlinear behavior of inflatable links for multi-DOF motion. We observed DI helps improve the prediction of the end effector position by including deformation information. However, the results demonstrate the limitations of polynomial regression analysis of an internally reinforced inflatable robot arm link. © 2025 IEEE.

关键词： End effectors

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

Detection of Electric Motor Damage Through Analysis of Sound...

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Annual Conference of Industrial Electronics Society

作者： Waldemar Bauer Marta Zagórowska Jerzy Baranowski Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.

关键词： Accuracy Fault detection Neural networks Training data Robustness Bayes methods Wind turbines Signal representation Time-domain analysis Electric motors

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