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Set-based state estimation of a Li-ion cell using DC programming and constrained zonotopes

IFAC-PapersOnLine 2023年第2期56卷 7147-7153页

作者： Angelo Tottoli Alesi A. de Paula Guilherme V. Raffo Bruno O.S. Teixeira Davide M. Raimondo Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

Advanced battery management systems (ABMSs) rely on mathematical models to ensure high battery safety and performance. One of the key tasks of a BMS is state estimation. In the following, we consider a single lithium-ion cell described with a dual polarization equivalent circuit model. To consider a realistic scenario, where the parameters have been identified from experimentally collected data, both parametric and measurement uncertainties are taken into account in the model. In particular, unknown but bounded uncertainties are assumed. In this setup, we address state estimation through a set-based approach using Constrained Zonotopes (CZ). Due to the model nonlinearities, a method able to propagate CZ through nonlinear mappings is demanded. Within this context, mean value and first-order Taylor CZ-based extensions were proposed which, however, might lead to conservative overestimation due to the sensitivity to the wrapping and dependency effects inherited from interval arithmetic. In the following, we suggest the use of DC programming as an alternative. The effectiveness of the proposed scheme is demonstrated in simulation for the considered Li-ion model.

关键词： Lithium-ion cells Nonlinear state estimation Uncertainty Set-based approach

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Human-Force-Based Imitation Learning Enabling Mobile Robotic Carts to Avoid Obstacles 26

Human-Force-Based Imitation Learning Enabling Mobile Robotic...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Nitobe, Motoya Ohhira, Takashi Hashimoto, Hideki Graduate School of Science and Engineering Chuo University Tokyo Japan Chuo University Department of Electrical Electronic and Communication Engineering Tokyo Japan

ISBN: (纸本)9798331521950

Recently, the demand for transport-support systems that provide physical assistance to humans in carrying out heavy tasks, such as transportation, has surged, and mobile robots with high mobility performance have been studied. However, autonomous movement control and obstacles on the path from the current position to the target position hamper the adaptability of these robots in the real world. In this paper, we propose a machine learning based autonomous control method for a mobile robotic cart to avoid obstacles while moving to the target position. We train a neural network using distance data to the obstacle and human operation information. The proposed method is validated via experiments on obstacle avoidance © 2025 IEEE.

关键词： Mobile robots

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1D-DCGAN for Oversampling Minority Mitotic Patterns in HEp-2 Cell Images 4

1D-DCGAN for Oversampling Minority Mitotic Patterns in HEp-2...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Anaam, Asaad Al-Masni, Mohammed A. Gofuku, Akio Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama Japan Yonsei University Department of Electrical and Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9781665419048

This paper proposes a framework for oversampling the minority mitotic patterns of the HEp-2 cell images. The classification of mitotic vs. non-mitotic (interphase) cell patterns is important for validating the Indirect Immunofluorescence on Human Epithelial Type-2 cell-substrate (IIF HEp-2) protocol, which is the "gold standard"test for diagnosing autoimmune diseases. Typically, the mitotic cells appear in the HEp-2 specimen image in a significantly less number than the interphases. This causes difficulty in adopting deep learning approaches to classify mitotic vs. interphase patterns with such high imbalanced data. This work suggests using One-Dimensional Deep Convolutional Generative Adversarial Networks (1D-DCGAN) for oversampling the minority mitotic patterns in the feature space of the Deep Cross Residual Network (DCRNet) to cope with the data skewness problem. The results demonstrated that the proposed approach improves the classification performance over the UQ-SNP I3A Task-3 mitotic cell dataset with the advantage of using an end-to-end CNN classifier. © 2022 IEEE.

关键词： Generative adversarial networks

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Measurement of electrical Characteristics of Vehicle Body Toward Capacitive Power Transfer Through Automotive Paintings

Measurement of Electrical Characteristics of Vehicle Body To...

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Wireless Power Week (WPW)

作者： Stephane Ken Yagi Hirohito Funato Graduate Program in Information Electrical and Electronic Systems Engineering Utsunomiya University Utsunomiya Japan Dept. of Fundamental Engineering Utsunomiya University Utsunomiya Japan

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

In this paper, impedance characteristics of real car body is measured toward implementation of capacitive power transfer through automotive paintings. In measurement of car impedance, capacitive coupling electrodes are applied at three different positions in order to observe impedance characteristics changed for different positions. After measurement, equivalent circuit at the different electrode positions are estimated for realization of further efficient capacitive power transfer.

关键词： Electrodes Wireless communication Power measurement Impedance measurement Position measurement Automobiles Impedance

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Machine Learning for Unmanned Aerial Vehicles Navigation: An Overview

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SN Computer Science 2024年第2期5卷 256页

作者： Fagundes-Junior, Leonardo A. de Carvalho, Kevin B. Ferreira, Ricardo S. Brandão, Alexandre S. Núcleo de Especialização em Robótica Department of Electrical Engineering Graduate Program in Computer Science Federal University of Viçosa-UFV Av. Petter Henry Rolfs MG Viçosa 36570-900 Brazil

Unmanned aerial vehicles (UAVs) are a valuable source of data for a wide range of real-time applications, due to their functionality, availability, adaptability, and maneuverability. Working as mobile sensors, they can provide a cost-effective solution for extremely complex tasks, such as inspection, air-to-ground communications, search and rescue, surveillance, among others. Nevertheless, the robots needs to navigate in quite distinct environments and in different dynamism levels, usually facing unpredicted situations, very often using limited sensing and computing capabilities. A large number of solutions to this problem has been featured by the scientific community in the last years, some of them based on machine-learning (ML) methods. Due to its great capability to deal with big data and complexity, as well as its speedy and high-accuracy processing, the ML framework has been used to improve existing technologies and control techniques. In this context, its adoption in several UAV navigation strategies is expected to provide solutions for various problems where UAVs are used in real-time applications. Thus, in order to contextualize the most recent advances, this work provides a detailed survey of relevant researches in which ML techniques have been used in UAV navigation to improve some functional aspects, such as energy-efficiency, communication, execution time, resource management, obstacle avoidance, and path planning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Autonomous navigation Path planning UAV-based solutions Visual-based navigation

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Polarization-Switchable Electrochemistry of 2D Layered Bi2O2Se Bifunctional Microreactors by Ferroelectric Modulation

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Nano Letters 2024年第35期24卷 11012-11019页

作者： Chiang, Chun-Hao Yu, Chun-Hung Lu, Yang-Sheng Yang, Yueh-Chiang Lin, Yin-Cheng Chen, Hsin-An Ho, Sheng-Zhu Chen, Yi-Chun Kumatani, Akichika Chang, Chen Kuo, Pai-Chia Shiue, Jessie Li, Shao-Sian Chiu, Po-Wen Chen, Chun-Wei Department of Materials Science and Engineering National Taiwan University Taipei10617 Taiwan Institute of Materials Science and Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Physics National Cheng Kung University Tainan70101 Taiwan Department of Electrical and Electronic Engineering Chiba Institute of Technology Chiba275-0016 Japan Saitama332-0012 Japan Tohoku University Sendai980-8577 Japan Graduate School of Engineering Tohoku University Sendai980-8579 Japan Institute of Atomic and Molecular Science Academia Sinica Taipei10617 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei10617 Taiwan National Taiwan University Taipei10617 Taiwan

Ferroelectric catalysts are known for altering surface catalytic activities by changing the direction of their electric polarizations. This study demonstrates polarization-switchable electrochemistry using layered bismuth oxyselenide (L-Bi2O2Se) bifunctional microreactors through ferroelectric modulation. A selective-area ionic liquid gating is developed with precise control over the spatial distribution of the dipole orientation of L-Bi2O2Se. On-chip microreactors with upward polarization favor the oxygen evolution reaction, whereas those with downward polarization prefer the hydrogen evolution reaction. The microscopic origin behind polarization-switchable electrochemistry primarily stems from enhanced surface adsorption and reduced energy barriers for reactions, as examined by nanoscale scanning electrochemical cell microscopy. Integrating a pair of L-Bi2O2Se microreactors consisting of upward or downward polarizations demonstrates overall water splitting in a full-cell configuration based on a bifunctional catalyst. The ability to modulate surface polarizations on a single catalyst via ferroelectric polarization switching offers a pathway for designing catalysts for water splitting. © 2024 The Authors. Published by American Chemical Society.

关键词： Hydrogen evolution reaction

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Guest Editorial: Special Issue on Technology Trends in Control, Robotics and Systems - For the 30th Anniversary of the Institute of Control, Robotics and Systems -

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Journal of Institute of Control, Robotics and Systems 2024年第4期30卷 295-295页

作者： Ko, Nak Yong Kang, Taesam Song, Jin Woo Lim, Myo Taeg Jo, Kang-Hyun Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Korea Republic of Department of Mechanical and Aerospace Engineering Konkuk University Korea Republic of Department of Intelligent Mechatronics Engineering Sejong University Korea Republic of School of Electrical Engineering Korea University Korea Republic of Department of Electrical Electronic and Computer Engineering University of Ulsan Korea Republic of

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Performance Evaluation of KNIME Low Code Platform in Deep Learning Study and Optimal Hyperparameter Tuning

Performance Evaluation of KNIME Low Code Platform in Deep Le...

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IEEE Region 10 International Conference TENCON

作者： Pornpawee Thongkhome Takuro Yonezawa Nobuo Kawaguchi Automotive Engineering - G30 International Program in Electrical Electronic and Information Engineering School of Engineering Nagoya University Aichi Japan Graduate School of Engineering Nagoya University Aichi Japan

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

A low-code platform is a software development environment that allows for the creation of applications through graphical user interfaces and configuration instead of traditional hand-coded computer programming. In this study, the application to classify a dataset of traffic sign images using the KNIME low-code deep learning development platform will be discussed to represents this software performance especial in term of model optimization processes. By creates the workflow to perform image preprocessing, create the CNN layer under KERAS sequential API and finding the best set of key hyperparameters among traditional KNIME build-in optimization algorithm including Brute force, Hill climbing, random search, Bayesian Optimization and black-box optimizer Optuna optimization algorithm under 3 types CNN architecture as simple CNN, Resnet-50 and VGG16 to classify traffic sign images. The result demonstrates that both grid search and random search optimization can be effective, while both Optuna and Bayesian optimization stands out as a powerful method due to its ability to efficiently explore the hyperparameter space and achieve superior results to meet 99% accuracy under simple CNN environment, but Optuna is significantly improve optimization times than Bayesian about 7 - 8 times. The KNIME low-code platform provides a user-friendly environment for developing and fine-tuning models to contribute the ongoing progress in machine learning and deep learning research development.

关键词： Deep learning Codes Software algorithms Software performance Bayes methods Space exploration Classification algorithms Optimization Tuning Software development management

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Wildlife Intrusion Detection System for Agriculture Protection Based on Image Recognition 13

Wildlife Intrusion Detection System for Agriculture Protecti...

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13th IEEE Global Conference on Consumer electronic, GCCE 2024

作者： Kiat, Yong Kit Som Mahamad, Abd Kadir Saon, Sharifah Othman, Maisara Kadir, Herdawatie Binti Abdul Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia Universiti Teknologi Petronas Department of Computer and Information Sciences Perak Malaysia Yamaguchi University Graduate School of Sciences and Technology for Innovation Japan

ISBN: (纸本)9798350355079

The proposed project uses a Raspberry Pi microcontroller to prevent crop losses caused by animals like dog, wild pigs, and monkeys. These animals pose a significant threat to farmers, leading to financial losses. This project offers an innovative solution, as it is impractical to physically secure entire fields or have farmers present at all times. Raspberry Pi 4 and the Raspberry Pi Camera Module 2 NoIR havebeen used in this project, to captures images of intruding animals, sends notifications to users, and triggers an alarm. The model obtained an accuracy of 95% for intruder detection. The goal is to provide timely alerts, allowing farmers to respond quickly and minimize crop damage, effectively addressing wildlife-induced agricultural losses. © 2024 IEEE.

关键词： Livestock

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Magnetization Estimation for Permanent Magnet Using Convolutional Neural Network

Magnetization Estimation for Permanent Magnet Using Convolut...

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IEEE Conference on Electromagnetic Field Computation

作者： Kazuki Igarashi Hidenori Sasaki Masahide Shioyama Yoshifumi Okamoto Graduate School of Science and Engineering Hosei University Koganei Japan Department of Electrical and Electronic Engineering Hosei University Koganei Japan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

A new method for the nondestructive estimation of the magnetization distribution (MD) inside a permanent magnet, using a convolutional neural network, is proposed herein. The proposed method improves the estimation accuracy by 15.1% compared to the conventional method that uses a multi-layer perceptron. It achieves high accuracy in estimating the MD. Furthermore, the proposed method accurately predicts the MD from the measured magnetic flux density distribution.

关键词： Training Magnetic flux density Accuracy Density measurement Magnetization Computational modeling Estimation

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