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Design and Analysis of Individually Demagnetized Power Converter for Switched Reluctance Motors

IEEE Transactions on Transportation Electrification 2025年第3期11卷 8439-8449页

作者： Liu, Hemiao Chen, Hao Wang, Xing Gorbounov, Yassen Wheeler, Patrick Anuchin, Alecksey Demidova, Galina Korovkin, Nikolay Liudmila, Sakhno Shenzhen Research Institute China University of Mining and Technology Shenzhen515100 China China University of Mining and Technology School of Electrical Engineering Xuzhou221116 China Xinjiang Institute of Engineering Ürümqi830000 China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou221008 China Xuzhou Key Laboratory of New Energy Electric Vehicle Technology and Equipment Xuzhou221008 China Mining Production University of Mining and Geology "St. Ivan Rilski " Department of Automation Sofia1700 Bulgaria The University of Nottingham Power Electronics Machines and Control Group NottinghamNG7 2RD United Kingdom Electric Drives Department Moscow111250 Russia Saint-Petersburg National Research University of Information Technologies Mechanics and Optics Faculty of Control Systems and Robotics Engineer Saint Petersburg197101 Russia Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg195251 Russia

Switched reluctance motors (SRMs) are capable of operating at high speeds and high loads. The large back EMF results in the excitation winding current being demagnetized more slowly, which, in turn, reduces the efficiency and output of the motor system. This study proposes a novel individually demagnetization power converter (IDPC) specifically designed for SRMs to effectively use the demagnetization energy through an independent demagnetization circuit. Compared with traditional converters, the IDPC can accept direct current (dc) and high-frequency ac inputs while achieving fast demagnetization and energy recovery. The topology and working principle of the IDPC are analyzed in detail in this article using a three-phase 12/8 SRM as the research object. In addition, the influence of the winding demagnetization voltage on the negative torque during the demagnetization phase was studied, as well as the functional implementation and logic control strategy of the IDPC. Simulation and experimental results confirm the validity of the proposed topology and high-frequency ac drive characteristics, as well as their advantages in terms of efficiency, output capacity, and dynamic performance. © 2015 IEEE.

关键词： Reluctance motors

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Enhanced Gesture Recognition via Interpretable Temporal Convolutional Networks Utilizing Spatio-Temporal sEMG Features

Enhanced Gesture Recognition via Interpretable Temporal Conv...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Xiaohu Ao Jinhua She Feng Wang Seiichi Kawata Edwardo Fumihiko Fukushima Jinseok Woo School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Rehabilitation robots and human-robot interaction systems are receiving increasing attention in modern healthcare and engineering. Motion intent recognition techniques based on surface electromyography (sEMG) signals provide important support for these systems. However, motion intent recognition models rarely focus on the spatiotemporal properties of sEMG signals simultaneously. There is a lack of interpretable analysis of these spatiotemporal properties. The purpose of this study is to perform gesture recognition of sEMG signals from the Ninapro database using a temporal convolutional network (TCN). The sEMG signals are converted into sEMG images, which are used as input to the TCN model. The TCN model is utilized to perform gesture recognition on these images. The results show that after fusing the spatial and temporal features of the sEMG signal, the recognition accuracy of the TCN model is significantly higher than that of models using only spatial features or only temporal features. To understand the decision-making process of the model, we used an interpretable method based on Shapley values to perform a detailed interpretive analysis of the recognition results. The experimental results show that the TCN model has significant accuracy and stability in the sEMG signal gesture recognition task, while the Shapley value-based interpretable method can effectively reveal the contribution of different features to the prediction results, thus improving the transparency and user trust in the model. This study not only provides a new technical tool for sEMG signal gesture recognition but also demonstrates the value of interpretable methods in deep learning models.

关键词： Accuracy Intent recognition Computational modeling Gesture recognition Predictive models Stability analysis Real-time systems Spatiotemporal phenomena Convolutional neural networks Time complexity

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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

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Expert systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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Disturbance Rejection in Lower-Limb Rehabilitation Robot with Parameter Uncertainties

Disturbance Rejection in Lower-Limb Rehabilitation Robot wit...

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Intelligent systems, International Workshop on

作者： He Wang Jinhua She Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper presents a method integrated equivalent-input-disturbance (EID) and repetitive control (RC) to mitigate the effects of non-linearity and uncertainty in a pedaling rehabilitation robot. The repetitive controller is designed to ensure accurate steady-state tracking of periodic signal. Meanwhile, the EID estimator, combined with a state observer, is responsible for estimating and compensating for the impacts of non-linearity, parameter uncertainties, and disturbances caused by patients with lower-limb impairments. We design a Lyapunov stability function to assess the stability of system. The parameters of the control gain are obtained by linear matrix inequality (LMI). The results confirm the effectiveness of the proposed method.

关键词： Uncertain systems Accuracy Uncertainty Simulation Observers Assistive robots Stability analysis Steady-state Robots Lyapunov methods

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A Research on Attitude control of Quadrotor UAV Based on Equivalent-Input-Disturbance Approach and Model Predictive control

A Research on Attitude Control of Quadrotor UAV Based on Equ...

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Intelligent systems, International Workshop on

作者： Zihao Ning Jinhua She Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper discusses the problem of attitude control of a quadrotor unmanned aerial vehicles (UAV). As external disturbances and system nonlinearities will affect the attitude control of quadcopter UAV, making it difficult for general control methods to achieve satisfactory results. To address this problem, this paper applies the equivalent-input-disturbance (EID) approach and the model predictive control (MPC) to the attitude control of a quadcopter UAV to ensure the reference tracking and suppress the influence of the total disturbances on the attitude of the quadcopter UAV, and the simulation results prove the effectiveness of the method.

关键词： Attitude control Simulation Conferences Autonomous aerial vehicles Prediction algorithms control systems Intelligent systems Quadrotors Predictive control Intelligent control

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Improved BP Neural Network Utilizing Bayesian optimization Algorithm for Iron-making Process control

Improved BP Neural Network Utilizing Bayesian optimization A...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Shaoran Wang Jinhua She Seiichi Kawata Feng Wang Juan Zhao Qifu Chen Zhenhong Liao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

The manufacturing sector covers a wide range of areas, focusing mainly on major equipment manufacturing and process industries, including new energy and new materials manufacturing. The ultimate goal is to realize intelligent manufacturing. Iron and steel metallurgy is an important pillar of the national economy, especially in the field of process industry. In the ironmaking process, a large number of process parameters are systematically collected, forming high-dimensional time series big data. These parameters contain hundreds of influencing factors. Key production indicators such as yield, energy consumption and iron quality are closely related to a controlled intermediate variable in the smelting process, the furnace temperature. Silicon content [Si] and the mass percentage of Si in the molten iron is a good example. In order to predict and control the blast furnace ironmaking process more effectively, this paper introduces a BP neural network prediction model, and optimizes and compares the neural network model with genetic algorithm and Bayesian optimization technique.

关键词： Heuristic algorithms Neural networks Process control Predictive models Prediction algorithms Silicon Iron

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

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International Electrical engineering Congress (iEECON)

作者： Guntinan Sakulphaisan Sirirat Son-in Nitikorn Junhuathon Industrial technology and innovation management Faculty of science and technology Pathumwan institute of technology Bangkok Thailand Electrical Engineering and Automation Control Systems Faculty of engineering and industrial technology Bansomdejchaopraya Rajabhat University Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathumthani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%.

关键词： Electrical engineering Vehicle-to-grid Energy loss Electric potential Demand forecasting Probabilistic logic Resilience

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Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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Improving the Adaptability of sEMG-Based Gesture Recognition Using Variational Mode Decomposition and Transfer Learning

Improving the Adaptability of sEMG-Based Gesture Recognition...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Hao Wu Feng Wang Juan Zhao Seiichi Kawata Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Transfer learning (TL) is a representative method to reducing user gaps by utilizing features already learned by pre-trained models. However, TL uses a large amount of training data due to the discrepancy of sEMG among different users, which increases the training burden. In this paper, a multi-user adaptive network (MUAN) is devised to decompose the insensitive features among different users to improve gesture recognition accuracy for new users, which is based on variational modal decomposition (VMD), convolutional neural network (CNN), and TL. Ninapro dataset is used to evaluate the adaptability of MUAN and the training burden on new users. Experimental results show that MUAN outperforms CNN and TL, and reduces the training burden for new users. MUAN has the potential to provide a robust and generalized HMI system for clinical applications.

关键词： Training Adaptation models Adaptive systems Transfer learning Training data Gesture recognition Electromyography Physiology Convolutional neural networks Signal resolution

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Formation control of Discrete-time Multi-agent systems Based on Distributed Filter Observer

Formation Control of Discrete-time Multi-agent Systems Based...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Huanli Gao Wei Li Ming Qiu He Cai School of Automation Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou China Department of Shanhaiguan District People’s Armed Forces Qinhuangdao City China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper explores the formation control of discrete multi-agent systems under external communication disturbance. Firstly, leveraging the modified algebraic Riccati equation, a disturbed output based discrete filter observer is proposed to minimize network information exchange and mitigate disturbance effects. Then, the formation control issue is transformed into a cooperative output regulation problem, which a certainty equivalent state feedback controller solves. The effectiveness of the control strategy is demonstrated by numerical simulations.

关键词： State feedback Riccati equations Observers Information filters Numerical simulation Filtering theory Formation control

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