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Analysis of influencing factors of excitation parameters for magnetoacoustic tomography with current injection

Chinese Physics B 2023年第5期32卷 496-503页

作者：李粟刘国强郭亮张文伟卢朝森夏慧 College of Information and Control Engineering China University of PetroleumQingdao 266580China Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China College of Electrical and Automation Engineering Shandong University of Science and TechnologyQingdao 266510China

Magneto-acoustic tomography with current injection(MAT-CI) is a type of hybrid imaging;under the excitation of the static magnetic field, the thermoacoustic effect and the Lorentz force effect will exist at the same time. Therefore,the detected signal is a mixed signal generated by the simultaneous action of the two effects, but the influence of excitation parameters on the two effects is different. In this paper, for objects with different conductivity, the proportion of thermoacoustic signal(TA) and magneto-acoustic signal(MA) in the mixed signal is quantitatively analyzed in terms of three aspects: the magnetic induction intensity, pulse excitation and injection current polarity. Experimental and simulation analyses show that the intensity ratio of MA to TA is not affected when the conductivity varies from 0.1 S/m to 1.5 S/m and other conditions remain unchanged. When the amplitude of the pulse excitation and the strength of the magnetic induction are different, the growth rates of MA and TA are different, which has a significant impact on the proportion of the two signals in the mixed signal. At the same time, due to the Lorentz force effect, MA is affected by the polarity of the injected current and the direction of the static magnetic field. The combination of the static magnetic field and the injected current can not only distinguish the two signals in the mixed signal, but also effectively enhance the intensity of the mixed signal and improve the quality of the reconstructed image.

关键词： magneto-acoustic tomography with current injection(MAT-CI) thermoacoustic effect Lorentz force effect acoustic source

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Iron ore pellets measurement using deep learning based on YOLACT

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Neural Computing and Applications 2024年第24期36卷 14909-14924页

作者： Santos, Caio Mario Carletti Vilela de Almeida, Ricardo Valadao, Carlos Torturella Cuadros, Marco Antonio de Souza Leite Almeida, Gustavo Maia de Postgradude Program of Control and Automation Engineering Federal Institute of Espírito Santo Avenida dos Sabiás Espirito Santo Serra29166-630 Brazil

The thermal efficiency for the pelletizing process is intrinsically linked to the diameter and humidity of the iron ore pellets, so that the sensing of the granulometric range in the formation of the pellets becomes essential to the flow of the pelletizing process in the steel industry;this paper presents the assembly of a computer vision system for the detection and segmentation of pellets aiming at the automation of the granulometric measurement, following its formation in the pelletizing disk, using the instance segmentation method to verify whether the particle granulometric distribution (PSD) is adequate for "real-time" applications. The system calculates the normal distribution of the diameter in millimeters, evaluating the normal curve and the standard deviation of the segmented pellets, using a deep neural network based on the You Only Look At CoefficienTs (YOLACT) network, adding speed and precision in the granulometric analysis. In the sample sets, the need for adjustment factors inherent to the pelletizing process became evident. This led to the establishment of the computer vision system, termed the Volumetric Correction Factor (VCF) and Visual Overlay Factor (VOF). The VCF is utilized to estimate the volume of pellets within the pelletizing disk during operation, while the VOF adjusts the millimeter-per-pixel (mpp) ratio. The results of the measurement system proved to be efficient in real-time granulometric measurement. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Pelletizing

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Spatial Coordination of Multiple Nonholonomic Agents With Sensory Connectivity Maintenance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3425-3435页

作者： Chen, Xi Chen, Meimin Zhu, Lijun Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Shandong Huayu University of Technology School of Electrical Engineering Shandong253034 China School of Artificial Intelligence and Automation Wuhan430074 China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

This article aims to propose a general control strategy for coordination of multiple nonholonomic agents in three dimensional space. For real-world applications, since the field sensor equipped on the mobile agents for local information detection and estimation has some limited detecting range, it is necessary to guarantee that the nearby agents must stay within this range of the onboard sensor. This is called sensory connectivity maintenance. A function termed as coordination function with sensory connectivity maintenance (CFSCM) is defined to describe the performances of the coordination as well as the sensory connectivity status between the agents. Then, a general control strategy is designed based on the proposed CFSCM for spatial coordination of multiple nonholonomic agents with sensory connectivity maintenance. Moreover, the applications of the proposed control strategy for formation with omnidirectional sensors and flocking with directional sensors are shown, respectively. Finally, some numerical examples are conducted to validate the theoretical analysis. © 2013 IEEE.

关键词： Maintenance Robot sensing systems Kinematics Vectors Electronic mail Aerospace electronics Three-dimensional displays System dynamics Robot kinematics Multi-agent systems

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A zeroth-order algorithm for distributed optimization with stochastic stripe observations

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Science China(Information Sciences) 2023年第9期66卷 297-298页

作者： Yinghui WANG Xianlin ZENG Wenxiao ZHAO Yiguang HONG School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Control Science and Engineering Tongji University

Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.

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Enhancing Unmanned Ground Vehicle Evasion Through Generative Adversarial Imitation Learning in UAV Pursuit Scenarios

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Guidance,Navigation and control 2025年第1期05卷 149-154页

作者： Shuai Hao Zhaoyu Zhang Mengzhen Huo Chen Wei School of Automation Science and Electrical Engineering Beihang University National Key Laboratory of Aircraft Integrated Flight Control Beihang University

This study focuses on enhancing the evasion capabilities of unmanned ground vehicles(UGVs) using Generative Adversarial Imitation Learning(GAIL). The UGVs are trained to evade unmanned aerial vehicles(UAVs). A decision-making neural network has been trained via GAIL to refine evasion strategies with expert demonstrations. The simulation environment was developed with OpenAI Gym and calibrated with real-world data for the improvement of accuracy. The integrated platform including the proposed algorithm was tested in flight experiments. Results showed that the UGVs could effectively evade UAVs in the complex and dynamic environment.

关键词： Unmanned ground vehicles unmanned aerial vehicles imitation learning flight experiment

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Analysis of circulating current elimination Based on Three Phase Dual Frequency Grid-Connected Inverter 8

Analysis of circulating current elimination Based on Three P...

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8th International Conference on electrical, Mechanical and Computer engineering, ICEMCE 2024

作者： Chen, Zhigang Cui, Zhenfei Yang, Liyong Tong, Chaonan University of Science and Technology School of Automation and Electrical Engineering Beijing China North China University of Technology College of Electrical and Control Engineering Beijing China

ISBN: (纸本)9798331506230

Integrating filters into inverters to improve the power quality is essential. This study examines a three-phase dual-frequency grid-connected inverter designed to minimize switching losses by reducing the switching frequency in the energy transmission channel. The system utilizes L-filters and auxiliary units (AUs) to suppress harmonics and ensure high-quality grid-connected current, effectively addressing resonance issues associated with LCL filters. Simulations and experimental results show that this scheme could eliminate the circulating current between the two units and effectively suppress the leakage current. © 2024 IEEE.

关键词： Switching frequency

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Distributed Adaptive Inverse Differential Game Approach to Leader's Behavior Learning for Multiple Autonomous Followers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1666-1678页

作者： Wu, Huai-Ning Wang, Mi Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

In this article, the leader's behavior learning problem is investigated for a class of leader-follower systems, where the leader's behavior is assumed not a priori known to all followers that are autonomous. A multiplayer nonzero-sum differential game is introduced to indicate the control interaction between agents, where the leader's behavior is modeled by an unknown cost function. The autonomous followers aim to retrieve the weighting matrix in the leader's cost function collaboratively. A distributed online adaptive inverse differential game (IDG) approach to the leader's behavior learning is proposed for the autonomous followers. Specifically, a concurrent learning based adaptive law and an interactive game controller are first developed for each autonomous follower to learn the leader's feedback gain matrix online, while the feedback Nash equilibrium of the game can be achieved. Then, a linear matrix inequality optimization problem is formulated to determine the weighting matrix of the leader's cost function for each autonomous follower. The proposed method simply requires that all followers share their interactive feedback gain matrices, not their private intents, and use only the system state data, not both the system state data and the leader's control input data. The main advantages of the proposed method are that it can be implemented online without requiring the persistent excitation condition and needs less computational power for all followers. Finally, numerical simulations are provided to demonstrate the effectiveness and feasibility of the developed method. © 2020 IEEE.

关键词： Cost functions

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Optimal Cooperative Secondary control for Islanded DC Microgrids via a Fully Actuated Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 405-417页

作者： Yi Yu Guo-Ping Liu Yi Huang Peng Shi IEEE Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Electrical and Mechanical Engineering the University of AdelaideSA 5005Australia Research and Innovation Centre Obuda University1034 BudapestHungary

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

关键词： DC microgrids distributed control high-order fully actuated system approach linear quadratic regulator microgrid modeling secondary control

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