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1st Aerospace Frontiers Conference, AFC 2024

作者： Du, Lei Chen, Zhen Hu, Hengzai Liu, Xiangdong Zhang, Qiang Zhang, Kemo Liu, Hui School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100191 China Beijing Institute Computer for Application Beijing100089 China

ISBN: (纸本)9781510681613

A large number of space debris with rotational motion occupy scarce orbital resources, thus, a de-tumbling phase may be necessary prior to the capturing phase to reduce the risk of collision. A new contactless AC current de-tumbling method based on the principle of electromagnetic eddy current is proposed in this paper. Firstly, an analysis is conducted on external disturbances affecting the attitude of the debris and de-tumbling device, such as magnetic field perturbations. Compared to the electromagnetic forces acting on de-tumbling device, the influence of the geomagnetic force on detumbling device can be negligible. However, the geomagnetic torque acting on de-tumbling device cannot be ignored. Then, the advantages and disadvantages of the two AC current cases are compared and analyzed, and the method to increase the de-tumbling torque can be obtained by adjusting the frequency of the three phase AC current. Finally, the three-dimensional attitude dynamics of the uncooperative space target is established taking a spinning cylinder as the hypothetical target of de-tumbling. Simulation shows that the de-tumbling method is effective and the de-tumbling rate can be greatly improved by adjusting the frequency of the three phase AC current. © 2024 SPIE.

关键词： Space debris

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Design of A Novel Primary Permanent Magnet Linear Machine with Low Thrust Ripple 7

Design of A Novel Primary Permanent Magnet Linear Machine wi...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Chai, Jiawei Xu, Jiajia Gui, Xianguo Gao, Qiang School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Innovation Center for Control Actuators Beijing Institute of Precision Mechatronics and Controls Beijing China

ISBN: (纸本)9798350366945

In order to reduce thrust ripple, this paper proposes a new primary permanent magnet linear machine with uneven pole (UP-PPMLM). Firstly, the proposed structure and advantages compared to conventional structure are introduced. Then, the expressions of air-gap flux density by permanent magnets (PMs) and armature reaction are derived. Finally, by using finite element method (FEM), the new topology reasonability is proved. Meanwhile, the performances of proposed machines are compared to conventional topology, and the result confirms the electromagnetic performances of the proposed machine have been greatly improved. © 2024 IEEE.

关键词： Permanent magnets

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Attack Detecting for the Multi-robots Systems: An Integrated Cyber-Physical Approach 7th

Attack Detecting for the Multi-robots Systems: An Integrat...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Yang, Chunyu Xu, Shanguang Ma, Lei Wu, Tihao Ye, Hongmei Xu, Yiming School of Information and Control Engineering China University of Mining and Technology Xu’zhou221116 China Jiangsu Institute of Automation Lianyungang China

ISBN: (纸本)9789819733316

To address security issues and effectively detect cyber-attack behaviors in multi-robot systems, this paper proposes a cyber-attack detection method that combines information-physical features. The method involves performing cyber intrusions, such as false data injection attack, replay attack, and DoS attack, on a multi-robot system using the Kali Linux operating system to disrupt its collaborative motion process. Physical state information of the system and network traffic information are then collected. Based on Convolutional Neural Network (CNN), a cyber-attack detection method is designed that fuses the information-physical features. The proposed method is validated on a multi-robot system in the Gazebo simulation environment. Simulation and experimental results demonstrate that the proposed cyber-attack detection method can effectively detect system anomalies with higher accuracy compared to the detection methods based solely on physical features or network traffic characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multipurpose robots

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Multi-objective Optimization of the Interior Permanent Magnet Synchronous Motor for Electric Vehicles 6

Multi-objective Optimization of the Interior Permanent Magne...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Event-Triggered Extended Dissipative FTB for T-S Fuzzy Switched Systems With Mismatched Phenomena and Deception Attacks: A Multidomain Framework

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IEEE Transactions on Cybernetics 2024年第9期54卷 4928-4938页

作者： Chen, Haiyang Zong, Guangdeng Su, Shun-Feng Gao, Fangzheng Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Jiangsu Collaborative Innovation Center for Smart Distribution Network Nanjing211167 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Nanjing Institute of Technology School of Automation Nanjing211167 China

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results. © 2013 IEEE.

关键词： controllers

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Optimal Strategy for Aircraft Pursuit-evasion Games via Self-play Iteration

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Machine Intelligence Research 2024年第3期21卷 585-596页

作者： Xin Wang Qing-Lai Wei Tao Li Jie Zhang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

In this paper,the pursuit-evasion game with state and control constraints is solved to achieve the Nash equilibrium of both the pursuer and the evader with an iterative self-play *** the condition where the Hamiltonian formed by means of Pontryagin’s maximum principle has the unique solution,it can be proven that the iterative control law converges to the Nash equilibrium ***,the strong nonlinearity of the ordinary differential equations formulated by Pontryagin’s maximum principle makes the control policy difficult to figured *** the system dynamics employed in this manuscript contains a high dimensional state vector with *** practical applications,such as the control of aircraft,the provided overload is ***,in this paper,we consider the optimal strategy of pursuit-evasion games with constant constraint on the control,while some state vectors are restricted by the function of the *** address the challenges,the optimal control problems are transformed into nonlinear programming problems through the direct collocation ***,two numerical cases of the aircraft pursuit-evasion scenario are given to demonstrate the effectiveness of the presented method to obtain the optimal control of both the pursuer and the evader.

关键词： Differential games pursuit-evasion games nonlinear control optimal control Nash equilibrium solution

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Foreground Segmentation Network with Enhanced Attention

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Journal of Shanghai Jiaotong university(Science) 2023年第3期28卷 360-369页

作者：姜锐朱瑞祥蔡萧萃苏虎 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing 100190China

Moving object segmentation (MOS) is one of the essential functions of the vision system of all robots,including medical robots. Deep learning-based MOS methods, especially deep end-to-end MOS methods, are actively investigated in this ﬁeld. Foreground segmentation networks (FgSegNets) are representative deep end-to-endMOS methods proposed recently. This study explores a new mechanism to improve the spatial feature learningcapability of FgSegNets with relatively few brought parameters. Speciﬁcally, we propose an enhanced attention(EA) module, a parallel connection of an attention module and a lightweight enhancement module, with sequentialattention and residual attention as special cases. We also propose integrating EA with FgSegNet_v2 by taking thelightweight convolutional block attention module as the attention module and plugging EA module after the twoMaxpooling layers of the encoder. The derived new model is named FgSegNet_v2 EA. The ablation study veriﬁesthe effectiveness of the proposed EA module and integration strategy. The results on the CDnet2014 dataset,which depicts human activities and vehicles captured in different scenes, show that FgSegNet_v2 EA outperformsFgSegNet_v2 by 0.08% and 14.5% under the settings of scene dependent evaluation and scene independent evaluation, respectively, which indicates the positive effect of EA on improving spatial feature learning capability ofFgSegNet_v2.

关键词： human-computer interaction moving object segmentation foreground segmentation network enhanced attention convolutional block attention module

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

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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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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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