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5th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2023

作者： Wang, Enlong Bai, Jing Liu, Hongtao Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking School of Electrical Engineering Lanzhou China Shenyang Institute of Technology School of Information and Control Shenyang China

ISBN: (纸本)9798350310603

Magnetizing inrush current caused by no-load closing of transformers frequently lead to malfunction of transformer differential protection, which will result in potential risks to power system. In some cases, electrical equipment may be damaged. Therefore, in this paper a method based on wavelet transform for identifying excitation inrush current and fault current is proposed. The wavelet transforms are performed on the current signal results. Through analyzing the results of wavelet transform, it is shown that this method can effectively identify between the excitation inrush current and the fault current, which helps to improve the effectiveness of transformer relay protection. © 2023 IEEE.

关键词： Wavelet transforms

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3D Object Pose Estimation Based on End-to-End Differentiable Perspective Projection 43

3D Object Pose Estimation Based on End-to-End Differentiable...

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43rd Chinese control Conference, CCC 2024

作者： Cai, Yuheng Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Locating 3D objects from a single RGB image via perspective projection is a long-standing problem in computer vision. Inspired by differentiable geometry methods in the field of camera relocation, we apply this neural network combined with differentiable perspective projection to form an end-to-end architecture to the field of object position estimation through object 3D reconstruction methods. The proposed method uses a differentiable LM algorithm for end-to-end pose estimation to achieve higher accuracy, and the gradient is passed back to the neural network to guide its learning. At the same time, we propose a virtual view generation algorithm for augmenting the data to improve the algorithm's sensitivity to the size of the co-vision region. Through experiments, we prove the effectiveness of our method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Neural networks

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Fixed-Time Generalized VGESO-Based Trajectory Tracking control for WMRs on Uneven Road: A Fully Actuated System Approach

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年第6期72卷 2915-2927页

作者： Qiang, Jiaping Li, Li Hua, Changchun Ren, Xiangyi Liu, Chao Yanshan University Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment School of Electrical Engineering Qinhuangdao066004 China Yanshan University School of Electrical Engineering Qinhuangdao066004 China Tianjin University International Joint Institute Fuzhou350207 China Tianjin University Tianjin300072 China

In this paper, a trajectory tracking control problem is investigated for a wheeled mobile robot (WMR) on uneven road. A mapping relationship is established between the velocity and position to describe the motion of the robot on uneven road. An adaptive kinematic controller (AKC) is designed to improve the trajectory tracking accuracy, where robot parameters and control gains are both estimated. Then, a fixed-time generalized variable gain extended state observer (VGESO) is proposed to estimate the states and disturbance caused by uneven road, where a balance is maintained between disturbance rejection and noise suppression. Next, a dynamic controller is put forward by combining fully actuated system (FAS) approach with practical prescribed time (PPT) control. It is analyzed that the velocity tracking error system is PPT stable. Finally, experimental results demonstrate effectiveness and superiority of the proposed method. © 2004-2012 IEEE.

关键词： Mobile robots

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A heuristic control framework for heavy-duty hexapod robot over complex terrain

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IET Cyber-Systems and Robotics 2022年第4期4卷 322-330页

作者： Jinmian Hou Hui Chai Yibin Li Yaxian Xin Wei Chen School of Control Science and Engineering Shandong UniversityJinanChina Engineering Research Center of Intelligent Unmanned System Ministry of EducationShandong UniversityJinanChina

The large and heavy-duty hexapod robot has strong motion stability and load capacity,which promises to have a wide range of application prospects in rescue and disaster ***-mode gait and static stability during walking make the hexapod robot adapt to more diverse terrains,while little research has been conducted on the motion control methods of heavy-duty hexapod robots in complex environments.A novel heuristic whole-body motion control framework for the heavy-duty hexapod robot to traverse complex terrain is *** splitting the legged locomotion into a single task,the whole-body motion could be planned in a reasonable *** terrain adaptation strategy is designed to improve the complex terrain *** reaction forces are then optimised based on single rigid-body dynamics with *** framework utilised simple but powerful heuristics to approximate complex dynamics and allows for a single set of parameters for all task *** results demonstrate the robustness and adaptability of the proposed framework.

关键词： legged locomotion robotics robots

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Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第6期10卷 656-665页

作者： Zhao, Zongya Ran, Xiangying Niu, Yanxiang Qiu, Mengyue Lv, Shiyang Zhu, Mingjie Wang, Junming Li, Mingcai Gao, Zhixian Wang, Chang Xu, Yongtao Ren, Wu Zhou, Xuezhi Fan, Xiaofeng Song, Jinggui Qi, Mingchao Yu, Yi School of Medical Engineering School of Mathematical Medicine Xinxiang Medical University Xinxiang China Second Affiliated Hospital of Xinxiang Medical University Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback Xinxiang China Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for Mental and Neurological Diseases Henan China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Institute of Disaster and Emergency Medicine Tianjin University Tianjin China

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used either electroencephalography (EEG) or clinical features, lack interpretability, or have small sample sizes. This study included 74 patients with MDD who responded (responders) and 43 patients with MDD who did not respond (nonresponders) to rTMS. Eight baseline EEG metrics and clinical features were sent to 7 machine learning models to classify responders and nonresponders. Shapley additive explanations (SHAP) was used to interpret feature contributions. Combining phase locking value and clinical features with support vector machine achieved optimal classification performance (accuracy = 97.33%). SHAP revealed that delta and beta band functional connectivity (F3-P7, F3-P4, P3-P8, T7-Cz) significantly influenced predictions and differed between groups. This study developed an explainable predictive framework to predict rTMS response in MDD, enhancing the accuracy of rTMS response prediction and supporting personalized treatment in MDD. © 2025 Society of Biological Psychiatry

关键词： Biomarkers Machine learning Major depressive disorder Predicting treatment response rTMS

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X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation 24

X-Prompt: Multi-modal Visual Prompt for Video Object Segment...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Pinxue Li, Wanyun Huang, Hao Hong, Lingyi Zhou, Xinyu Chen, Zhaoyu Li, Jinglun Jiang, Kaixun Zhang, Wei Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology China

ISBN: (纸本)9798400706868

Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where traditional VOS methods struggle, such as extreme illumination, rapid motion, and background distraction. Existing approaches often involve designing specific additional branches and performing full-parameter fine-tuning for fusion in each task. However, this paradigm not only duplicates research efforts and hardware costs but also risks model collapse with the limited multi-modal annotated data. In this paper, we propose a universal framework named X-Prompt for all multi-modal video object segmentation tasks, designated as RGB+X. The X-Prompt framework first pre-trains a video object segmentation foundation model using RGB data, and then utilize the additional modality of the prompt to adapt it to downstream multi-modal tasks with limited data. Within the X-Prompt framework, we introduce the Multi-modal Visual Prompter (MVP), which allows prompting foundation model with the various modalities to segment objects precisely. We further propose the Multi-modal Adaptation Experts (MAEs) to adapt the foundation model with pluggable modality-specific knowledge without compromising the generalization capacity. To evaluate the effectiveness of the X-Prompt framework, we conduct extensive experiments on 3 tasks across 4 benchmarks. The proposed universal X-Prompt framework consistently outperforms the full fine-tuning paradigm and achieves state-of-the-art performance. Code: https://***/PinxueGuo/*** © 2024 ACM.

关键词： Image segmentation

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Fixed-time position coordinated tracking control for spacecraft formation flying with collision avoidance

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Chinese Journal of Aeronautics 2021年第11期34卷 182-199页

作者： Minglei ZHUANG Liguo TAN Kehang LI Shenmin SONG Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China Research Center of Basic Space Science Harbin Institute of TechnologyHarbin 150001China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100190China

In this paper,the fixed-time stability of spacecraft formation reconfiguration(position tracking)is ***,a novel nonsingular terminal sliding mode surface is designed and based on which a fixed-time coordinated controller is designed to keep the closed-loop system states have a finite settling time bounded by some predefined ***,another nonsingular terminal sliding mode surface is designed by combining the artificial potential function and the aforementioned sliding surface,which meets the mutual distance constraint during transition process among spacecraft when it is *** another coordinated controller with fixed-time observer considering mutual distance constraint is presented,which guarantees the closed-loop system states stable also in bounded settling ***,simulation results are shown to validate the correctness of the proposed *** is worth mentioning that the control schemes also work even though there is a properly limit on the control input.

关键词： Collision avoidance Disturbance rejection Fixed-time stability Sliding mode control Spacecraft formation flying

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A multi-UAV deployment method for border patrolling based on Stackelberg game

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Journal of Systems engineering and Electronics 2023年第1期34卷 99-116页

作者： LEI Xing HU Xiaoxuan WANG Guoqiang LUO He School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Engineering Research Center for Intelligent Decision-making&Information Systems Technologies Ministry of EducationHefei 230009China

To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader,who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.

关键词： border patrol unmanned aerial vehicle(UAV) Stackelberg game compact linear programming dominated strategy elimination

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A Semi-Decentralized and Variational-Equilibrium-Based Trajectory Planner for Connected and Autonomous Vehicles 27

A Semi-Decentralized and Variational-Equilibrium-Based Traje...

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27th IEEE International Conference on intelligent Transportation Systems, ITSC 2024

作者： Liu, Zhengqin Lei, Jinlong Yi, Peng Tongji University Department of Control Science and Engineering Shanghai201804 China Tongii University Department of Control Science and Engineering Shanghai201804 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (纸本)9798331505929

This paper designs a novel trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated methods by exploiting vehicle-to-everything (V2X) technology. The trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define interaction-fair trajectories and prove that they correspond to the variational equilibrium (VE) of this game. We propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, which can significantly improve computational efficiency through parallel computing among CAVs and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, experimental results show the advantages of the approach, including fast computation speed, high scalability, equilibrium concordance, and safety. © 2024 IEEE.

关键词： Trajectories

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Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 619-628页

作者： Kao, Sheng-Xiang Chien, Chen-Fu International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Intelligent Manufacturing and Circular Economy Research Center National Tsing Hua University Artificial Intelligence for Intelligent Manufacturing Systems Research Center National Science and Technology Council Department of Industrial Engineering and Engineering Management Hsinchu300044 Taiwan

Little research has been done for artificial intelligence applications of semiconductor backend. This study aims to develop a deep learning based fault diagnosis framework as prognostics and health management (PHM) solutions with a comprehensive analytics process. A linear encoder sensor is employed to measure position, while data preparation is conducted to convert position information into a signal. Then, signal processing is used to extract the features, in which high pass filter is applied to normalize the signal and emphasize the features. High-dimensional features including time domain statistical features, time-frequency domain features obtained by continuous wavelet transform (CWT), and frequency domain features converted by fast Fourier transform (FFT) are extracted to prepare the dataset. An ensemble neural network model that integrates deep neural network (DNN) and convolutional neural network (CNN) is employed to recognize the pattern and diagnose the positioning errors. The accuracy and false alarm rate are considered to support maintenance decisions. An empirical study was conducted in a world leading IC assembly and testing company for validation. The results have shown that the proposed approach can effectively detect inappropriate installation of the bond head in wire bonding equipment for predictive maintenance and cost reduction. © 1988-2012 IEEE.

关键词： Failure analysis

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