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26th International Conference on Mechatronics Technology, ICMT 2023

作者： Tran, Duc Thien Nguyen, Thanh Nha Nguyen, Xuan Tra Nguyen, Duc Manh Truong, Quoc Thanh Trinh, Hoai An Nguyen, Manh Hung Ahn, Kyoung Kwan Ho Chi Minh City University of Technology and Education Automation Control Department Ho Chi Minh City70000 Viet Nam Hcmc University of Technology Faculty of Mechanical Engineering Ho Chi Minh City70000 Viet Nam School of Mechanical and Automotive Engineering University of Ulsan Ulsan44610 Korea Republic of

ISBN: (纸本)9798350381429

This paper proposes a sliding mode control (SMC) with a time delay estimation (TDE) for a 4-DOF parallel manipulator with modeling error and a variant payload in practice. The proposed control is constructed from a sliding mode control and a time delay estimator. The sliding mode control is designed to guarantee the tracking objective of the parallel robot. Additionally, the TDE is utilized to compensate for the lumped uncertainty. By incorporating these techniques in the proposed control, the proposed method not only guarantees stability but also enhances the accuracy of the tracking performance in the parallel robot against the lumped uncertainty. The stability and robustness of the closed-loop control system are evaluated by the Lyapunov approach in theory. Finally, some experiments are conducted on the 4-DOF parallel robot testbench. The experimental results of the proposed control are compared to other methods to verify its superiorities. © 2023 IEEE.

关键词： Sliding mode control

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SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

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Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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Developing a Window control Algorithm Based on Reinforcement Learning for Indoor PM2.5 Mitigation 5th

Developing a Window Control Algorithm Based on Reinforcement...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： An, Yuting Xia, Tongling You, Ruoyu Lai, Dayi Liu, Junjie Chen, Chun Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China Department of Building Services Engineering The Hong Kong Polytechnic University China School of Design Shanghai Jiao Tong University Shanghai China Tianjin Key Lab of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9789811998218

Smart control of window is a means of effectively reducing concentrations of indoor PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) in naturally ventilated residential buildings without air cleaning devices. This study aimed to develop a reinforcement learning (RL) approach to automatically control window behavior in real time for mitigation of indoor PM2.5 pollution. The method trains the window controller using deep Q-network (DQN) in a specific apartment for a month. The trained controller can be employed to control window behavior to mitigate indoor PM2.5 in that apartment. The input data for the controller are the real-time indoor and outdoor PM2.5 concentrations with 1-min resolution. Simulation was conducted in a real apartment in Tianjin. The results show that, the RL algorithm reduced the average indoor PM2.5 concentration by 9.11% when compared with the I/O ratio algorithm and by 7.40% when compared with real window behavior. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Unmanned Aerial Vehicles for Inspection of Power Lines

Unmanned Aerial Vehicles for Inspection of Power Lines

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Naumov, I.I. Ibadov, R.R. Oboimova, S.P. Don State Technical University Faculty of Automation Mechatronics and Control Rostov-on-Don Russia Don State Technical University Department of Electrical Engineering and Electronics Rostov-on-Don Russia

ISBN: (纸本)9798331511241

Problem Statement. New unmanned aerial vehicle (UAV) technology has become more accessible and practical for transmission line inspection. In this paper, we propose a multi-platform UAV system and a multi-model communication system designed for high-efficiency transmission line inspection tasks. Different UAVs perform both long-range and short-range surveillance functions, and provide communication relay. High-quality images and videos are transmitted in real time to the local control station for UAV navigation and to the remote office for analysis. Practice shows that joint inspection using multiple UAVs can significantly outperform traditional inspection methods. Objective. To propose a new method for inspecting and checking the condition of power lines using several types of UAVs using certain signal transmission and relay methods. Results. A new concept of power line inspection using joint actions of unmanned aerial vehicles (UAVs) is proposed. The functional capabilities of the UAVs and the communication system are considered and discussed. Practical experience shows that due to joint work, the time spent on inspection can be significantly reduced and the efficiency can be significantly increased. The feasibility and advantages of inspections using UAVs are confirmed. Future work can focus on optimizing the configuration of UAVs as part of a group. Practical significance. The presented method allows increasing the efficiency of inspection and testing of power transmission lines in comparison with existing methods. © 2025 IEEE.

关键词： Drones

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A Novel Robust Broad Nonlinear Representation CVA Method for Monitoring Blast Furnace Iron-making Process

A Novel Robust Broad Nonlinear Representation CVA Method for...

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2022 Chinese automation Congress, CAC 2022

作者： Yang, Yuelin Yang, Chunjie Yang, Bo Chen, Yu Lou, Siwei Zhu, Xiongzhuo Zhejiang University College of Control Science and Engineering Hangzhou China Tsinghua University Department of Automation Beijing China Jiangsu Binxin Iron and Steel Group Co. Ltd. Lianyungang China

ISBN: (纸本)9781665465335

Blast furnace iron-making process is one of the most crucial parts in iron and steel industry. Due to many interference factors and a series of complex physical and chemical reactions, abnormal furnace conditions often occur. The nonlinear characteristics hidden in the blast furnace data and the existence of large noise and outliers make it difficult to establish an effective monitoring model. In this paper, a novel robust broad nonlinear representation canonical variate analysis (RBNCVA) method is proposed to overcome the above problems. First, a feature extraction strategy is developed to extract robust broad nonlinear features based on stacked denoising autoencoder (SDAE). The robust broad nonlinear features can assist the model to cope with the complex nonlinearity and resist the interference of noise and outliers. Then canonical variate analysis (CVA) method is used to analyze the relationship between past and future feature vectors. Subsequently, control limits are computed through probability density functions defined by kernel density estimation. Finally, the practical blast furnace data is adopted to validate the effectiveness and robustness of the proposed method. © 2022 IEEE.

关键词： Process monitoring

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Vision-Based Autonomous Landing of a Quadrotor UAV on a Double-Layered Nested Landing Marker

Vision-Based Autonomous Landing of a Quadrotor UAV on a Doub...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Mu, Lingxia Zhang, Yuan Xin, Jing Zhang, Youmin School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical and Industrial Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9789811966125

This paper focuses on the vision-based autonomous landing mission of a quadrotor unmanned aerial vehicle (UAV). A double-layered nested Aruco landing marker is designed which can adapt to the situation that the field of sight of airborne camera changes as the UAV’s altitude decreases during the landing. Then, considering that the landing marker might be obstructed in certain situation, traditional Aruco detection method cannot effectively recognize marker. A YOLOv4 deep learning algorithm is used for marker detection. In addition, to address the problem of false detection in some test results, normalized Wasserstein distance is used to replace the non-maximum suppression to deal with redundant frames. The UAV’s relative location to the marker is then calculated and landing mission is finally achieved by the image-based visual servoing strategy. The simulation on Gazebo demonstrates that the designed marker is effective for the UAV landing mission and the revised YOLOv4 detection algorithm can increase detection results accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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Future Energy Solutions (FES), International Conference on

作者： Amirhossein Akbarian Mahdi Bahrami Mehdi Vakilian Matti Lehtonen Electrical Engineering Department Center of Excellence in Power System Management and Control Sharif University of Technology Tehran Iran Department of Electrical Engineering and Automation Aalto University Espoo Finland

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration; in well developed countries; due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers’ perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users’ response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account.

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Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-Angle CT Image Reconstruction 2022

Frequency Domain Filtering Based Compressed Sensing Applied ...

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7th International Conference on Multimedia and Image Processing, ICMIP 2022

作者： Dong, Jian Chen, Hao Yang, Xiaoxia Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Department of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781450387408

In the process of CT scanning, multi-Angle projection data needs to be obtained from a large number of projection actions, which makes the scanned individual bear the risk of high radiation exposure. In order to solve such problems, the use of sparse projection data for CT image reconstruction is proposed as a new type of solution. The previous research can obtain good quality reconstructed images when the projection data is sparse by using the CT reconstruction technology based on the nonlinear sparsity transformation of compressed sensing. However, the heavy time loading of the image reconstruction is a practical problem that needs to be solved urgently. This study optimizes the non-linear filtering process of the regularization term of the original scheme, and proposes a novel method which replaces the original non-linear filter with a low-pass frequency domain filter. This strategy effectively utilizes the properties of low-pass frequency domain filtering in image processing. The excellent properties include high efficiency and low time complexity for image smoothing. The simulation experiment results show that in the process of CT image reconstruction using compressed sensing algorithm, the low-pass frequency domain filtering of the new scheme can greatly reduce the required time in the reconstruction of sparse projection data, and the image quality is feasibly guaranteed. © 2022 ACM.

关键词： Compressed sensing

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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