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2024 China automation Congress, CAC 2024

作者： Shi, Jinguang Kong, Xiangsong Li, Xiaoyu Yang, Zean Chen, Weiming School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

The steam generator level control system is one of the critical control systems in a nuclear power plant, and its performance is vital for the safe and stable operation of the plant. The control parameters are key factors that affect the operational performance of the steam generator level control system. Therefore, during the commissioning of the nuclear power plant, it is usually necessary to adjust the parameters through the trial-and-error method. This tuning process mainly relies on the experience of the commissioning personnel and may potentially compromise plant safety due to human errors. Considering that the parameter tuning process of control systems can be regarded as a special type of batch processes, this paper proposes to apply an efficient data-driven optimization method, multi-source hybrid data-driven optimization method based on model accuracy gradient estimation (MHDO-MAGE), to the problem of parameter tuning for the steam generator control system. This method effectively combines the advantages of traditional MBO (Model-Based Optimization) and MFO (Model-Free Optimization) methods, while efficiently utilizing prior data information related to the optimization problem. This method was implemented on a widely adopted steam generator control system model specifically designed for steam generator simulations. The simulation results clearly indicate that the MHDO-MAGE method significantly enhances the efficiency of parameter tuning for the steam generator level control system, ultimately leading to a reduction in the costs associated with nuclear power plant commissioning. © 2024 IEEE.

关键词： Nuclear power plants

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PMSM System Identification by Knowledge-informed Machine Learning 22

PMSM System Identification by Knowledge-informed Machine Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yu, Yue Tong, Jiageng Xia, Jinhui Su, Jinya Li, Shihua School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9798331527471

Because of its excellent efficiency, compact dimensions, and accurate control features, Permanent Magnet Synchronous Motor (PMSM) are experiencing widespread applications across various industries. By accurately characterizing the dynamic behavior of PMSM systems through system identification, engineers can ensure that PMSM motors reach their maximum potentials and meet the stringent requirements of modern industrial and technical systems while reducing energy consumption and maintenance costs. The traditional recursive least squares method is sensitive to noises, and unable to accurately identify parameters in complex environments. Pure data-driven models lack interpretability and require complex model architecture and computational costs. To this end, this work draws knowledge-informed neural ordinary differential equations (NODEs) for system identification, which embeds system prior knowledge into the NODEs for more efficient and accurate model learning. Comparative simulations show that this method not only obtains a higher-precision system model, but also significantly reduces the amount of training data and computation costs. © 2024 IEEE.

关键词： Cost reduction

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YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario 10

YOLO-MSP: Effective Cigarette Detection YOLO Network for Sur...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Zhiwei Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

ISBN: (纸本)9798331510138

This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We introduce a Multi-Spatial Pooling (MSP) bottleneck mechanism integrated into a YOLOv8 backbone, which significantly improves the network's ability to discern smaller objects without compromising real-time inference capabilities. This enhancement facilitates the extraction of relevant features across various scales and efficiently handles the computational overhead, thereby boosting the detection accuracy and operational efficiency. Our results demonstrate a substantial improvement in performance metrics over traditional and existing neural network approaches, making this method highly suitable for real-time applications in diverse scenarios. © 2024 IEEE.

关键词： Deep learning

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Multi-UAV Coverage Path Planning Method Based on Improved Particle Swarm Optimization

Multi-UAV Coverage Path Planning Method Based on Improved Pa...

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2024 China automation Congress, CAC 2024

作者： Zhou, Yan Xiong, Yonghua School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350368604

The widespread application of unmanned aerial vehicle (UAV) has made the coverage path planning problem for multiple UAV systems a research hotspot. In this paper, we propose a multi-UAV solution to achieve complete coverage of multi-region. Based on this solution, we present an improved particle swarm optimization (PSO) algorithm that combines PSO algorithm, greedy strategy, and back-and-forth (BAF) algorithm. Its purpose is to plan the optimal paths for multi-UAV while achieving the shortest total distance and the highest UAV cooperation efficiency with the minimum number of UAVs, thereby enhancing the multi-UAV coverage cooperation and path optimization capabilities. Simulation results demonstrate the feasibility and superiority of this algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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PWM Wave-Based Cooperative Vector control of Multiple Stepper Motors for Unmanned Surface Vehicles 7

PWM Wave-Based Cooperative Vector Control of Multiple Steppe...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Xu, Ruizhe Xu, Yihang Wu, Yongbao Liu, Jian Xue, Lei Southeast University Key Laboratory of Measurement And Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing China

ISBN: (纸本)9798350359145

This paper studies the control system of multiple stepper motors. Four stepper motors are selected to design the vector control system for the actual application scenario of the unmanned surface vehicles. This paper designs a PWM wave-based motor control algorithm taking into account the characteristics of stepper motors. We proposes a closed-loop control algorithm incorporating magnetic encoders. The system applies CAN bus to realize the communication among stepper motors, enabling distributed cooperative control four stepper motors of the unmanned surface vehicles. In this paper, the multiple stepper motor system is modeled and finally simulated and experimentally verified to achieve cooperative vector control of multiple stepper motors for surface vehicles. © 2023 IEEE.

关键词： Vectors

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SAR image data enhancement based on IF-SinConGAN 4

SAR image data enhancement based on IF-SinConGAN

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Mu, Ruizhe Qin, Yingmei Han, Chunxiao Che, Yanqiu Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781510686106

In order to solve the problem of insufficient samples of near-shore synthetic aperture radar data in ship detection, a ship synthetic aperture radar (SAR) image data augmentation model based on generative adversarial network was designed in this ***, this study combines image fusion and data enhancement to design an Image Fusion Concurrent-Single-Image-GAN model (IF-SinConGAN).This model first fuses offshore ship images with nearshore scenes, employing a dual-threshold sea-land segmentation method to seamlessly integrate offshore ships into nearshore water regions. These fused images are then used as input for training the ConSinGAN model. Compared to the original model, IF-SinConGAN significantly improves both the diversity and quality of generated SAR images. © 2024 SPIE.

关键词： Synthetic aperture radar

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Learning Object Spatial Relationship from Demonstration 2

Learning Object Spatial Relationship from Demonstration

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2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Yu, Tianhao Zhou, Zhongxiang Chen, Yinghao Xiong, Rong School of Automation Hangzhou Dianzi University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350328530

Object spatial relationship refers to the use of predicates to describe the relative positional relationship between two objects. For robots, understanding the spatial relationships between objects in scenes can contribute more robustness to the interaction process between robots and the environment in a closed-loop way. Traditional methods typically use models to learn 2D features of images to determine spatial relationships between objects, which has certain limitations in understanding 3D spatial relationships. In this paper, first, we propose a method to learn object spatial relationships from human demonstration, which utilizes spatial probability models to characterize spatial relationships between objects. Second, in order to facilitate the addition of understanding of new spatial relationships, we use a monocular 3D object detection method to construct new probability distribution models of spatial relationships from image and language demonstrations. Finally, we use SVM model to distinguish different spatial relationships and recognize spatial relationships between objects in images. We test the proposed method using the NUSCENES dataset and tabletop scenes to confirm its efficacy. The experimental results show that our method acquire high spatial relationship recognition accuracy and has the potential to enable robot manipulation more robust. © 2023 IEEE.

关键词： Demonstrations

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A Review of Fault Diagnosis and Remaining Useful Life Prediction of High-Speed Train Bearings 15

A Review of Fault Diagnosis and Remaining Useful Life Predic...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Zheng, Yiwei Yan, Fei School of Automation and Intelligence Beijing Jiaotong University Beijing China Urban Rail Transit Beijing Laboratory National Engineering Research Centre of Rail Transportation Operation and Control Systems School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350354010

With the rapid expansion of China's high-speed railway network, the safety and dependability of train bearings, as vital components, are critical to the overall stability of the transportation system. This paper overviews the most recent developments in train bearing remaining useful life (RUL) prediction and fault diagnosis research. First, the significance of train bearings and the state of China's high-speed rail system are discussed. The structural properties of train bearings and failure modes are then thoroughly examined. The vibration signal-based fault diagnostic approaches covered in this study include both conventional signal analysis techniques and intelligent approaches like deep learning and machine learning. Furthermore, train bearing RUL prediction strategies are examined, including physical model-based, data-driven, and hybrid models. Finally, the paper discusses the challenges that current research faces, such as the limitations of data quality and quantity, the explainability of deep learning models, the complexity of fault diagnosis, and RUL prediction under variable operating conditions, and looks to the future. This study intends to give scientific basis and technical support for fault detection and RUL prediction of train bearings in rail transit by conducting an in-depth review of present technologies and discussing future difficulties. © 2024 IEEE.

关键词： Railroads

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AN IMPROVED APF METHOD FOR SAFETY control OF MOBILE MANIPULATOR IN HUMAN-ROBOT COEXISTENCE ENVIRONMENT

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International Journal of Robotics and automation 2025年第3期40卷 184-193页

作者： Ye, Jinhua Hong, Linxin Wu, Haibin Zheng, Gengfeng School of Mechanical Engineering and Automation Fuzhou University Fuzhou350116 China Fujian Provincial Special Equipment Inspection and Research Institute Fuzhou350003 China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fuzhou350003 China

Technology is nudging robot into automation and intelligence field, making dynamic collision avoidance indispensable in robotics. Aiming at safety control of mobile manipulator in human-robot coexistence environment, a novel artificial potential field based on danger index (DIAPF) approach is proposed. Firstly, hinged on the kinematic and dynamic model of mobile manipulators, a danger index system is established to take precautions against accident during the operation. We further improve the repulsion field and velocity repulsive field to promote dynamic obstacle avoidance in the human-robot coexistence environments. Feasibility and effectiveness of the proposed method are verified in simulation environment. © 2025 Acta Press. All rights reserved.

关键词： Social robots

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