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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-17页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China James Watt School of Engineering University of Glasgow Glasgow U.K State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. IEEE

关键词： Trajectories

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PCEB-YOLOV8: An Improved PCB Defect Detection Method Based on YOLOV8 8

PCEB-YOLOV8: An Improved PCB Defect Detection Method Based o...

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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Li, Meihui Song, Chunhe Yu, Xia Chu, Mengmeng Li, Rui Shenyang University of Technology State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Shenyang China Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331506230

Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches 92.6%, with a 2.3% increase in accuracy. © 2024 IEEE.

关键词： Deep learning

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A Comprehensive Review of Humanoid Robots

SmartBot

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SmartBot 2025年第1期1卷 86-120页

作者： Qincheng Sheng Zhongxiang Zhou Jinhao Li Xiangyu Mi Pingyu Xiang Zhenghan Chen Haocheng Xu Shenhan Jia Xiyang Wu Yuxiang Cui Shuhao Ye Jiyu Yu Yuhan Du Shichao Zhai Kechun Xu Yifei Yang Zhichen Lou Zherui Song Zikang Yin Yu Sun Rong Xiong Jun Zou Huayong Yang State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhouChina State Key Laboratory of Industrial Control Technology School of Control Science and EngineeringZhejiang UniversityHangzhouChina

Humanoid robots,increasingly recognized for their potential to drive economic and social development,have garnered significant attention in recent *** paper aims to provide a comprehensive overview of the progress,challenges,and future directions in humanoid robotics,with a particular emphasis on essential system components and key technological *** a review of historical milestones,this paper explores critical aspects such as the design of the head and body,and examines state-of-the-art technologies in areas like locomotion control,perception,and intelligent *** presenting a thorough analysis of the field,this work aims to serve as a valuable resource for researchers and inspire future innovations that will drive the continued evolution of humanoid robots.

关键词： challenges and trends humanoid robot humanoid robot components key technologies

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Ultrasonic enhancement of persulfate oxidation system governs emerging pollutants decontamination

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Green Energy & Environment 2024年第11期9卷 1666-1678页

作者： Yanpan Li Yanbo Zhou Yi Zhou National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical ProcessEast China University of Science and Technology130 Meilong RoadShanghai200237China Shanghai Engineering Research Center for Multi-media Environmental Catalysis and Resource Utilization East China University of Science and Technology130 Meilong RoadShanghai200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092China

Emerging contaminants (ECs) are widely present in aquatic environments, posing potential risks to both ecosystems and human health. Theultrasound-assisted persulfate oxidation process has attracted considerable attention in the degradation of ECs due to its ability to generate bothsulfate radicals and cavitation effects, enhancing degradation effects. In this paper, the principle of ultrasonic synergistic Fenton-like oxidationsystem for degrading organic pollutants was reviewed, divided into homogeneous system, non-homogeneous system, and single-atom system toexplore the synergistic effect of ultrasound-enhanced persulfate technology in three aspects, and the effects of environmental factors such asultrasonic frequency and power, system pH, temperature, and initial oxidant concentration on the system's decontamination performance werediscussed. Finally, future research on ultrasonically activated persulfate technology is summarized and prospected.

关键词： Ultrasound Persulfate Emerging contaminants Reaction mechanism Synergistic effects

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Disturbance Observer-based Model Predictive control for Discrete-time Linear systems with Bounded Disturbances 41

Disturbance Observer-based Model Predictive Control for Disc...

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第41届中国控制会议

作者： Mengzhi Wang Bing Liu Jichao Bi Kaixiang Yang Jingpei Wang Nanwei Yao State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Institute of Industry and Information Technology China Academy of Information and Communications Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This work develops a disturbance rejection model predictive control(MPC) algorithm for discrete-time linear systems with bounded disturbances.A disturbance observer is designed to estimate the unknown matched disturbances with bounded incremental constraint.A tube-based MPC algorithm is presented to handle the system constraints.A composite controller is presented to stabilize the systems,including a MPC controller to drive the system state into the neighborhood of the origin,and a disturbance estimation-dependent controller to compensate for the effect of external *** feasibility of the disturbance rejection MPC is analyzed,and the stability of the system can be *** results show the effectiveness of the proposed methods.

关键词： Model predictive control Disturbance observer Bounded disturbances

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning 42

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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42nd Chinese control Conference, CCC 2023

作者： Khan, Malak Abid Ali Ma, Hongbin Jin, Ying Ma, Jingxiang Rehman, Zia Ur Rahman, Mizanur School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deforestation

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An Enhanced Distributed Optimization Subject to Multiple Constraints: A Case Study on Entire Ethylene Separation Process

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IEEE Transactions on control of Network systems 2025年

作者： Liu, Bing Du, Wenli Li, Zhongmei East China University of Science and Technology State Key Laboratory of Industrial Control Technology Ministry of Education Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China

This paper studies an enhanced distributed optimization algorithm in an undirected topology based on local communication and computation to optimize the sum of local objective functions under multiple constraints. In particular, the adapt-then-combine distributed inexact gradient tracking algorithm (ATC-DIGing-MC) is developed for smooth and strongly convex local functions with multiple constraints. By implementing the adapt-then-combine scheme and using the gradient tracking technique, rapidity and flexibility can be obtained by the ATC-DIGing-MC with uncoordinated step sizes. Subsequently, the parameter projection strategy is implemented to deal with realistic production limitations in the presence of multiple constraints. Meanwhile, rigorous theoretical proofs and convergence theorem are provided to verify the geometrical convergence rate of the ATC-DIGing-MC. Furthermore, the numerical experiments based on the entire ethylene separation process validate the superior performance of the ATC-DIGing-MC algorithm in terms of time and energy savings compared to established algorithms. © 2025 IEEE.

关键词： Optimization algorithms

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A Reinforcement Learning-Based Detection Method for False Data Injection Attack in Distributed Smart Grid

A Reinforcement Learning-Based Detection Method for False Da...

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2021 International Conference on Information, cybernetics, and Computational Social systems, ICCSS 2021

作者： Zhang, Kuo Wu, Zheng-Guang Zhejiang University Institute of Cyber-Systems and Control Hangzhou China Zhejiang University The State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9781665402453

False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA. © 2021 IEEE.

关键词： Reinforcement learning

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

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IET cyber-systems and Robotics 2022年第3期4卷 200-211页

作者： Yiwei Chong Jiaming Liang Tehuan Chen Chao Xu Changchun Pan College of Control Science and Engineering Zhejiang UniversityHangzhouZhejiangChina School of Mechanical Engineering and Mechanics Ningbo UniversityNingboZhejiangChina Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingboZhejiangChina The State Key Laboratory of Industrial Control Technology Institute of Cyber‐Systems and ControlZhejiang UniversityHangzhouZhejiangChina School of Electronic Information and Electrical Engineering Key Laboratory of Shanghai Navigation and Location Based ServicesShanghai Jiao Tong UniversityShanghaiZhejiangChina

Particle image velocimetry(PIV)is an essential method in experimental fluid *** recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of ***,the supervised learning of PIV is driven by large volumes of data with ground truth ***,the authors consider unsupervised PIV *** has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning *** authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical *** this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV *** authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic ***,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle *** a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

关键词： neural network particle image velocimetry unsupervised learning

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