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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 61-69页

作者： Zhao, Xudong Xing, Wei Zhang, Junfeng Wang, Xinyu Zong, Guangdeng Zhao, Ning Hainan University School of Information and Communication Engineering Haikou570288 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116622 China Tiangong University School of Control Science and Engineering Tianjin300387 China Bohai University College of Control Science and Engineering Jinzhou121013 China

Cyber-physical systems (CPSs) are of great importance in various applications and have garnered significant attention. Ensuring their security is a crucial concern, while this paper focuses on the security of CPSs against replay attacks during the transmission of sensor measurements over a wireless network. It is widely recognized that incorporating physical watermark signals is an effective approach to mitigate replay attacks. However, traditional physical watermarks are time-based and result in a notable degradation in the performance of Linear Quadratic Gaussian systems. With this in mind, a novel event-based physical watermarking technique is devised to address the performance degradation and detect replay attacks. The approach involves a deterministic physical watermark schedule that utilizes the innovation sequence to determine whether to incorporate physical watermarks or not. We provide evidence that the probability of successfully embedding physical watermarks increases when the system is targeted by malicious attacks. Additionally, we demonstrate that the deterministic event-based watermarking approach is more effective than the traditional time-based watermarking method. Finally, simulation examples are included to validate and illustrate the theoretical results. © 2023 IEEE.

关键词： Cyber attacks

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Signal Temporal Logic Based Vehicle Trajectory Planning Under Mandatory Stop Rule for Heavy Vehicle Right Turns at Signal-Free Intersections 27

Signal Temporal Logic Based Vehicle Trajectory Planning Unde...

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

作者： Zuo, Wanqing Xu, Yunwen Chen, Youren Li, Dewei School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798331505929

The blind spots of heavy vehicles during right turns arise a safety concern for traffic. Hence, the regulation mandating heavy vehicles to come to a complete stop before making right turns is crucial in ensuring intersection safety. However, the current trajectory planning methods struggle to integrate these traffic regulations, and the restrictions imposed by such traffic rules greatly impact traffic efficiency at intersections. This paper proposes a signal temporal logic (STL) based trajectory planning method for connected and autonomous vehicles (CAVs) at a signal-free intersection, where traffic rules for heavy vehicles are conveniently described by STL formulas. All CAVs at the intersection are modeled without predefined paths to enhance the flexibility of trajectories. Each STL formula is encoded in a standardized way as a conjunction of inequality and equality constraints for the optimization, and the whole trajectory planning of CAVs is achieved through solving a mixed-integer nonlinear programming (MINLP) problem. Simulation results indicate that our method improves traffic efficiency in the scenario with heavy vehicles right turns. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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Federated Multitask Learning for HyperFace

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 788-797页

作者： Zhang, Chen Bai, Hang Zhang, Yonggang Niu, Xinyi Yu, Bin Gao, Yuan Xie, Yu Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Province Science and Technology Resources Coordination Center Xi'an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xi'an710071 China Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan030006 China

Multitask learning (MTL) is a promising field in machine learning owing to its capability to improve the generalization performance of all tasks by sharing knowledge among the related tasks. MTL has attracted a large amount of attention in the multitask-related community. In recent years, with the rapid development of distributed machine learning, MTL in a distributed environment becomes a hot research topic. Although MTL can reap great fruit in a distributed environment, the sensitive information, such as photos and voice recordings of the owner, in distributed data may be leaked during training. Consequently, in the distributed settings, one prominent challenge of MTL is to prevent the disclosure of sensitive private data. In this article, we propose a novel approach, federated multitask learning (FMTL), which applies federated learning to MTL in a distributed environment in order to protect the MTL model from being leaked and to better optimize the utility of the MTL model. In FMTL, all the participants independently train their local models on their own dataset in parallel and only transmit their model updates to the central server for aggregation at every epoch. As a result, their learning accuracy is enhanced beyond what is achieved only on their own training data. In this manner, it can achieve the best tradeoff between the utility and privacy: participants not only preserve the privacy of their own data, but also benefit from the models of other participants. The experimental results on annotated facial landmarks in the wild (AFLW) and annotated faces in the wild (AFW) benchmark datasets verify the effectiveness of our framework. © 2020 IEEE.

关键词： Linearization

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Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 104-114页

作者： Lin, Zhijie He, Zhaoshui Yao, Ruoyu Wang, Xu Liu, Taiheng Deng, Yamei Xie, Shengli Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Third Affiliated Hospital of Guangzhou Medical University Department of Radiology Guangzhou510150 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Automatic diagnosis of Coronavirus disease 2019 (COVID-19) using chest computed tomography (CT) images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network ($\text {D}^{\text image}\text {ANet}$) is proposed for accurate diagnosis of COVID-19 by integrating dual attention modules (DAMs) with different scales of the feature extractor, where DAM can adaptively detect relevant lesion regions to extract discriminative imaging features of COVID-19. Specifically, DAM is implemented by two parallel blocks: 1) global attention block (GAB) and 2) local attention block (LAB), in which GAB is designed to roughly locate the infected regions from the entire image by modeling global contexts while LAB is developed to explicitly highlight subtle differences of COVID-19 from other viral pneumonia in the infected regions by learning detailed lesion information. Experimental results on several public datasets show that $\text {D}^{\text image}\text {ANet}$ outperforms the state-of-the-art approaches in various performance metrics. © 2020 IEEE.

关键词： COVID-19

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Simulation Research on Generalized Proportional Integral Observer Based Active Disturbance Rejection control for Coarse Pointing Assembly 8

Simulation Research on Generalized Proportional Integral Obs...

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

作者： Ma, Jijun Guo, Yulong Wang, YuNing Shi, Yongkang Beijing Research Institute of Telemetry Beijing100074 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Research Institute of Telemetry Beijing China

ISBN: (纸本)9798331506230

The coarse pointing assembly (CPA), as the outer loop of the laser terminal, its tracking stability is the basis for ensuring laser communication. This paper presents the model of the CPA. Aiming at the disturbance factors in the low-speed operation process of the CPA, an active disturbance rejection control (ADRC) method for the position loop of the CPA is designed based on the generalized proportional integral observer (GPIO) to achieve disturbance compensation control. Finally, simulations are carried out on the system model. The simulation results show that compared with the PID control strategy adopted, the proposed ADRC strategy based on GPIO of the CPA can effectively improve the tracking accuracy. © 2024 IEEE.

关键词： Disturbance rejection

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Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition 41

Multi-Branch Spatial-Temporal Attention Graph Convolution Ne...

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第41届中国控制会议

作者： Daoshuai Wang Dewei Li Yaonan Guan Gang Wang Haibin Shao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Skeleton-based human action reco gnition has attracted considerable research interest due to its robustness to dynamic environments and complex *** based on graph neural networks have achieved great success in this ***,most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input *** to the separation of space and time,many methods cannot pay attention to the critical information of human skeleton sequence,and multi-scale spatial-temporal dependent attention modeling becomes the key to *** paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network(MB-STAGCN) to recognize human actions from skeleton *** proposed model adopts a multi-branch structure,fuses three-stream features,and then inputs them into the backbone network to extract features from multiple perspectives to a greater ***,we design a new temporal convolutional block for multi-scale extraction of information in the backbone *** also propose an attention block named Spatio-temporal Concat Attention(STCatAtt) to capture critical *** on benchmark datasets show remarkable performance for human action reco gnition,demonstrating the effectiveness of our method.

关键词： Skeleton-based human action recognition MB-STAGCN STCatAtt

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Process Monitoring Based on Temporal Feature Agglomeration and Enhancement

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 825-827页

作者： Xiao Liang Weiwu Yan Yusun Fu Huihe Shao Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation the School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

Dear Editor,This letter proposes a process-monitoring method based on temporal feature agglomeration and enhancement,in which a novel feature extractor called contrastive feature extractor(CFE)extracts the temporal and relational features among process *** the feature representations are enhanced by maximizing the separation among different classes while minimizing the scatter within each class.

关键词： among while letter

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