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Fully Distributed Model-Free Adaptive Sliding Mode Control for MASs With Hybrid-Attacked Topology

IEEE Transactions on Automation Science and Engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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A dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy

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Multimedia Tools and Applications 2025年 1-19页

作者： Su, Kainan Wang, Zunran Yang, Chenguang The Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510641 China Huawei Technologies Co Ltd Shenzhen518028 China

Dynamic point clouds can be compressed by eliminating spatial and temporal redundancy, but few research studies have considered both simultaneously. Existing research can only distinguish the specific foreground and background of the scene, achieving the former with a limited compression ratio, while the latter relies on 3D motion estimation with high computing complexity. Therefore, it is difficult to meet the real-time requirement of building remote human-robot interaction system. Aiming at the shortcomings of the above methods, a dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy is proposed. On the one hand, for eliminating the spatial redundancy, the information of the recognized objects in point cloud data will be replaced with their categories and poses. On the other hand, we eliminate the temporal redundancy by using the motion vector of the point cloud macroblock. Specifically, in order to reduce cost time of the 3D motion estimation, we firstly introduce the 2D image ORB feature to calculate the motion vector from current frame to key frame, and then look for the similar macroblock in key frame. Thus, the information of the macroblocks similar to the key frame in the current frame is replaced by the index of the corresponding macroblocks of the key frame and the motion vector. Experimental results show that the proposed method has higher computational efficiency and compression quality than the existing compression algorithm. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance

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IEEE Robotics and Automation Letters 2025年第6期10卷 5353-5360页

作者： Fang, Senlin Ding, Haoran Liu, Yangjun Liu, Jiashu Zhang, Yupo Li, Yilin Kong, Hoiio Yi, Zhengkun Faculty of Data Science City University of Macau 999078 China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China City University of Hong Kong Hong Kong University of Macau 999078 China Shenzhen Institute of Advanced Technology Shenzhen518055 China City University of Macau 999078 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System Shenzhen518055 China

Snap-fit peg-in-hole assembly is common in industrial robotics, particularly for 3 C electronics, where fast and accurate tactile recognition is crucial for protecting fragile components. Event-based optical sensors, such as Evetac, are well-suited for this task due to their high sparsity and sensitivity in detecting small, rapid force changes. However, existing research often converts event data into dense images and processing them with dense methods, leading to higher computational complexity. In this letter, we propose a Sparse Probabilistic Spiking Neural Network (SPSNN) that utilizes sparse convolutions to extract features from the event data, avoiding computations on non-zero cells. We introduce the Forward and Backward Propagation Through Probability (FBPTP) method, which enables simultaneous gradient computation across all time steps, eliminating the need for the step-by-step traversal required by traditional Forward and Backward Propagation Through Time (FBPTT). Additionally, the Temporal Weight Prediction (TWP) method dynamically allocates weights for different time outputs, enhancing recognition performance with minimal impact on model efficiency. We integrate the Evetac sensor compactly into our robotic system and collected two datasets, named Tactile Event Ethernet (TacEve-Eth) and Tactile Event Type-C (TacEve-TC), corresponding to cantilever and annular snap-fit structures. Experiments show that the SPSNN achieves the superior trade-off between recognition performance and efficiency compared to other widely used methods, achieving the highest average recognition performance while reducing inference time by over 90% compared to FBPTT-based dense SNN baselines. © 2016 IEEE.

关键词： Robotic assembly

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Hyperbolic Sine Function-Based Full-State Feedback Attitude Tracking Control for Rigid Spacecraft

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3412-3424页

作者： Dong, Rui-Qi Wu, Ai-Guo Li, Bin Duan, Guang-Ren Department of Automation Shenzhen518055 China Sichuan University School of Aeronautics and Astronautics Chengdu610207 China Southern University of Science and Technology Center for Control Science and Technology Shenzhen518055 China Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin150001 China

The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted closed-loop system can achieve two stable equilibria. By Lyapunov stability theory and Barbalat's Lemma, it is proven that the obtained closed-loop system is almost globally asymptotically stable, and achieves unwinding-free performance. Further, by constructing a strict Lyapunov function, it is demonstrated that the two stable equilibria are exponentially stable. Moreover, subsets of attraction regions corresponding to each stable equilibrium are characterized. The simulation results illustrate that the proposed attitude control scheme can effectively avoid the unwinding problem during attitude tracking. © 2025 IEEE.

关键词： Lyapunov functions

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Rapid stabilization of high-speed motion stage positioning oscillation using a vibration reduction unit

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Precision Engineering 2025年 95卷 89-98页

作者： Zhang, Lanyu Zhang, Shaoxuan Gao, Jian Zhang, Peiyuan Feng, Fuyuan Luo, Yuheng Liu, Yachao State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology Guangdong Guangzhou510006 China

A high-speed motion stage with less settling time is essential for most microelectronics manufacturing equipment. Usually, high-speed motion induces overshoot and inertial vibration during the stage positioning process. To rapidly reduce the stage positioning oscillation, this paper proposes a novel vibration-reduction-unit (VRU) for speeding up the positioning stabilization of the stage. More precisely, the VRU includes a servo actuator, and it is installed on the fixed-end of the stage with maintaining a small-gap from the displacement output-end of the stage. During the deceleration positioning process of the stage, the VRU is actuated with optimal start-up condition to actively act on the stage with an acting force and interfere the stage inertial vibration. This start-up condition including the acting moment of the VRU is determined based on the in-time displacement. The theoretical relationship between the VRU acting force and the attenuation of the stage inertial vibration amplitude is clarified based on the dynamics and force analyses. The effects of different VRU acting forces on stage vibration reduction are examined through simulations. A series of experiments are conducted, and the results show that the proposed VRU can help the stage to significantly reduce the vibration amplitude and settle down rapidly. © 2025 Elsevier Inc.

关键词： Vibration analysis

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Towards Target Sequential Rules

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3766-3780页

作者： Gan, Wensheng Huang, Gengsen Weng, Jian Gu, Tianlong Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China University of Illinois Chicago ChicagoIL60607 United States

In many real-world applications, sequential rule mining (SRM) can offer prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that can reveal the temporal relationship between objects. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on the problem of targeted mining. Targeted sequential rule mining aims to obtain those interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. It can further improve the efficiency of users in analyzing rules and reduce the consumption of computing resources. In this paper, we first present the relevant definitions of target sequential rules and formulate the problem of targeted sequential rule mining. Then, we propose an efficient algorithm called TaSRM. Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the baseline algorithm. © 1989-2012 IEEE.

关键词： Structured Query Language

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BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem

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IEEE Robotics and Automation Letters 2025年第6期10卷 5313-5320页

作者： Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Department of Automation Shanghai200237 China Chinese University of Hong Kong School of Data Science Shenzhen518172 China

We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full 6 °-of-freedom (DoF) pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, we embed BESTAnP into a single sonar-based odometry which shows its effectiveness for trajectory estimation. © 2016 IEEE.

关键词： Sonar

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Building Privacy-Preserving Medical Text Models With a Pretrained Transformer

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IEEE Internet of Things Journal 2025年第9期12卷 11529-11538页

作者： Shafiq, Muhammad Ren, Lijing Srivastava, Gautam Zhang, Denghui Bourouis, Sami Gadekallu, Thippa Reddy Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou511370 China Institute of Artificial Intelligence Guangdong Mechanical & Electrical Polytechnic Guangzhou510515 China Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Division of Research and Development Lovely Professional University Phagwara144411 India Center of Research Impact and Outcome Chitkara University Rajpura140401 India

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis. © 2014 IEEE.

关键词： Differential privacy

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Federated Incomplete Multi-view Clustering with Heterogeneous Graph Neural Networks

Federated Incomplete Multi-view Clustering with Heterogeneo...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Yan, Xueming Wang, Ziqi Jin, Yaochu Guangdong University of Foreign Studies Guangzhou China East China University of Science and Technology Shanghai China School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9783031822391

Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. However, current methods face challenges due to the absence of label information and the paramount importance of data privacy. A significant issue is the feature heterogeneity across multi-view data, which complicates the effective mining of complementary clustering information. Additionally, the inherent incompleteness of multi-view data in a distributed setting can further complicate the clustering process. To address these challenges, we introduce a federated incomplete multi-view clustering framework with heterogeneous graph neural networks (FIM-GNNs). In the proposed FIM-GNNs, autoencoders built on heterogeneous graph neural network models are employed for feature extraction of multi-view data at each client site. At the server level, heterogeneous features from overlapping samples of each client are aggregated into a global feature representation. Global pseudo-labels are generated at the server to enhance the handling of incomplete view data, where these labels serve as a guide for integrating and refining the clustering process across different data views. Comprehensive experiments have been conducted on public benchmark datasets to verify the performance of the proposed FIM-GNNs in comparison with state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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MA-SAM: A Multi-Atlas Guided SAM Using Pseudo Mask Prompts Without Manual Annotation for Spine Image Segmentation

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2157-2169页

作者： Fan, Dingwei Zhao, Junyong Li, Chunlin Wang, Xinlong Zhang, Ronghan Zhu, Qi Wang, Mingliang Si, Haipeng Zhang, Daoqiang Sun, Liang Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Shandong University Cheeloo College of Medicine Department of Orthopedics Qilu Hospital Jinan250012 China Shandong Normal University College of Physics and Electronics Jinan250358 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China Shandong Qingdao266035 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Shenzhen Research Institute Nanjing211106 China

Accurate spine segmentation is crucial in clinical diagnosis and treatment of spine diseases. However, due to the complexity of spine anatomical structure, it has remained a challenging task to accurately segment spine images. Recently, the segment anything model (SAM) has achieved superior performance for image segmentation. However, generating high-quality points and boxes is still laborious for high-dimensional medical images. Meanwhile, an accurate mask is difficult to obtain. To address these issues, in this paper, we propose a multi-atlas guided SAM using multiple pseudo mask prompts for spine image segmentation, called MA-SAM. Specifically, we first design a multi-atlas prompt generation sub-network to obtain the anatomical structure prompts. More specifically, we use a network to obtain coarse mask of the input image. Then atlas label maps are registered to the coarse mask. Subsequently, a SAM-based segmentation sub-network is used to segment images. Specifically, we first utilize adapters to fine-tune the image encoder. Meanwhile, we use a prompt encoder to learn the anatomical structure prior knowledge from the multi-atlas prompts. Finally, a mask decoder is used to fuse the image and prompt features to obtain the segmentation results. Moreover, to boost the segmentation performance, different scale features from the prompt encoder are concatenated to the Upsample Block in the mask decoder. We validate our MA-SAM on the two spine segmentation tasks, including spine anatomical structure segmentation with CT images and lumbosacral plexus segmentation with MR images. Experiment results suggest that our method achieves better segmentation performance than SAM with points, boxes, and mask prompts. © 1982-2012 IEEE.

关键词： Image segmentation

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