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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

arXiv

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arXiv 2025年

作者： Zhang, Zejia Yang, Bo Chen, Xinxing Shi, Weizhuang Wang, Haoyuan Luo, Wei Huang, Jian Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China State key laboratory of intelligent vehicle safety technology chongqing changan automobile co ltd Chongqing400023 China Science and technology innovation center China ship development and design centre Wuhan430060 China

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system’s ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user’s intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task. Copyright © 2025, The Authors. All rights reserved.

关键词： Eye movements

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GooDFlight: Goal-Oriented Diffusion Model for Flight Trajectory Prediction

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Yang, Songru Liu, Liqin Chen, Bowen Cheng, Shaoyuan Shi, Zhenwei Zou, Zhengxia Beihang University Department of Aerospace Intelligent Science and Technology School of Astronautics China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Beijing Institute of Space Mechanics and Electricity Beijing100076 China

Flight trajectory prediction is an essential task in the air traffic control field. Previous approaches to this problem usually follow a single-stage or short-term intention-guided prediction paradigm, which suffers from problems such as insufficient trajectory prediction diversity, limited accuracy and interpretability. Different from existing paradigms, in this paper, we present GooDFlight - A Goal-oriented Diffusion Model for Flight trajectory prediction. GooDFlight is a long-term intention-guided, diversity-emphasizing framework that decouples the flight trajectory prediction process into two stages: goal estimation and trajectory prediction. In the first stage, we propose a One-then-all goal estimation method to sufficiently cover the macro-uncertainty in flight patterns and then tailor the interaction-aware joint goal distribution, which extends the flight intention from a single, deterministic ground truth to the empirical intention distribution from the similar experience. In the second stage, we employ a transformer-based diffusion model to generate stochastic flight trajectories conditioned on the intention estimations, modeling the micro-uncertainty in flight operations under each pattern estimated in stage one. In terms of evaluation metrics, existing metrics have difficulties in accurately reflecting the model's ability to handle the natural uncertainty of trajectories. We further propose a simple yet effective Global-local endpoints Variance (GLeV) metric for evaluating the diversity of predicted trajectories under social acceptance. Our proposed method is validated in-depth on TrajAir, a large-scale dataset collected from the real-world air traffic control environment at the Pittsburgh-Butler Regional Airport, a non-towered general aviation airport. The experimental results demonstrate that the proposed method significantly outperforms other methods in terms of both accuracy and diversity with superior interpretability. © 1965-2011 IEEE.

关键词： Stochastic systems

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Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids

arXiv

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arXiv 2025年

作者： Liu, Wei Zhang, Tao Lin, Chenhui Li, Kaiwen Wang, Rui College of System Engineering the National University of Defense Technology the Xiangjiang Laboratory the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China

Independent microgrids are crucial for supplying electricity by combining distributed energy resources and loads in scenarios like isolated islands and field combat. Fast and accurate assessments of microgrid vulnerability against intentional attacks or natural disasters are essential for effective risk prevention and design optimization. However, conventional Monte Carlo simulation (MCS) methods are computationally expensive and time-consuming, while existing machine learning-based approaches often lack accuracy and explainability. To address these challenges, this study proposes a fast and explainable vulnerability assessment framework that integrates MCS with a graph attention network enhanced by self-attention pooling (GAT-S). MCS generates training data, while the GAT-S model learns the structural and electrical characteristics of the microgrid and further assesses its vulnerability intelligently. The GAT-S improves explainability and computational efficiency by dynamically assigning attention weights to critical nodes. Comprehensive experimental evaluations across various microgrid configurations demonstrate that the proposed framework provides accurate vulnerability assessments, achieving a mean squared error as low as 0.001, real-time responsiveness within 1 second, and delivering explainable results. Copyright © 2025, The Authors. All rights reserved.

关键词： Microgrids

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Multi-order attributes information fusion via hypergraph matching for popular fashion compatibility analysis

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Expert systems with Applications 2025年 263卷

作者： Sun, Kexin Zhao, Zhiheng Li, Ming Huang, George Q. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Research Institute for Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China

Popular fashion compatibility modeling aims to quantitatively assess the compatibility of a set of wearable items for everyday pairings and clothing purchases to assist human decision-making, which has garnered extensive academic attention. Earlier approaches that studied garments as a whole could only discern loose relationships between items, resulting in low accuracy and poor interpretability. Although existing state-of-the-art methods attempt to reveal the mechanism of garment compatibility at a fine-grained level by quantifying pairwise attribute compatibility, they overlook the fact that multi-attribute combinations between apparel items tend to play a more salient role in compatibility. Considering the complex and high-order characteristics of compatibility data, we propose a network named MAIF to deeply mine and reveal the intricate compatibility mechanisms of clothing by fusing multi-order attributes compatibility information through hypergraph matching. Specifically, we use the compatibility modeling of top-item and bottom-item as an example. First, we construct an adaptive hypergraph representation module to model the multi-attribute association combinations of individual clothing items and fuse single-attribute variable information to form multi-order attribute association information. Second, we learn the multi-order compatibility information of attributes between clothing items through spatial similarity matching. Considering the varying compatibility impacts caused by different attribute combinations, we construct a dynamic cross-plot matching mechanism to model the impact weights of multi-order attribute compatibility information. Finally, personalized ranking loss is designed to optimize the model parameters using fashion context information. Experimental and user survey studies conducted on the FashionVC and Polyvore-Maryland datasets verified the validity and superiority of MAIF in accurately assessing apparel compatibility, demonstrating its abil

关键词： Information fusion

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Large-scale high uniform optoelectronic synapses array for artificial visual neural network

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Microsystems & Nanoengineering 2025年第1期11卷 247-256页

作者： Fanqing Zhang Chunyang Li Zhicheng Chen Haiqiu Tan Zhongyi Li Chengzhai Lv Shuai Xiao Lining Wu Jing Zhao State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of TechnologyMinistry of Education100081 BeijingChina School of Mechatronical Engineering Beijing Institute of Technology100081 BeijingChina Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology100081 BeijingChina Laser Micro/Nano Fabrication Laboratory School of Mechanical EngineeringBeijing Institute of Technology100081 BeijingChina School of Mechanical Engineering Beijing Institute of Technology100081 BeijingChina

Recently,the biologically inspired intelligent artificial visual neural system has aroused enormous ***,there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and *** this study,we demonstrate a 28×28 synaptic devices array for the artificial visual neuromorphic system,within the size of 0.7×0.7 cm 2,which integrates sensing,memory,and processing *** highly uniform floating-gate synaptic transistors array were constructed by the wafer-scale grown monolayer molybdenum disulfide with Au nanoparticles(NPs)acting as the electrons capture *** synaptic plasticity behaviors have been achieved owing to the switchable electronic storage *** excellent optical/electrical coordination capabilities were implemented by paralleled processing both the optical and electrical signals the synaptic array of 784 devices,enabling to realize the badges and letters writing and erasing ***,the established artificial visual convolutional neural network(CNN)through optical/electrical signal modulation can reach the high digit recognition accuracy of 96.5%.Therefore,our results provide a feasible route for future large-scale integrated artificial visual neuromorphic system.

关键词： optoelectronic synapses monolayer molybdenum disulfide artificial visual neural system convolutional neural network artificial visual neuromorphic systemwithin artificial visual neural network floating gate transistors synaptic plasticity

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Leader-Following Adaptive Tracking Control for A Class of Uncertain Discrete-Time Nonlinear Multi-Agent systems Under Event-Triggered Communication

引用

Journal of systems Science and Complexity 2025年第2期38卷 756-781页

作者： Xu, Yuchun Zhang, Yanjun Zhang, Ji-Feng State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100149 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China School of Automation and Electrical Engineering Zhongyuan University of Technology Zhengzhou450007 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100149 China

This paper studies the leader-following adaptive tracking control problem for multi-agent systems comprising a leader agent and N follower agents with uncertain nonlinear dynamics. Specifically, a novel event-triggered communication based adaptive distributed observer is developed to enable each follower agent to estimate the leader’s information. Then, new forms of adaptive control law and parameter update law are designed with the estimated leader’s signals. The developed distributed adaptive control strategy has several characteristics: (i) With the introduced time-varying observer gain, the designed adaptive distributed observer eliminates the need for global graph information but ensures convergence of the estimates;(ii) By appropriately designing the event-triggered mechanism, the communication frequency among follower agents is reduced in the sense that the communication rate decays over time;(iii) The newly designed adaptive control law ensures a linear estimation error equation, facilitating the development of a stable parameter update law without requiring prior knowledge of uncertain system parameters. The stability of closed-loop system and leader-following asymptotical tracking are achieved. Simulation study demonstrates the theoretical results. © The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2025.

关键词： Self adjusting control systems

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Enhanced Large Language Models for Effective Screening of Depression and Anxiety

arXiv

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arXiv 2025年

作者： Liu, June M. Gao, Mengxia Sabour, Sahand Chen, Zhuang Huang, Minlie Lee, Tatia M.C. State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong Laboratory of Neuropsychology and Human Neuroscience The University of Hong Kong Hong Kong The CoAI group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs) offer potential solutions, yet high costs and ethical concerns about training data remain challenges. This paper introduces a pipeline for synthesizing clinical interviews, resulting in 1,157 interactive dialogues (PsyInterview), and presents EmoScan, an LLM-based emotional disorder screening system. EmoScan distinguishes between coarse (e.g., anxiety or depressive disorders) and fine disorders (e.g., major depressive disorders) and conducts high-quality interviews. Evaluations showed that EmoScan exceeded the performance of base models and other LLMs like GPT-4 in screening emotional disorders (F1-score=0.7467). It also delivers superior explanations (BERTScore=0.9408) and demonstrates robust generalizability (F1-score of 0.67 on an external dataset). Furthermore, EmoScan outperforms baselines in interviewing skills, as validated by automated ratings and human evaluations. This work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools. © 2025, CC BY-NC-SA.

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