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Asymptotic properties of distributed social sampling algorithm

Science China(Information Sciences) 2020年第1期63卷 152-166页

作者： Qian LIU Xingkang HE Haitao FANG The Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences ACCESS Linnaeus Centre School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology

Social sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social sampling algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time, and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social sampling algorithm over random networks. First, we provide a framework based on stochastic approximation to study the asymptotic properties of the algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution. Furthermore, the asymptotic normality of estimation error generated by distributed social sample algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper.

关键词： social networks opinion formation social sampling stochastic approximation random networks asymptotic normality

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (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 at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

引用

IEEE International Conference on Cyber Technology in Automation, control, and intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks control systems Iterative algorithms intelligent systems

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

引用

IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

引用

Chinese Automation Congress (CAC)

作者： Junxiang Qiu Changhe Li Shengxiang Yang Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science and Informatics De Montfort University Leicester UK

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.

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Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

Spatial adaptivity with boundary refinement for smoothed par...

引用

作者： Xu, Yanrui Song, Chongming Wang, Xiaokun Ban, Xiaojuan Wang, Jiamin Zhang, Yalan Chang, Jian School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing China National Centre for Computer Animation Bournemouth University Poole United Kingdom Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Fluid simulation is well-known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary-distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed-distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid–solid coupling details more efficiently and effectively capture dynamic effects beneath the surface. © 2023 The Authors. computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Hydrodynamics

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A Hybrid Robot Kinematics Modeling and Trajectory Planning 18th

A Hybrid Robot Kinematics Modeling and Trajectory Planning

引用

18th Chinese intelligent systems Conference, CISC 2022

作者： Li, Jiakun Liu, Qunpo Ye, Ming Zhang, Zhuoran Hanajima, Naohiko School of Electrical Engineering and Automation Henan Polytechnic University 2001 Century Avenue Jiaozuo454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Henan Polytechnic University Jiaozuo454003 China College of Information and Systems Muroran Institute of Technology Muroran050-8585 Japan

ISBN: (纸本)9789811962257

This paper designs a hybrid robot with three prismatic joints and three revolute joints for the production scene with long stroke, high precision and high attitude requirements. The forward kinematics model of the robot was established by improved DH modeling method, and the inverse kinematics model was obtained by analytic method. The simulation experiment of workspace analysis and trajectory planning based on MATLAB verifies the advantages of large workspace and good flexibility. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

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