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Circular Formation Control for Networked Dynamic Autonomous Underwater Vehicles: Estimator-Based Approaches

IEEE Transactions on Control of Network Systems 2025年

作者： Xie, Jingjing Lan, Weiyao Tong, Feng Yu, Xiao Xiamen University Department of Automation China Xiamen University College of Ocean and Earth Sciences The National and Local Joint Engineering Research Center for Navigation and Location Service Technology Xiamen361005 China Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China The Institute of Artificial Intelligence Xiamen361005 China

This paper investigates the circular formation control problem of networked dynamic autonomous underwater vehicles (AUVs) in a directed network. The objective is to make dynamic AUVs encircle a given target with specific radius and velocity. The network topology among all AUVs is modeled by a directed graph containing a spanning tree, and the target is only known to one AUV. Estimator-based approaches with the backstepping technique are proposed for cases with or without communication. First, in communication-based networks, AUVs are allowed to transmit information via communication devices. A dynamic control law consisting of a distributed estimator is proposed. By analyzing with perturbed system method, the multi-vehicle system achieves global exponential stability instead of asymptotic stability. Second, in communication-denied networks, AUVs depend only on the relative measurements from the onboard sensors. A dynamic control law consisting of a decentralized estimator is proposed. By small-gain analysis, the global asymptotic stability for the multi-vehicle system is established. To demonstrate effectiveness, simulation results are presented. © 2014 IEEE.

关键词： Directed graphs

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Efficient Twin Migration in Vehicular Metaverses: Multi-Agent Split Deep Reinforcement Learning with Spatio-Temporal Trajectory Generation

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Junlong Kang, Jiawen Xu, Minrui Wu, Fan Zhang, Hongliang Huang, Huawei Niyato, Dusit Mao, Shiwen Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computing and Data Science Singapore Singapore Central South University School of Computer Science and Engineering Changsha China Peking University School of Electronics Beijing China Sun Yat-Sen University School of Data and Computer Science Guangzhou China Auburn University Department of Electrical and Computer Engineering Auburn United States

Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way that migrates the VT when the locations of physical entities keep changing to maintain seamless immersive VT services. However, an efficient VT migration is challenging due to the rapid movement of vehicles, dynamic workloads of Roadside Units (RSUs), and heterogeneous resources of the RSUs. To achieve efficient migration decisions and a minimum latency for the VT migration, we propose a multi-agent split Deep Reinforcement Learning (DRL) framework combined with spatio-temporal trajectory generation. In this framework, multiple split DRL agents utilize split architecture to efficiently determine VT migration decisions. Furthermore, we propose a spatio-temporal trajectory generation algorithm based on trajectory datasets and road network data to simulate vehicle trajectories, enhancing the generalization of the proposed scheme for managing VT migration in dynamic network environments. Finally, experimental results demonstrate that the proposed scheme not only enhances the Quality of Experience (QoE) by 29% but also reduces the computational parameter count by approximately 25% while maintaining similar performances, enhancing users' immersive experiences in vehicular metaverses. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Interference Factors and Compensation Methods when Using Infrared Thermography for Temperature Measurement: A Review

arXiv

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arXiv 2025年

作者： Pan, Dong Mo, Tan Jiang, Zhaohui Duan, Yuxia Maldague, Xavier Gui, Weihua State Key Laboratory of Precision Manufacturing for Extreme Service Performance the School of Automation Central South University Changsha410083 China School of Automation Central South University Changsha410083 China School of Physics Central South University Changsha410083 China Department of Electrical and Computing Engineering Université Laval QuébecQCG1V 0B1 Canada

Infrared thermography (IRT) is a widely used temperature measurement technology, but it faces the problem of measurement errors under interference factors. This paper attempts to summarize the common interference factors and temperature compensation methods when applying IRT. According to the source of factors affecting the infrared temperature measurement accuracy, the interference factors are divided into three categories: factors from the external environment, factors from the measured object, and factors from the infrared thermal imager itself. At the same time, the existing compensation methods are classified into three categories: Mechanism Modeling-based Compensation method (MMC), Data-Driven Compensation method (DDC), and Mechanism and Data jointly driven Compensation method (MDC). Furthermore, we discuss the problems existing in the temperature compensation methods and future research directions, aiming to provide some references for researchers in academia and industry when using IRT technology for temperature measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (temperature measurement)

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Research on experimental method of high-speed railway perimeter intrusion monitoring technology based on multi-sensor fusion 24

Research on experimental method of high-speed railway perime...

引用

Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Xue Yang Bo Zhang Tao Yu Ye Liang Wanpeng Zhang Wentao Li Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China School of Automation and Software Engineering Shanxi University Taiyuan Shanxi China China Railway Nanchang Group Co. Ltd. Nanchang Jiangxi China Graduate Department China Academy of Railway Sciences Beijing China

ISBN: (纸本)9798400709753

In recent years, China's high-speed railway has achieved rapid development, and has become the largest and fastest high-speed railway country in the world. By the end of 2023, the operating mileage has reached 159,000 km, including 45,000 km of high-speed railway. With the continuous expansion of high-speed railway development scale, the country also attaches more and more importance to railway operation safety. The report of the 20th National Congress of the Communist Party of China pointed out that we should accelerate the construction of a modern infrastructure system, transportation equipment system, transportation service system, and transportation governance system, promote the safe, intelligent, green and open development of transportation, and find a new road of transportation modernization with Chinese characteristics. Many scholars have successively proposed high-speed railway perimeter monitoring technology schemes based on various sensor devices. However, due to the diversity of monitoring equipment, uneven data interfaces and unified data standards, centralized testing of high-speed railway perimeter intrusion monitoring technology based on different types and models of sensing equipment has been unable to be carried out, and synchronous testing cannot be conducted in a unified test environment. In order to solve the above problems, this paper carried out research on device interface, data standardization, sample library construction methods and test methods, and compared various high-speed railway perimeter intrusion monitoring technology schemes based on multi-sensor fusion to verify the functional performance and environmental adaptability of various monitoring devices in actual operation scenarios.

关键词： Data standard

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Privacy-Preserving Biometric Verification with Handwritten Random Digit String

arXiv

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arXiv 2025年

作者： Zhang, Peirong Liu, Yuliang Lai, Songxuan Li, Hongliang Jin, Lianwen School of Electronic and Information Engineering South China University of Technology Guangzhou China SCUT-Zhuhai Institute of Modern Industrial Innovation Zhuhai China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China EI Product Department Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China

Handwriting verification has stood as a steadfast identity authentication method for decades. However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures. To address this concern, we propose using the Random Digit String (RDS) for privacy-preserving handwriting verification. This approach allows users to authenticate themselves by writing an arbitrary digit sequence, effectively ensuring privacy protection. To evaluate the effectiveness of RDS, we construct a new HRDS4BV dataset composed of online naturally handwritten RDS. Unlike conventional handwriting, RDS encompasses unconstrained and variable content, posing significant challenges for modeling consistent personal writing style. To surmount this, we propose the Pattern Attentive VErification Network (PAVENet), along with a Discriminative Pattern Mining (DPM) module. DPM adaptively enhances the recognition of consistent and discriminative writing patterns, thus refining handwriting style representation. Through comprehensive evaluations, we scrutinize the applicability of online RDS verification and showcase a pronounced outperformance of our model over existing methods. Furthermore, we discover a noteworthy forgery phenomenon that deviates from prior findings and discuss its positive impact in countering malicious impostor attacks. Substantially, our work underscores the feasibility of privacy-preserving biometric verification and propels the prospects of its broader acceptance and application. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics

arXiv

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arXiv 2025年

作者： Tang, Jialiang Chen, Shuo Gong, Chen Zhang, Jing Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore School of Intellgence Science and Technology Nanjing University China Department of Automation Institute of Image ProceProceedingssing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science Wuhan University China

Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs), with their powerful in-contextual modeling capabilities, hold significant potential for TSF. However, existing LLM-based methods usually perform suboptimally because they neglect the inherent characteristics of time series data. Unlike the textual data used in LLM pre-training, the time series data is semantically sparse and comprises distinctive temporal patterns. To address this problem, we propose LLM-PS to empower the LLM for TSF by learning the fundamental Patterns and meaningful Semantics from time series data. Our LLM-PS incorporates a new multi-scale convolutional neural network adept at capturing both short-term fluctuations and long-term trends within the time series. Meanwhile, we introduce a time-to-text module for extracting valuable semantics across continuous time intervals rather than isolated time points. By integrating these patterns and semantics, LLM-PS effectively models temporal dependencies, enabling a deep comprehension of time series and delivering accurate forecasts. Intensive experimental results demonstrate that LLM-PS achieves state-of-the-art performance in both short- and long-term forecasting tasks, as well as in few- and zero-shot settings. © 2025, CC BY.

关键词： Spatio-temporal data

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FUZZY QUALITATIVE SIMULATION

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1993年第4期23卷 1038-1061页

作者： SHEN, Q LEITCH, R Intelligent Automation Laboratory Department of Computing and Electrical Engineering Heriot-Watt University Edinburgh UK

The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning about the properties of physical systems. An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors. Experimental results with the algorithm are presented and comparison with other recently proposed methods is made.

关键词： Fuzzy Set Theory temporal filter Arbitrary Qualitative Fuzzy fuzzy relations

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