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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan School of Computer and Control Engineering Yantai UniversityYantai264005China School of Computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

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Digital Image Encryption Algorithm Based on Double Chaotic Map and LSTM

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computers, Materials & Continua 2023年第11期77卷 1645-1662页

作者： Luoyin Feng Jize Du Chong Fu School of Computer Science and Engineering Northeastern UniversityShenyang110819China School of Electrical Engineering and Computer Science University of MissouriMissouri65201USA

In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital ***,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic *** signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic *** the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training *** overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM ***,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted *** research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and *** algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential ***,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.

关键词： Digital image encryption LSTM particle swarm optimization algorithm DCM

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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On-Policy and Off-Policy Value Iteration Algorithms for Stochastic Zero-Sum Dynamic Games

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Journal of Systems science & Complexity 2025年第1期38卷 421-435页

作者： GUO Liangyuan WANG Bing-Chang ZHANG Ji-Feng School of Control Science and Engineering Shandong UniversityJinan 250063China School of Automation and Electrical Engineering Zhongyuan University of TechnologyZhengzhou 450007China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.

关键词： Approximate dynamic programming on-policy off-policy stochastic zero-sum games valueiteration

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Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems:Advances,Challenges,and Prospects

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Protection and control of Modern Power Systems 2024年第5期9卷 16-41页

作者： Hanxiao Liu Luan Zhang Liwei Li School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Because of their advantages of high energy and power density,low self-discharge rate,and long lifespan,lithium-ion batteries(LIBs)have been widely used in many applications such as electric vehicles,energy storage systems,smart grids,***,lithium-ion battery systems(LIBSs)frequently malfunction because of complex working conditions,harsh operating environment,battery inconsistency,and inherent defects in battery ***,safety of LIBSs has become a prominent problem and has attracted wide ***,efficient and accurate fault diagnosis for LIBs is very *** paper provides a comprehensive review of the latest research progress in fault diagnosis for ***,the types of battery faults are comprehensively introduced and the characteristics of each fault are ***,the fault diagnosis methods are systematically elaborated,including model-based,data processing-based,machine learning-based and knowledge-based *** latest research is discussed and existing issues and challenges are presented,while future developments are also *** aim is to promote further researches into efficient and advanced fault diagnosis methods for more reliable and safer LIBs.

关键词： Battery management system battery safety fault diagnosis lithiumion battery system

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Sawtooth-enhanced bend sensor for gesture recognition

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science China(Technological sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

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A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17729-17741页

作者： Ran, Weizhi Nantogma, Sulemana Yang, QingZhuo Zhang, Shangyan Wang, Yan Xu, Yang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Xi'an University of Architecture and Technology School of Information and Control Engineering China

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid pre-set solutions, and limited interoperability. This paper presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as a Markov Decision Process (MDP), with parameters instantiated using semantic information and rules. A novel Feedback-based Reinforcement Learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation. © 2014 IEEE.

关键词： Smart homes

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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science China(Technological sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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