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Distributed Finite-Time Observer for Multiple Line Outages Detection in Power Systems

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第3期54卷 1768-1778页

作者： Chen, Yu Liu, Zhi-Wei Wen, Guanghui Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 210096 Peoples R China

Fast detection of power line outages is critical for maintaining the stable operation of the power system. The aim of this article is to address the real time detection problem of multiple line outages in power systems. To effectively tackle the high-computational complexity issues associated with traditional approaches, we propose a multiple line outages detection algorithm that utilizes a distributed finite-time observer. The proposed method utilizes only local measurements and information from neighboring buses to update local observer for each bus. The proposed observer is mathematically proven to converge in a finite time, ensuring rapid detection of multiple line outages. Finally, simulation results demonstrate the effectiveness and rapidity of the proposed detection algorithm.

关键词： Distributed computing finite-time observer multiple line outages detection topology identification

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A radial visualization method based on knee point information for many-objective optimization

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Numerous high-dimensional solutions for many-objective optimization problems (MaOPs) usually impose a high cognitive burden on decision makers (DMs). Pareto front (PF) of MaOPs can express the problem characteristics, and then provide prior knowledge for solving the MaOPs. However, the existing high-dimensional visualization methods usually do not establish the relationship between PF information and decision making. Therefore, a novel radial visualization (RadViz) method called KRadViz that incorporates knee point information is proposed to visualize the information of PF shape and aid decision making. The relationship between the optimized performance information and PF shape is established, and the PF shape identification method is constructed. KRadViz is constructed by combining the optimization performance and PF shape. Three preferred solution selection methods are proposed to quickly screen out a few preferred solutions in different scenarios. The proposed KRadViz is compared with three high-dimensional visualization methods. The experimental results show that KRadViz can effectively display the high-dimensional PF shape, and give the optimization performance information of different solutions. The selection preferences of the three methods are also analyzed, and the effectiveness of the assisted decision process is verified. For the DTLZ2 and real-world MaOPs, the individual hypervolume (HV) contribution of preferred solutions increased by 9.98 % and 10.95 %, respectively.

关键词： Many-objective optimization High-dimensional visualization Knee points Preferred information RadViz

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Lagrange Stability of Fuzzy Memristive Neural Networks on Time Scales With Discrete Time Varying and Infinite Distributed Delays

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第8期30卷 3138-3151页

作者： Wan, Peng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The existing results of Lagrange stability for neural networks with distributed time delays are scale-free, which introduces conservativeness naturally. A class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) on time scales with discrete time-varying and infinite distributed delays is brought in this article. First, a new scale-limited Halanay inequality is demonstrated by timescale theory. Next, on the basis of inequality techniques on time scales, some new scale-limited algebraic criteria and linear matrix inequality criteria of Lagrange stability are obtained by comparison strategy and generalized Halanay inequality. All scale-limited sufficient criteria of Lagrange stability for FMNNs not only apply to continuous-time FMNNs and their discrete-time analogs, but also could deal with the arbitrary combination of them. Finally, two numerical simulations are given to verify the validity of the obtained theoretical results.

关键词： Neural networks Stability criteria Delays Takagi-Sugeno model Numerical stability control theory Synchronization Infinite distributed delays Lagrange stability memristive neural networks Takagi-Sugeno fuzzy logics time scales

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A thermophysical mechanism exploration of the brain: Motor cortex modeling with canonical ensemble theory

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NEUROCOMPUTING 2024年 610卷

作者： Li, Wei Zhou, Chenxi Chen, Xi Mao, Haodong He, Jiping Li, Qiang Zhang, Peng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Britton Chance Ctr Biomed Photon Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Collaborat Innovat Ctr Biomed Engn Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Hubei Peoples R China Beijing Inst Technol Adv Innovat Ctr Intelligent Robots & Syst Beijing Peoples R China

The brain, recognized as one of the most intricate systems globally, has been a focal point for scientific exploration. Researchers have made efforts to construct models of the brain based on neural dynamics and complex networks to gain insights into its workings. It is crucial to investigate the brain's working principles from various perspectives. This study presents a novel thermophysical model of the motor cortex and examines its potential thermodynamic properties. Utilizing canonical ensemble theory, we constructed the thermophysical model using spike and local field potential (LFP) signals obtained from intracortical brain-machine-interfaces (iBMIs) in two monkeys. The parameters derived from this model-namely internal energy, free energy, and entropy-were employed to assess the thermodynamic properties and observe alterations in these properties during reaching and grasping movements. Furthermore, this proposed model was applied to movement pattern decoding, highlighting its potential in neural decoding tasks. In both LFP- and spike-based thermodynamic models, there was an increase in internal energy and free energy, coupled with a decrease in entropy when the motor cortex was activated across various movement tasks. This suggests that the neural system adheres to the principles of a thermophysical system. Notably, the thermodynamic features demonstrated superior performance in decoding movement intentions compared to traditional LFP and spike features. This study represents the first construction of a comprehensive thermodynamic model of the motor cortex based on LFP and spike signals. The model exhibits remarkable stationarity and holds promise for long-term and stable evaluations of motor cortex functions.

关键词： Thermodynamic model Motor cortex Intracortical brain-machine interface Local field potential Spike

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Dynamic Bilinear Fusion Network for Synthetic Aperture Radar image Change Detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Dong, Huihui Du, Xinyu Li, Zhijie Li, Xiaohuan Ma, Zongfang Gao, Feng Jiao, Licheng Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods.

关键词： Convolution Feature extraction Radar polarimetry Logic gates Kernel Synthetic aperture radar Convolutional neural networks Adaptation models Transformers Rivers Bilinear pooling change detection dynamic convolution synthetic aperture radar (SAR) image

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Three-dimensional task allocation for smart transportation in spatial crowdsourcing: An intelligent role division approach

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Feng, Zhenhui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Spatial Crowdsourcing (SC) is an effective way to solve real world problems based on the Internet. As a core issue of SC, crowdsourcing task allocation has become the research hotspot. Existing researches usually focus on the matching of two types of objects: users and workers. However, some new SC platforms provide more flexible services which have a third type of task objects. In this paper, a kind of three-dimensional matching problem for transportation service (3DM-TS) based on real-world scenarios is investigated. In terms of problem construction, we consider three types of task objects, and prove the proposed 3DM-TS is NP-hard. To address the 3DM-TS problem, we propose an intelligent role division approach for online task allocation (IRD-OTA) based on quasi real-time solving framework, which can be divided into three stages. The first is task sensing. By introducing the batch-based mode, we handle the data of crowdsourcing tasks to extract the information of task objects. Second, we propose a roles division model based on the attraction-repulsion mechanism. The model performs matching based on the interaction forces between different objects, to obtain preliminary matching results. Finally, a threshold-based group adjustment strategy is designed to locally adjust the matching groups with lower utility. In the process of real-time task allocation, the task objects are matched according to the three stages of "task sensing-roles division-group adjustment", and the algorithm can obtain the final matching results after multiple batches of information iteration and feedback. In this paper, the IRD-OTA is examined on real dataset and synthetic dataset. Experimental results confirm the performance advantage of IRD-OTA in comparison with other approaches. In addition, we further investigate the effect of different parameter values on the performance, and verify the scalability of our proposal.

关键词： Spatial crowdsourcing Task allocation Role division Attraction-repulsion mechanism

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Distributed Predefined-Time Optimization and control for Multi-Bus DC Microgrid

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 5769-5779页

作者： Wang, Yan-Wu Zhang, Yu Liu, Xiao-Kang Chen, Xia Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Peoples R China

As an extension of single-bus DC microgrid, multi-bus DC microgrid has become a popular research topic due to its better availability and reliability and more reconfiguration options. The fundamental and challenging control objectives for multi-bus DC microgrids are bus voltage regulation and current sharing with transmission loss minimization. In this paper, a multi-objective optimization problem with penalty factors is formulated, and the global optimal solution is presented explicitly. A distributed predefined-time optimization and control approach is designed by employing a time-dependent function and a distributed predefined-time observer. Under the proposed approach, bus voltage, output current, and power flow converge within a predefined settling time. The performance of the proposed approach is evaluated by simulations and hardware-in-the-loop experiments in terms of trade-off ability, impact of parameter changes, robustness to load variation and grid reconfiguration, plug-and-play functionality, and comparison case.

关键词： Optimization Microgrids Voltage control Propagation losses Minimization Convergence Load flow Bus voltage regulation current sharing multi-bus DC microgrid predefined-time optimization and control transmission loss minimization

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Dynamic memory-event-triggered consensus of uncertain MASs with networked attacks and delays

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第2期361卷 852-866页

作者： Zhang, Qingcao An, Qing Su, Housheng Yin, Xiuxia Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan 430074 Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Nanchang Univ Sch Math & Comp Sci Nanchang 330031 Peoples R China

This paper addresses the problem of achieving consensus in leader-following multi-agent systems under event-triggered control, with considerations of model uncertainties, network time-delay, and random deception attacks. To address the challenges of limited network resources, we propose a novel event-triggered control scheme that employs a dynamic memorybased approach. The proposed scheme utilizes multiple historic event-triggered states that are correlated with the states of neighboring agents and the leader, and the threshold parameter varies dynamically with time. We establish a memory-based closed-loop system that considers both network delays and deception attacks. Using the Lyapunov stability theory, a sufficient condition is derived to ensure consensus performance, and the desired controller gains and triggered matrices are obtained by solving linear matrix inequalities. Besides, we provide a comparative analysis of the memoryless dynamic event-triggered control. Finally, we validate the effectiveness of proposed approach with simulation results.

关键词： Multi-agent systems Parameters uncertainty Networked delays Deception attacks Dynamic memory-event-triggered consensus

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Event-Based Distributed Average Tracking for Second-Order Heterogeneous Multiagent Systems With Uncertain Dynamics

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第5期54卷 2990-3000页

作者： Zhang, Junjia Wang, Yong Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Army Acad Artillery & Air Def Sch Informat Secur Zhengzhou 450052 Peoples R China

The event-triggered distributed average tracking (ETDAT) problem for heterogeneous multiagent systems (MASs) with uncertain dynamics is investigated in this article. The ETDAT algorithms aim to build control laws for heterogeneous agents to follow the average states of multiple time-varying input signals in event-triggered communication networks. The uncertain dynamics of agents and the event-triggered communication mechanisms make the design of distributed average tracking (DAT) protocols difficult. To achieve ETDAT for heterogeneous MASs with uncertain dynamics, we designed two kinds of ETDAT protocols. First, on the basis of model reference adaptive control (MRAC) technology and sampling measurements, we present a class of static-gain ETDAT algorithms. In comparison to conventional DAT, the proposed ETDAT algorithms not only solve the DAT problem of heterogeneous MASs but also greatly reduce the cost of network communication. Second, dynamic-gain ETDAT algorithms based on self-adaptive principles are presented to minimize network global information needs. The above two algorithms adopt boundary layer approximation methods and dynamic event-triggered strategies, which can further reduce the chattering phenomenon and event-triggered frequency. Finally, the theoretical findings are shown with several examples.

关键词： Distributed average tracking (DAT) event-triggered mechanisms (ETMs) heterogeneous multiagent systems (MASs) model reference adaptive control (MRAC)

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A New Binary Encoding Method for Energy Consumption Patterns Quantification

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu Chung, C. Y. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Hong Kong Polytech Univ PolyU Dept Elect Engn Hong Kong Peoples R China

Extracting users' energy consumption patterns (ECPs) from smart meter data is an important work for retailers. The existing literature usually describe these patterns by clustering the daily load curves (DLCs), but lack a clear and quantified representation to explain what the exact schema of a user is. Therefore, this article proposes a new binary encoding method for ECPs quantification. Specifically, first, both time and value intervals are divided for dimensionality reduction based on the similarity of adjacent timestamp loads. Then, a binary aggregate approximation (BAX) method is proposed to encode each DLC into a BAX word, and the BAX words of users are merged to obtain the schemas with a three-element alphabet. Finally, based on the schemas, the stability scores of users' patterns are quantified and are used to select target users for demand response (DR) measures. Case studies on a real dataset with 5566 users show that each target user averagely contributes to 0.172% of peak reduction, while each unselected user only contributes to 0.026%. Furthermore, to obtain target schemas and to find new users in future DR measures, a $K$ -means symbolic algorithm is designed to cluster BAX words of target users. The proposed encoding method and the findings can provide guidance of finding typical target users for DR measures.

关键词： Binary aggregate approximation (BAX) dimension reduction energy consumption pattern (ECP) K-means symbolic algorithm stability quantification

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