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Online Multiobjective Optimization for Electric Vehicle Charging Station Operation

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第4期10卷 8640-8655页

作者： Li, Yuanzheng Duan, Juntao Pan, Linqiang Ishizaki, Takayuki Gao, Liang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Tokyo Inst Technol Grad Sch Engn Tokyo 1528552 Japan Huazhong Univ Sci & Technol Sch Mech Sci & Engn Dept Ind & Mfg Syst Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the increasing development of electric vehicles (EVs), their demand for charging has increased. To satisfy their demand with limited public charging posts while minimizing their charging cost online, the charging operation of EV charging stations (EVCSs) should be optimized. In this context, we propose an online multiobjective optimization framework for EVCS charging operation optimization with the quality of service (QoS) and the total charging cost of EVCSs as objectives. In the framework, a novel quantitative definition of QoS for online optimization of EVCSs charging operation is proposed based on the difference between the cumulative charging power demand and supply. We introduce a target-based online dynamic weighted algorithm (TBODWA) into the proposed framework to solve the online multiobjective optimization problem. The advantage of proposed framework is that it can lead the average objectives to converge to a preset target. In the numerical experiment, a real EVCS charging example in California, USA is employed to verify the effectiveness and efficiency of the proposed framework.

关键词： Optimization Quality of service Costs Electric vehicle charging Uncertainty Heuristic algorithms Transportation Electric vehicles charging station (EVCS) online multiobjective optimization quality of service (QoS) uncertainty

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Point-Query Quadtree for Crowd Counting, Localization, and More

Point-Query Quadtree for Crowd Counting, Localization, and M...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Liu, Chengxin Lu, Hao Cao, Zhiguo Liu, Tongliang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350307184

We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation;too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://***/cxliu0/PET.

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization 9

A Fast-trained and Generalized Spiking Neural Network for Ro...

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9th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Liu, Chiyang Chen, Xinxing Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Human Augmentat & Rehabil Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Mobile robots

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Semi-Global and Global Fixed-Time Stability for Nonlinear Impulsive Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第4期55卷 1814-1824页

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Huang, Tingwen Zeng, Zhigang 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 Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar

This study investigates the semi-global fixed-time stability (SGFTS) and global fixed-time stability (GFTS) of nonlinear impulsive systems (NISs). A key challenge in analyzing the SGFTS of such systems lies in the evolving integration methods caused by the impulses. To address this, we dynamically partition the semi-global attraction set (SGAS) and solve the corresponding differential equations within each subset. Additionally, by constructing the transition dynamics of impulse points and iteratively computing these points, we establish the conditions for SGFTS under both stabilizing and destabilizing impulses. For GFTS, the primary difficulty arises from the distinct trajectories and dynamics of points located inside and outside the SGAS. To overcome this, we introduce the concept of the maximum-minimum impulse interval and derive a sufficient condition that ensures the system can enter the SGAS from a distance under a finite number of impulses. Furthermore, we develop a criterion for GFTS under varying impulse degrees and provide convergence time estimation based on the research on SGFTS of NIS. Finally, numerical examples are presented to validate the theoretical results. Notably, in Example 3, a fixed-time impulse controller is designed based on the proposed theoretical framework to achieve global stabilization of complex systems. This example highlights the potential applications of this work in the field of control.

关键词： Fixed-time stability global stability nonlinear impulsive systems (NISs) nonlinear impulsive systems (NISs) semi-global stability semi-global stability semi-global stability

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Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第6期34卷 3034-3043页

作者： Sheng, Yin Zeng, Zhigang Huang, Tingwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

关键词： Artificial neural networks Synchronization Delays Stability criteria State feedback Neurons Asymptotic stability Discontinuous activation finite-time synchronization infinite delay neural networks (NNs)

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A New Parameter Tuning Method for Generalized Multiresonant Current controllers in Grid-Connected Inverters

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第9期39卷 10962-10975页

作者： Ye, Jie Huang, Songtao Huang, Yukai Hu, Yihua Zhang, Chen Xu, Jinbang Shen, Anwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Kings Coll London Dept Engn London WC2R 2LS England Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

Multiresonant controllers are widely used in the current control of grid-connected converters due to their accurate tracking of reference sinusoidal signals containing high harmonic counts. However, the performance of multiresonant controllers relies heavily on the suitability of the parameter design. In this paper, a generalized multiresonant controller that unifies various existing resonant controllers is proposed, which achieves the decoupling of the proportional terms, eliminates the need for online operation of trigonometric functions, and obtains a more extensive range of regulation capability. Furthermore, a new multioptimized parameter tuning method that considers several performance indexes is proposed to utilize the generalized resonant controller's performance fully. Finally, based on the proposed method, a generalized multiresonant controller is designed for L-type and LCL-type grid-connected inverters, respectively, demonstrating the universality of the proposed approach. Simulation and experimental results validate the effectiveness of the proposed method.

关键词： Inverters Tuning Design methodology Current control Computational modeling Stability analysis Reviews generalized multi-resonant controller (RC) grid-connected inverter (GCI) parameter tuning

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A Survey on Load Frequency control of Multi-Area Power Systems: Recent Challenges and Strategies

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ENERGIES 2023年第5期16卷 2323页

作者： Liu, Xinghua Qiao, Siwei Liu, Zhiwei Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Load frequency control (LFC) is well known for balancing the load demand and frequency for a multi-area power system. Studies have proven that LFC can improve the global performance of multi-area power systems. In recent years, the increasing proportion of renewable energy, integration of EVs, and cyber-attacks have become the main challenges in LFC power systems. Different strategies have been applied in the literature for LFC power systems and the possible impacts of renewable energy, EVs, and cyber-attacks. This survey paper is devoted to the research on directions in LFC multi-area power systems. The mathematical model of recent challenges in LFC multi-area power systems is summarized and the similarities and differences of these challenges are analyzed. The uncertainty of renewable energy is a frequently noted issue in LFC power systems;however, the uncertainty that exists in controller design is often ignored. In this survey, we analyze methods for treating the uncertainty of renewable energy and controller. This survey paper introduces the most recent research on LFC and acquaints anyone interested in its development, such that the most effective strategies can be developed by the researchers.

关键词： power system LFC renewable energy cyber-attacks sliding mode control data-driven

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Proximal nested primal-dual gradient algorithms for distributed constraint-coupled composite optimization

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APPLIED MATHEMATICS AND COMPUTATION 2023年第1期444卷

作者： Li, Jingwang An, Qing Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Wuchang Univ Technol Articial Intelligence Sch Wuhan 430223 Peoples R China

In this paper, we study a class of distributed constraint-coupled optimization problems, where each local function is composed of a smooth and strongly convex function and a convex but possibly non-smooth function. We design a novel proximal nested primal-dual gradient algorithm (Prox-NPGA), which is a generalized version of the exiting algorithm- NPGA. The convergence of Prox-NPGA is proved and the upper bounds of the step-sizes are given. Finally, numerical experiments are employed to verify the theoretical results and compare the convergence rates of different versions of Prox-NPGA.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Constraint-coupled optimization Non-smooth function Proximal operator Primal-dual gradient algorithm

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Semi-supervised Class-Agnostic Motion Prediction with Pseudo label Regeneration and BEVMix 38

Semi-supervised Class-Agnostic Motion Prediction with Pseudo...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Wang, Kewei Wu, Yizheng Pan, Zhiyu Li, Xingyi Xian, Ke Wang, Zhe Cao, Zhiguo Lin, Guosheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Nanyang Technol Univ S Lab Singapore Singapore SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)1577358872

Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this challenge, our study explores the potential of semi-supervised learning (SSL) for class-agnostic motion prediction. Our SSL framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data by generating pseudo labels through test-time inference. To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module. This module effectively selects reliable pseudo labels and regenerates unreliable ones. Furthermore, we propose two data augmentation strategies: temporal sampling and BEVMix. These strategies facilitate consistency regularization in SSL. Experiments conducted on nuScenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. These results highlight the ability of our method to strike a favorable balance between annotation costs and performance. Code will be available at https://***/kwwcv/SSMP.

关键词： Forecasting

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Space Decoupled Prototype Learning for Few-Shot Attack Detection in Cyber-Physical Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第10期20卷 12350-12362页

作者： Sun, Haili Huang, Yan Zhou, Chunjie Han, Lansheng Liu, Hongle Chen, Juan Li, Xin Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Due to the lack of effective attack detection measures, cyberattacks may cause strong damage to industrial cyber-physical systems (CPSs). The embedding of attack categories learned by the existing attack detection methods is highly coupled to each other with fuzzy boundaries and overlapped neighborhood, leading to weak robustness and high false positive rates. To address these issues, in this article, we propose a few-shot attack detection method based on decoupled prototype learning (DPL-FSAD), aiming to enhance the detection accuracy and generalization capabilities for malicious attacks in CPS. Specifically, we first introduce feature contrastive learning to extract differentiated features from highly similar samples, achieving compact intraclass and sparse interclass feature embedding space. To solve the problem of fuzzy boundaries of different attack categories, prototype contrastive learning is then employed to reduce the coupling degree among prototypes and enhance their discriminability. A regularization term is exploited to mitigate the overfitting problem by reducing the gap between the feature embedding and prototypes. Furthermore, an orthogonal constraint is employed to separate prototypes of different attack types, generating a decoupled prototype embedding space. The experimental results on three public cyberattack datasets show that, compared with the suboptimal model a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), the proposed DPL-FSAD can improve the precision by 5.53%, F1-score by 3.3%, and reduce the false positive rate by 2.37% in average, which proves that the space decoupled prototype learning is effective for improving the generalization and robustness of industrial CPS attack detection in few-shot scenario.

关键词： Prototypes Feature extraction Couplings Task analysis Sun Contrastive learning Mutual information Few-shot learning (FSL) industrial cyber-physical system (CPS) network attack detection prototype contrastive learning (PCL) space decoupling

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