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Transmission Lines Overload Alleviation: Distributed Online Optimization Approach

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第5期17卷 3197-3208页

作者： Ding, Li Hu, Ping Liu, Zhi-Wei Wen, Guanghui Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 210096 Peoples R China

The risk of transmission lines overload in the power grid is increasing with the large-scale integration of fluctuating distributed renewable energies. Meanwhile, the requirement for accommodating more distributed resources in the future smart grids promotes the transition from the current highly centralized control structure toward a distributed one. In this article, we propose a distributed corrective control approach to mitigate line overloads in a real-time and close-loop manner. Unlike the conventional centralized approach, only simple computation and local information exchange are required to update the local corrective control action. This makes it possible to mitigate line overloads timely and adapt to the topology changes of grids. Furthermore, the introduction of system measurements and security constraints in the proposed algorithm ensures an effective and secure corrective control without new line overloads. The performance of the proposed distributed approach is demonstrated in the IEEE 14-Bus and 118-Bus systems.

关键词： Distributed control algorithm line overload multiagent system optimal power flow (OPF)

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Pose-guided spatiotemporal alignment for video-based person Re-identification

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INFORMATION SCIENCES 2020年 527卷 176-190页

作者： Gao, Changxin Chen, Yang Yu, Jin-Gang Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

Spatiotemporal alignment is a critical problem in video-based person re-identification (reid) tasks. To address this problem, we propose a pose-guided spatiotemporal alignment (PISA) method to align video sequences for video-based person re-id. First, to perform precise temporal alignment, we align a video sequence to a series of image sets, corresponding to a series of reference pedestrian poses. Each image set is obtained by selecting the images with the same pose and high quality. Furthermore, to perform spatial alignment, we decompose a pedestrian image into human body parts, and accordingly compute the representations over the parts. Finally, we evaluate the similarity between two video sequences by aggregating the distances of the pose-corresponded image sets rather than all pairs. The experimental results on two public datasets show that the proposed method performs favorably against state-of-the-art methods, even deep learning-based approaches. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Person re-identification Video representation Spatiotemporal alignment Pose alignment

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A Game-Theoretic Approach to Cross-Layer Security Decision-Making in Industrial Cyber-Physical Systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第3期67卷 2371-2379页

作者： Huang, Kaixing Zhou, Chunjie Qin, Yuanqing Tu, Weixun Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China

Current security measures in industrial cyber-physical systems (ICPS) lack the active decision capability to defend against highly-organized cyber-attacks. In this paper, a security decision-making approach based on stochastic game model is proposed to characterize the interaction between attackers and defenders in ICPSs and generate optimal defense strategies to minimize system losses. The major distinction of this approach is that it presents a practical way to build a cross-layer security game model for ICPSs by means of quantitative vulnerability analysis and time-based unified payoff quantification. A case study on a hardware-in-the-loop simulation testbed is carried out to demonstrate the feasibility of the proposed approach.

关键词： Games Security Physical layer Stochastic processes Game theory Adaptation models Decision-making game theory industrial cyber-physical system (ICPS) security

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Multistability and robustness of complex-valued neural networks with delays and input perturbation

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NEUROCOMPUTING 2021年 447卷 319-328页

作者： Zhang, Fanghai Huang, Tingwen Feng, Dan Zeng, Zhigang 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 Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Minist Educ China Key Lab Informat Storage Syst Engn Res Ctr Data Storage Syst & Technol Wuhan 430074 Peoples R China

This paper concentrates on the multistability and robustness of complex-valued neural networks (CVNNs) with delays and input perturbation. Firstly, several criteria on the multiple w-type stability of CVNNs with time-varying delays are obtained by virtue of w-type function and the analytical method. Secondly, several sufficient conditions of robustness have been derived to guarantee the w-type stability and boundedness of delayed CVNNs with input perturbation. These obtained results improve and extend the previous results. Finally, one numerical example is provided to show the effectiveness of the theoretical results. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Complex-valued neural networks Multistability Robustness Delays Input perturbation

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Improved black-box attack based on query and perturbation distribution

Improved black-box attack based on query and perturbation di...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Weiwei Zhao Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

Adversarial examples cause the deep neural network prediction error, which is a great threat to the deep neural network. How to generate more natural adversarial examples and improve the robustness of deep neural networks has received attention. In this paper, we propose an improved blackbox attack (IBBA) algorithm based on query and perturbation distribution. This algorithm only needs the top-l label of the attacked model to generate the adversarial examples. Based on the existing black-box attacks, we optimize the performance of the algorithm from two aspects: query distribution and perturbation distribution. In the aspect of query distribution, we adopt different strategies for nontargeted attack and targeted attack; in the aspect of perturbation distribution, we choose different low-frequency noise according to the difference between the targeted attack and nontargeted attack. The experimental results on imageNet show that the proposed algorithm is better than the existing algorithms in low query number, and the targeted attack is better in each specified query number.

关键词： Perturbation methods Neural networks Prediction algorithms Robustness Low-frequency noise Discrete cosine transforms Computational intelligence

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Stabilization of Second-Order Memristive Neural Networks With Mixed Time Delays via Nonreduced Order

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第2期31卷 700-706页

作者： Zhang, Guodong Zeng, Zhigang South Cent Univ Nationalities Sch Math & Stat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Automat Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results.

关键词： Delays Artificial neural networks Delay effects Stability criteria Asymptotic stability Synchronization Adaptive control memristive neural networks (MNNs) mixed time delays stabilization

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Modality-aware triplet hard mining for zero-shot sketch-based image retrieval

arXiv

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arXiv 2021年

作者： Huang, Zongheng Sun, Yifan Han, Chuchu Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Baidu Research United States

This paper tackles the Zero-Shot Sketch-Based image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes and 2) the sketch query and the photo gallery are in different modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects. First, it facilitates improvement through recent good practices in deep metric learning (DML). By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR. Without bells and whistles, this baseline achieves competitive retrieval accuracy. Second, it provides an insight that properly suppressing the modality gap is critical. To this end, we design a novel method named Modality-Aware Triplet Hard Mining (MATHM). MATHM enhances the baseline with three types of pairwise learning, e.g., a cross-modality sample pair, a within-modality sample pair, and their combination. We also design an adaptive weighting method to balance these three components during training dynamically. Experimental results confirm that MATHM brings another round of significant improvement based on the strong baseline and sets up new state-of-the-art performance. For example, on the TU-Berlin dataset, we achieve 47.88+2.94% mAP@all and 58.28+2.34% Prec@100. Code will be publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

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ASIAN JOURNAL OF control 2020年第1期22卷 156-167页

作者： Ding, Bo Zhang, Tianping Fang, Huajing Yangzhou Univ Dept Automat Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.

关键词： Input estimation state estimation linear systems stochastic systems

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UniAnimate-DiT: Human image Animation with Large-Scale Video Diffusion Transformer

arXiv

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arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Tang, Longxiang Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology United States Alibaba Group China Tsinghua University China

This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the robust generative capabilities of the original Wan2.1 model, we implement Low-Rank Adaptation (LoRA) technique to fine-tune a minimal set of parameters, significantly reducing training memory overhead. A lightweight pose encoder consisting of multiple stacked 3D convolutional layers is designed to encode motion information of driving poses. Furthermore, we adopt a simple concatenation operation to integrate the reference appearance into the model and incorporate the pose information of the reference image for enhanced pose alignment. Experimental results show that our approach achieves visually appearing and temporally consistent high-fidelity animations. Trained on 480p (832x480) videos, UniAnimate-DiT demonstrates strong generalization capabilities to seamlessly upscale to 720P (1280x720) during inference. The training and inference code is publicly available at https://github. com/ali-vilab/UniAnimate-DiT. Copyright © 2025, The Authors. All rights reserved.

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Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

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MATHEMATICAL PROBLEMS IN ENGINEERING 2020年第1期2020卷 1-15页

作者： Wang, Jian Wang, Xueyan Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Coll Management Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms-Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)-and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

关键词： Supply chains

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