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arXiv 2024年

作者： Wang, Xiang 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 China

Recent advancements in controllable human-centric video generation, particularly with the rise of diffusion models, have demonstrated considerable progress. However, achieving precise and localized control over human motion, e.g., replacing or inserting individuals into videos while exhibiting desired motion patterns, still remains challenging. In this work, we propose the ReplaceAnyone framework, which focuses on localizing and manipulating human motion in videos with diverse and intricate backgrounds. Specifically, we formulate this task as an image-conditioned pose-driven video inpainting paradigm, employing a unified video diffusion architecture that facilitates image-conditioned pose-driven video generation and inpainting within masked video regions. Moreover, we introduce diverse mask forms involving regular and irregular shapes to avoid shape leakage and allow granular local control. Additionally, we implement a two-stage training methodology, initially training an image-conditioned pose-driven video generation model, followed by joint training of the video inpainting within masked areas. In this way, our approach enables seamless replacement or insertion of characters while maintaining the desired pose motion and reference appearance within a single framework. Experimental results demonstrate the effectiveness of our method in generating realistic and coherent video content. Copyright © 2024, The Authors. All rights reserved.

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Distributed Two-Layer Predictive control of AC Microgrid Clusters With Communication Delays

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IEEE TRANSACTIONS ON SMART GRID 2025年第3期16卷 2040-2051页

作者： Yang, Tao Lai, Jingang Yu, Chang Wang, Xiaoping Xiao, Qiang 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 Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430000 Peoples R China

This paper proposes a two-layer distributed network predictive control strategy for AC microgrids (MGs) clusters with communication delays. The strategy involves establishing a two-layer communication network to regulate the voltage/frequency of all distributed generators (DGs) within the MG cluster to predefined reference values while ensuring consistency in incremental costs across individual MGs. Furthermore, a multi-step predictive controller is designed, where delay information in the controller is replaced by the latest predictions, enabling proactive compensation for delays. Stability analysis of the closed-loop AC MG clusters is conducted and the response matching condition is derived between the second and tertiary levels. Finally, real-time simulations on an OPAL-RT platform are performed for AC MG clusters, validating the robustness of the proposed control method against communication delays.

关键词： Delays Costs Voltage control Communication networks Predictive control Pins Microgrids Frequency control Stability analysis Topology AC microgrid cluster distributed two-layer predictive control active compensation communication delay

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A hybrid SNN-STLSTM method for human error assessment in the high-speed railway system

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ADVANCED ENGINEERING INFORMATICS 2024年 60卷

作者： Zhou, Jian-Lan Guo, Zhi-Ming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China

Over the years, many state-of-the-art technologies have reinforced safety in the high-speed railway driving process. However, the accident rate has not dropped significantly with the advanced technology onboard. The leading cause of this phenomenon is adverse human performance. The crucial aspects contributing to human performance are performance shaping factors (PSFs). The existing expert judgment techniques evaluate human error probabilities depending on the descriptions of PSFs' influences on human performance. However, they cannot perform dynamic human error assessment with the real-time fluctuations of PSF interventions. To overcome this deficiency, we propose a deep learning -based method considering the instantaneous effect and time -dependent influence of PSF interventions on human performance and matching different neural network characteristics to human cognitive processes. The proposed SNN-STLSTM method combines the spiking neural network (SNN) auto -encoder and the sparse -temporal long short-term memory (STLSTM) network. The unsupervised SNN, a brain -like computational model, simulates the human brain's response when PSF interventions occur instantaneously. Meanwhile, an auto -encoder architecture integrating the unsupervised SNN makes the representations learned by SNN more meaningful. In addition, for the irregularity of PSF interventions, the standard LSTM is extended to the architecture STLSTM to predict the long-term effects of PSFs on human performance. Finally, a case study of the Wuhan-Guangzhou (WH-GZ) high-speed railway (HSR) shows how Monte Carlo simulation based on Fault Tree Analysis is used to assess and collect human error data and then how PSF and human error data are used to train the whole framework. The comparative experiment and ablation study indicate that the proposed SNN-STLSTM method outperforms other artificial intelligence approaches. This study provides insightful findings that help to understand how and what aspects affect h

关键词： Human error assessment Performance shaping factors Long short-term memory network Spiking neural network High-speed railway system

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Event-triggered integral sliding mode control of two-time-scale systems

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NONLINEAR DYNAMICS 2025年第2期113卷 1263-1277页

作者： Yu, Tian-Tian Wang, Yan-Wu Yang, Wu Lei, Yan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China GuangXi Univ Sch Elect Engn Nanning 530004 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Gener Technol & Syst Serv Robot Chongqing 400715 Peoples R China

This paper investigates the H-infinity performance of two-time-scale systems with nonlinear uncertainty and external disturbance via the event-triggered integral sliding mode control (ETISMC) strategy. To facilitate the analysis of the sliding mode surface's reachability and the design of the event-triggered (ET) integral sliding mode controller, the epsilon-dependent integral sliding function with the event-triggered mechanism is constructed. Based on H-infinity control theory, the integral sliding mode controller gain and the ET parameter matrix are co-designed through the solution of a set of linear matrix inequalities. The proposed event-triggered mechanism can avoid Zeno behavior. Furthermore, an adaptive ETISMC strategy is proposed to address the unknown upper bounds of uncertainty and external disturbance. Finally, the effectiveness, applicability, and advantages of the control strategies are verified by simulations and comparative analysis.

关键词： Two-time-scale system H-infinity performance Event-triggered mechanism Integral sliding mode control Adaptive control

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Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data

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IEEE TRANSACTIONS ON SMART GRID 2025年第3期16卷 2439-2450页

作者： Yang, Honggang Lian, Cheng Xu, Bingrong Ding, Ruijin Zhao, Pengbo Zeng, Zhigang Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China Chinese Acad Sci Inst Hydrobiol State Key Lab Breeding Biotechnol & Sustainable Aq Wuhan 430072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China

The widespread adoption of advanced metering infrastructure has provided abundant data, enabling the integration of deep learning techniques into smart grids. However, it has also led to more sophisticated and concealed methods of electricity theft. Due to the challenges posed by data imbalance and missing values caused by device malfunctions and communication issues, existing deep learning models often perform poorly. To address these issues, this paper proposes a multi-step training framework named DING, which incorporates diffusion generation, self-supervised pre-training, normalized condition imputation, and generation-balanced fine-tuning. First, sufficient balanced smart meter data is generated using a diffusion model. Second, a pre-trained encoder is trained on the generated data, extracting unbiased low-dimensional features that can be used for downstream classification tasks and as conditions to guide the training of the imputation model. Next, an imputation model is trained based on a diffusion state-space equation. Finally, fine-tuning is performed on the balanced data. Experiments on a real dataset from the State Grid Corporation of China demonstrate that the proposed method outperforms previous models for both electricity theft detection and imputation tasks.

关键词： Imputation Electricity Time series analysis Data models Smart meters Feature extraction Training Deep learning Diffusion models Mathematical models Diffusion model electricity theft detection imbalanced data time series imputation

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Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

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Complex System Modeling and Simulation 2022年第1期2卷 18-34页

作者： Yindong Shen Leqin Yu Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution *** electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the *** some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still ***,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual ***,we propose the robust optimization model based on a route-related uncertain set to tackle this ***,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the *** effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

关键词： electric vehicle routing problem time windows uncertain demand energy consumption model robust optimization adaptive large neighbourhood search

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A Multi-Scale Validation and Analysis of Flash Flooding Impacts on Urban Road Transportation

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2024年第11期25卷 16869-16880页

作者： Du, Chongyang Ouyang, Min Fan, Chao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Clemson Univ Sch Civil & Environm Engn & Earth Sci Clemson SC 29634 USA

The impacts of flash flooding on road transportation, such as travel time delays, are crucial concerns by different stakeholders including neighborhoods and governments at all levels, which calls for a multi-scale flooding impact assessment. Although existing methods have such a capacity, they are not effectively validated, limiting our understanding of the impacts for successful flooding management. This paper develops a multi-scale flooding impact assessment framework and a validation method applied to road transportation in Wuhan, China. Using real-world traffic data in a historical flooding event, the validation results show the capability of the framework in predicting the post-flooding travel time of trips across different scales. The framework is further used to predict travel time delays in flooding at neighborhood, district, and city scales during peak-traffic periods. Results show that while flooding primarily impacts commuting times in central city areas, the integration of traffic accidents in flooding expands the impact to districts and communities farther away from the city center. At the city scale, our analysis indicates that the travel time to workplaces is mostly increased, compared to the increase in travel time to critical facilities. The proposed framework is also adopted to prioritize the treatment of flood-prone sites. These results show the potential of the framework to help flooding response and mitigation.

关键词： Floods Roads Urban areas Rain Transportation Analytical models Accidents Flash flooding impact model validation multi-scale assessment urban road transportation

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Memristor-Based Circuit Design of Biological Behavior Chain

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年

作者： Yang, Le Cai, Ren Cheng, Ming Ding, Zhixia Li, Sai Zeng, Zhigang Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China 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

Currently, studies on memristor-based operant conditioning circuits concentrate on the learning process of the single behaviors, without attention to the chaining process composed of multiple behaviors. This paper proposes a memristor-based circuit design of biological behavior chain, which consists of behavior modules, learning modules, delay modules, and satiety modules. After learning, the mouse can complete a chain process consisting of three behaviors: pressing button A to eliminate electrical stimulation, pressing button B to obtain food, and pressing button C to open the cage. Behavior and delay modules are used to simulate these three behaviors, the learning module is used to simulate the learning process of chaining between two behaviors and chaining between three behaviors. In addition, the study also explored the effects of mouse satiety on the experiment, as well as processes such as natural forgetting and relearning. The simulation results in PSPICE indicate that the circuit is capable of effectively simulating the aforementioned functions. Furthermore, Monte Carlo analysis and temperature simulation analysis are conducted on the circuit. The simulation results confirm that the circuit exhibits good stability.

关键词： Circuits Memristors Associative memory Integrated circuit modeling Electrical stimulation Pressing Adaptation models Organisms Presses Decision making Biological behavior chain memristor operant conditioning classical conditioning satiety

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AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease

arXiv

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arXiv 2024年

作者： Xiang, Xiang Zhang, Zihan Ma, Jing Deng, Yao Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer vision

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Non-negative scaled edge-consensus of saturated networked systems via adaptive output-feedback control

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NEUROCOMPUTING 2024年 586卷

作者： Sun, Yaping Yang, Xinsong Zhao, Yini Su, Housheng Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ Sch Foreign Languages Chengdu 610065 Sichuan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper investigates fully distributed scaled non -negative edge -consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, outputfeedback -based low -gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge -consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper's algorithm and result, including the number of vertexes and edges;the feasible solutions of the scaled edge -consensus conditions exist and can be easily obtained;the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results.

关键词： Edge-consensus Actuator saturation Networked systems Observer Adaptive control

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