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Finite-Time Output Synchronization for Output-Coupled Reaction-Diffusion Neural Networks With Directed Topology

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第3期9卷 1386-1394页

作者： Qiu, Qian Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hongshan 430074 Peoples R China Key Lab Image Proc & Intelligent Control Educ Min Wuhan 430074 Peoples R China

For output-coupled reaction-diffusion neural networks (RDNNs), the finite-time output synchronization (FTOS) issue is researched under directed topology in this paper. By virtue of the Lyapunov approach, inequality skills and important lemmas, the feedback control protocol is designed to obtain the FTOS criterion for coupled RDNNs with output coupling. Furthermore, to ensure that the output-coupled RDNNs realize output synchronization in finite time, a novel adaptive coupling weights control strategy and output feedback controller are proposed. Finally, the relevant simulation results are presented to demonstrate the correctness of our obtained theorems respectively.

关键词： Synchronization Couplings Topology Artificial neural networks Network topology Protocols Adaptive control FTOS directed topology coupled RDNNs output coupling adaptive coupling weights

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ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 9002-9014页

作者： Qing, Zhiwu Huang, Ziyuan Zhang, Shiwei Tang, Mingqian Gao, Changxin Jin, Rong Ang Jr, Marcelo H. Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Univ Singapore Adv Robot Ctr Singapore 119077 Singapore Alibaba Grp Hangzhou 311100 Peoples R China

The central idea of contrastive learning is to discriminate between different instances and force different views from the same instance to share the same representation. To avoid trivial solutions, augmentation plays an important role in generating different views, among which random cropping is shown to be effective for the model to learn a generalized and robust representation. Commonly used random crop operation keeps the distribution of the difference between two views unchanged along the training process. In this work, we show that adaptively controlling the disparity between two augmented views along the training process enhances the quality of the learned representations. Specifically, we present a parametric cubic cropping operation, ParamCrop, for video contrastive learning, which automatically crops a 3D cubic by differentiable 3D affine transformations. ParamCrop is trained simultaneously with the video backbone using an adversarial objective, so that it learns to increase the contrastive loss and thus gradually reduces the shared contents between two cropped views. Experiments show that this adaptive and gradual increase in the disparity yielded by ParamCrop is beneficial to learning a strong and generalized representation for downstream tasks, which is shown to be effective on multiple contrastive learning frameworks and video backbones.

关键词： Parametric cropping contrastive learning video representation learning

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Distributed predefined-time algorithms for optimal solution seeking in multi-agent systems subject to input disturbances

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AUTOMATICA 2025年 174卷

作者： Xu, Jing-Zhe Liu, Zhi-Wei Ge, Ming-Feng Yang, Tao Chi, Ming He, Dingxin 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 China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper presents a novel incremental consensus-based algorithm for solving a class of distributed optimization problems in multi-agent systems, considering input disturbances, equality constraints, and box constraints. Traditional methods rely on average consensus to maintain the satisfaction of equality constraints throughout the entire evolution process. However, in practical applications, input disturbances can disrupt these equality constraints, rendering traditional methods ineffective. To address this challenge, the proposed algorithm combines integration sliding mode control technology with the observer methodology, creating a unified framework capable of handling input disturbances and preventing the system state from deviating beyond the solution space defined by the equality and box constraints. Moreover, the proposed algorithm offers the advantage of ensuring that all agents reach the optimal solution within a predefined time frame. This settling time can be directly adjusted by modifying one or more parameters. Finally, several numerical examples are validated to demonstrate the effectiveness and performance of the proposed algorithm. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Distributed optimization Disturbance rejection Predefined-time convergence Multi-agent systems

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Optimal Decisions in the Closed-Loop Supply Chain considering Capacity Constraints and Stochastic Demand under the Cap-and-Trade Regulation

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COMPLEXITY 2022年第0期2022卷

作者： Wang, Jian Shao, Wenxuan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In the current context of resource scarcity and increased pollution, enterprises need to better allocate capacity and achieve carbon emission reduction. We examine the optimal decision problem for a closed-loop supply chain (CLSC) considering capacity constraints and stochastic demand under a cap-and-trade system. A two-level CLSC comprising a supplier and a manufacturer is established. To make the model more realistic, we treat demand as a random variable, consider the manufacturer's production capacity within a certain upper limit, and allow the manufacturer to make investments to expand capacity to complete production demand. Based on this CLSC, the collection activities of the supplier and manufacturer are considered. Meanwhile, government carbon limits and carbon trading controls on the manufacturer are considered based on carbon reduction by enterprises. Through modeling and analysis, optimal decisions are derived for the amount of capacity investment, level of carbon emission reduction, and collection price variables. The results indicate that supplier collection has a positive effect on the sustainability of the CLSC. Moreover, the government's carbon quota and carbon trading control of manufacturers can effectively reduce the carbon emissions of the CLSC, encourage enterprises to reduce carbon emissions, and improve the sustainability of the CLSC.

关键词： Supply chains

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Multi-Task Affinity Propagation Based Natural image Matting

Multi-Task Affinity Propagation Based Natural Image Matting

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IEEE International Conference on image processing

作者： Renkai Zhang Nong Sang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

image matting, aiming to accurately extract foreground objects by estimating their opacity against the background, has made remarkable progress through deep-learning approaches. Nevertheless, the majority of these methods require a user-defined auxiliary input, such as a trimap, which limits their applications in real-world scenarios. There are many auxiliary input-free methods that have been proposed by now, and some of them adopt a multi-task learning framework that includes a shared encoder and two separate decoders. However, these methods lack interactions between the two decoders, or interactions are implemented through simple summation or concatenation. Unfortunately, the integration of different features may cause negative transfer and limit the model performance due to the invisible information transmission process. To address the issue, we introduce the Pattern-Affinitive Propagation Module (PAP) to explicitly model cross-task propagation and task-specific propagation. Furthermore, image matting not only requires high-resolution detail features, but also semantic features. However, current CNN-based methods have limited receptive fields, making it challenging to capture global semantic features. Therefore, we design a module that integrates Dilated Convolution and Spectral Transformer (DSM), which can effectively capture global features and enhance global-local feature fusion. Extensive experiments on AM-2k and P3M-10k datasets demonstrate the superiority of our method.

关键词： Fuses Computational modeling Semantics Urban areas Information processing Multitasking Feature extraction

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Estimation and Utilization of the Geomagnetic Field Inhomogeneities Using the Relaxation Characteristics of the FID Signal in an Overhauser Magnetometer

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

作者： Luo, Wang Hu, Xiangyun Dong, Haobin Ge, Jian Liu, Lichao Feng, Ke Zhang, Yongchao Huang, Lishan She, Jinhua China Univ Geosci Sch Geophys & Geomat Wuhan 430074 Peoples R China Hubei Subsurface Multiscale Image Key Lab Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Univ British Columbia Sch Engn Kelowna BC Canada

Accurate measurement of geomagnetic field inhomogeneity commonly necessitates the use of two or more magnetic sensors for differential measurements, and most of the geomagnetic sensors are unusable in large inhomogeneous fields. To address these issues, this article presents a new approach for estimating the geomagnetic field's inhomogeneities based on a single Overhauser magnetometer. First, we establish the improved free induction decay (FID) signal model by integrating the phases of all protons over the entire Overhauser sensor in arbitrary inhomogeneous fields, which enables the inversion of the geomagnetic field gradient using the relaxation characteristics of the FID signal. Then, we propose a composite algorithm designed to accurately derive the relaxation parameters of the FID signal by carefully extracting and denoising its envelope and after that calculate the gradient of the geomagnetic field by the above FID signal model. Moreover, we designed a specialized Overhauser magnetic sensor prototype for measuring geomagnetic gradients and conducted experiments on a dedicated experimental platform. The designed prototype successfully measured the magnetic gradient even under high gradients of up to 10 005 nT/m, yielding a measurement error of 15.83%, with one sensor in the experiments. In addition, we employed this method to successfully detect unexploded ordnance (UXO) using the transverse relaxation time of the FID signal as an indicator. This application further validates the effectiveness and practicality of our proposed methodology.

关键词： Magnetometers Magnetic resonance imaging Sensors Nonhomogeneous media Magnetic sensors Protons Magnetization Envelope extraction free induction decay (FID) signal geomagnetic field inhomogeneity Overhauser magnetometer unexploded ordnance (UXO) detection

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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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Replace Anyone in Videos

arXiv

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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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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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A dynamic multi-objective optimization approach for integrated timetabling and vehicle scheduling

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Shen, Yindong Xie, Wenliang Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol MOE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Dynamic integrated timetabling and vehicle scheduling (D-ITVS) is essential for mitigating the negative impacts of service disruptions arising from the stochastic nature of traffic flow and passenger demand fluctuations in public transit. Existing optimization approaches for the D-ITVS problem typically conduct optimization independently at each rescheduling stage. In contrast, this paper proposes a novel dynamic multi-objective optimization approach that considers evolving patterns across different rescheduling stages from a holistic perspective. This approach formulates the optimization problem for all rescheduling stages during a day's operation as a dynamic multi-objective optimization problem, modeled using a dynamic time-space network flow framework. To leverage these evolving patterns for achieving optimal solutions throughout, a dynamic multi-objective optimization approach for the D-ITVS problem (DMO-TVS) is introduced. The DMO-TVS approach learns the evolving patterns, and incorporates a dynamic solution representation alongside three key mechanisms: (1) change detection mechanism, (2) change response mechanism, and (3) multi-objective optimization mechanism. These mechanisms work in tandem to dynamically adjust the initial solution set at each rescheduling stage based on predicted optimal solutions derived from learned evolving patterns, balance conflicting objectives in the DITVS problem, and select optimal solutions with diversity throughout the optimization process. Experimental results demonstrate that the dynamic multi-objective optimization approach is capable of generating timetables and vehicle schedules with reduced costs, enhanced robustness, and improved convergence and diversity across all rescheduling stages. By balancing operational costs and passenger service quality, these improvements benefit transit operators, and during daily operations, passengers enjoy reduced travel costs and enhanced service reliability.

关键词： Public transport Timetabling Vehicle scheduling Dynamic multi-objective optimization

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