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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

Journal of Systems Engineering and Electronics 2021年第2期32卷 365-379页

作者： SHEN Yindong PENG Liwen LI Jingpeng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computing Science and Mathematics University of StirlingStirling FK94LAUK

The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service *** solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local *** results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA *** addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale *** experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

关键词： multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight

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Coupled Urban Risks: A Complex Systems Perspective with a People-Centric Focus

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ENGINEERING 2025年第1期44卷 44-50页

作者： Ouyang, Min Cheng, Zekai Ma, Jiaxin Wang, Hongwei Mitoulis, Stergios Aristoteles 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 Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT England

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

关键词： Coupled urban risks People-centric Risk management

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Large-Scale Multiobjective Optimization via Reformulated Decision Variable Analysis

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第1期28卷 47-61页

作者： He, Cheng Cheng, Ran Li, Lianghao Tan, Kay Chen Jin, Yaochu Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan 430074 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Bielefeld Univ Fac Technol Chair Nat Inspired Comp & Engn D-33615 Bielefeld Germany Univ Surrey Dept Comp Sci Guildford GU2 7XH England

With the rising number of large-scale multiobjective optimization problems (LSMOPs) from academia and industries, some multiobjective evolutionary algorithms (MOEAs) with different decision variable handling strategies have been proposed. Decision variable analysis (DVA) is widely used in large-scale optimization, aiming at identifying the connection between each decision variable and the objectives, and grouping those interacting decision variables to reduce the complexity of LSMOPs. Despite their effectiveness, existing DVA techniques require the unbearable cost of function evaluations for solving LSMOPs. We propose a reformulation-based approach for efficient DVA to address this deficiency. Then a large-scale MOEA is proposed based on reformulated DVA, namely, LERD. Specifically, the DVA process is reformulated into an optimization problem with binary decision variables, aiming to approximate different grouping results. Afterwards, each group of decision variables is used for convergence-related or diversity-related optimization. The effectiveness and efficiency of the reformulation-based DVA are validated by replacing the corresponding DVA techniques in two large-scale MOEAs. Experiments in comparison with six state-of-the-art large-scale MOEAs on LSMOPs with up to 2000 decision variables have shown the promising performance of LERD.

关键词： Optimization Statistics Sociology Convergence Iron Maintenance engineering Visualization Decision variable analysis (DVA) evolutionary algorithm (EA) large-scale optimization problem reformulation

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A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

A Recipe for Scaling up Text-to-Video Generation with Text-f...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Xiang Zhang, Shiwei Yuan, Hangjie Qing, Zhiwu Gong, Biao Zhang, Yingya Shen, Yujun Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at here.

关键词： Video on demand

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Spatiotemporal Residual Graph Attention Network for Traffic Flow Forecasting

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IEEE INTERNET OF THINGS JOURNAL 2023年第13期10卷 11518-11532页

作者： Zhang, Qingyong Li, Changwu Su, Fuwen Li, Yuanzheng Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Belt & Rd Joint Lab Measurement & Control Te Wuhan 430074 Peoples R China

Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatiotemporal characteristics of the traffic flow simultaneously, we propose a novel spatiotemporal residual graph attention network (STRGAT). First, the network adopts a deep full residual graph attention block, which performs a dynamic aggregation of spatial features regarding the node information of the traffic network. Second, a sequence-to-sequence block is designed to capture the temporal dependence in the traffic flow. The traffic flow data with weekly periodic dependencies are also integrated and STRGAT is used for traffic forecasting of traffic road networks. The experiments are conducted on three real data sets in California, USA. Results verify that our proposed STRGAT is able to learn the spatiotemporal correlation of traffic flow well and outperforms the state-of-the-art methods.

关键词： Index Terms-Attention mechanism graph attention network (GAT) spatiotemporal characteristics traffic flow forecasting

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A General Double-Input Synchronous Signal Processor for Imbalanced Vibration Mitigation in AMB-Rotor Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3823-3832页

作者： Ren, Gui-Ping Zhang, Hai-Tao Wu, Yue Ding, Han Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

vibration is an urgent yet challenging problem in active magnetic bearings (AMBs) rotor manufacturing due to the rotor mass imbalance effect. The virtue of active control in AMB systems lies in enabling substantial online mitigation of imbalanced vibrations. However, in practice, due to the lack of speed sensors in most of the existing AMB-rotor systems, efficient rotational speed feedback is still on the way. As a remedy, this article proposes a rotational speed sensor-free synchronous signal processor (SSP) with the double inputs: x- and y-axes direction displacement measurements of radial AMBs. The proposed SSP is capable of estimating the rotational speed and accordingly generating synchronous signals of the imbalanced vibrations by filtering noise in both directions. Such signals are afterward implemented as a feedforward compensator for eliminating the periodical imbalance effects. With the assistance of the Lyapunov theory, the conditions of the proposed SSP method together with the feedforward imbalance compensator are derived to guarantee the stability of the closed-loop AMB-rotor system. Extensive experimental results substantiate the effectiveness and superiority of the proposed SSP method in terms of imbalanced vibration suppression.

关键词： Vibrations Rotors Velocity control Coils Feedforward systems Asymptotic stability Manufacturing Frequency estimation machine tool control magnetic levitation vibration control

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A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

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IET RENEWABLE POWER GENERATION 2024年第0期

作者： Lian, Yicheng Li, Yuanzheng Zhao, Yong Li, Yang Liu, Zhiwei Wang, Jianxiao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

To achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day-ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two-stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst-case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration. This paper proposes an efficient computational approach to address the distributionally robust unit commitment problem in the context of high renewable energy penetration. The enhanced ambiguity set is designed to leverage more data information, resulting in a reliable and less conservative solution. Through model analysis, the paper introduces a novel reformulation of the distributionally robust unit commitment problem and proposes a new method for effectively solving it by identifying the worst-case confidence set. image

关键词： optimization renewable energy sources

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LEARNING DECENTRALIZED LINEAR QUADRATIC \surd REGULATORS WITH \bfitT REGRET

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SIAM JOURNAL ON control AND OPTIMIZATION 2024年第6期62卷 3341-3368页

作者： Ye, Lintao Chi, Ming Liao, Ruiquan Gupta, Vijay Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 439974 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 439974 Peoples R China Yangtze Univ Coll Petr Engn Wuhan 434023 Peoples R China Purdue Univ Elmore Sch Elect & Comp Engn W Lafayette IN 47907 USA

We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as T with the time horizon T for the case of partially nested information pattern. For more general information patterns, the optimal controller is unknown even if the system model is known. In this case, the regret of our controller is shown with respect to a linear suboptimal controller.

关键词： key words. decentralized control optimal control reinforcement learning online optimization

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Planning and Motion control for Underwater Bimanual Soft Manipulator in Underwater Grasping Task

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第4期29卷 2487-2498页

作者： Shen, Yi Tai, Ruochen Zhang, Jinghao Deng, Linan Yuan, Ye Su, Rong Zhang, Fumin Ding, Han Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Clear Water Bay Hong Kong Peoples R China

Researchers have always been curious about underwater biodiversity and energy diversity. However, autonomous intelligent and friendly manipulation in the restricted aquatic environment remains challenging. Inspired by the friendly bimanual manipulation of humans in the restricted aquatic environment, this work proposes an underwater bimanual soft manipulator (UBSM) system to complete the grasping task. Since an underwater object may move with flow during grasping, this work designs a hierarchical coordination framework consisting of an upper task planning level and a lower motion control level to integrate UBSM perception, interpretation of object state, task state, and UBSM actions. An upper planner at the task planning level is designed to realize a perception-action strategy consisting of two bimanual coordination modes. And an online-learning-inverse-kinematics controller at the motion control level is proposed to enable the UBSM to handle the randomness and adapt to the underwater environment. We validate the effectiveness of the designed upper planner and proposed motion controller in the underwater grasping task by experiments.

关键词： Bimanual cooperation discrete event system online learning soft robot underwater robot

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T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第2期71卷 423-432页

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518029 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Henan Key Lab Brain Sci & Brain Comp Interface Tec Zhengzhou Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This article proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.

关键词： Brain-computer interface electroencephalogram motor imagery test-time adaptation transfer learning

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