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Chinese control Conference (CCC)

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Chengdu Sichuan China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa China

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known “Stanford marshmallow experiment” (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control; different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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System level modeling and optimization of high temperature proton exchange membrane electrolyzer system considering recirculated hydrogen as carrier gas

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第12期46卷 17590-17601页

作者： Zhao, Dongqi Guo, Meiting He, Qijiao Yu, Jie Xu, Qidong Xia, Zhiping Li, Xi Ni, Meng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Min Wuhan Hubei Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Hung HomKowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Kowloon Hung Hom Hong Kong Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen Guangdong Peoples R China

High temperature proton exchange membrane electrolyzer cell is promising for hydrogen production using excess renewable power. However, system level simulation and optimization are lacking, which is critical for practical application. In this research, system level models are developed, considering the use of recirculated hydrogen and steam as carrier gases. It is found that the use of recirculated hydrogen shows higher efficiency than steam as a carrier gas due to lower thermal energy input, especially under the large steam stoichiometric excess ratio. Moreover, the effects of the working temperature and gas flow rate on the energy demand and energy efficiency are studied. The hydrogen recirculation system is more sensitive to the effects of temperature, and the steam carrier gas system has a lower thermoneutral voltage. Compared with the hydrogen recirculation system, the current density that corresponds to the optimal energy efficiency point of the steam carrier gas system is higher. At a gas flow rate of 0.015 mol s(-1), the current density at the optimal energy efficiency point of the hydrogen recirculation system is 7700 A m(-2), while that of the steam carrier gas system is 8800 A m(-2). The study can guide the integration of the electrolyzer system.

关键词： hydrogen recirculation system operating parameter research proton exchange membrane electrolyzer cell system level numerical modelling system structure optimization

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Dynamic hierarchical modeling and control strategy of high temperature proton exchange electrolyzer cell system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第53期47卷 22302-22315页

作者： Zhao, Dongqi Xia, Zhiping Guo, Meiting He, Qijiao Xu, Qidong Li, Xi Ni, Meng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan Hubei Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Hung HomKowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Hung Hom Kowloon Hong Kong Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen Guangdong Peoples R China

High temperature proton exchange membrane electrolyzer cells (HT-PEMECs) show faster reaction kinetics than the low temperature PEMECs (LT-PEMECs) and are suitable for utilizing waste heat from the industry. However, dynamic modeling and control of HT-PEMECs are still lacking, which is critical for integrating the HT-PEMECs with fluctuating renewable power. In this study, hierarchical models are developed to investigate the transient behavior of the HT-PEMEC system with hydrogen recirculation. It is observed that the maximum efficiency point of the reference power can be reached by cooperatively adjusting the current density and anode inlet gas flow rate, and the application of artificial neural networks can accurately predict the operating conditions at the points of maximum efficiency. Moreover, the proposed cooperative model predictive control strategy not only improves the efficiency (about 1.2%) during dynamic processes but also avoids the problem of reactant starvation. This study provides useful information to understand the dynamic behaviors of HT-PEMECs driven by excess renewable power. (C) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hierarchical system model Multiphysics analysis Hydrogen recirculation system System identification Cooperative model predictive control

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Taming Consistency Distillation for Accelerated Human image Animation

arXiv

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arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Yuan, Hangjie Wei, Yujie 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 China Alibaba Group China Fudan University China

Recent advancements in human image animation have been propelled by video diffusion models, yet their reliance on numerous iterative denoising steps results in high inference costs and slow speeds. An intuitive solution involves adopting consistency models, which serve as an effective acceleration paradigm through consistency distillation. However, simply employing this strategy in human image animation often leads to quality decline, including visual blurring, motion degradation, and facial distortion, particularly in dynamic regions. In this paper, we propose the DanceLCM approach complemented by several enhancements to improve visual quality and motion continuity at low-step regime: (1) segmented consistency distillation with an auxiliary light-weight head to incorporate supervision from real video latents, mitigating cumulative errors resulting from single full-trajectory generation;(2) a motion-focused loss to centre on motion regions, and explicit injection of facial fidelity features to improve face authenticity. Extensive qualitative and quantitative experiments demonstrate that DanceLCM achieves results comparable to state-of-the-art video diffusion models with a mere 2-4 inference steps, significantly reducing the inference burden without compromising video quality. The code and models will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

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Structure-aware human pose estimation with graph convolutional networks

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PATTERN RECOGNITION 2020年 106卷 107410-107410页

作者： Bin, Yanrui Chen, Zhao-Min Wei, Xiu-Shen Chen, Xinya Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Megvii Technol Ltd Megvii Res Nanjing Nanjing Peoples R China

Human pose estimation is the task of localizing body key points from still images. As body key points are inter-connected, it is desirable to model the structural relationships between body key points to further improve the localization performance. In this paper, based on original graph convolutional networks, we propose a novel model, termed Pose Graph Convolutional Network (PGCN), to exploit these important relationships for pose estimation. Specifically, our model builds a directed graph between body key points according to the natural compositional model of a human body. Each node (key point) is represented by a 3-D tensor consisting of multiple feature maps, initially generated by our backbone network, to retain accurate spatial information. Furthermore, attention mechanism is presented to focus on crucial edges (structured information) between key points. PGCN is then learned to map the graph into a set of structure-aware key point representations which encode both structure of human body and appearance information of specific key points. Additionally, we propose two modules for PGCN, i.e., the Local PGCN (L-PGCN) module and Non-Local PGCN (NL-PGCN) module. The former utilizes spatial attention to capture the correlations between the local areas of adjacent key points to refine the location of key points. While the latter captures long-range relationships via non-local operation to associate the challenging key points. By equipping with these two modules, our PGCN can further improve localization performance. Experiments both on single- and multi-person estimation benchmark datasets show that our method consistently outperforms competing state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Human pose estimation Graph convolutional networks key points structural relations

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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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IEEE Transactions on Vehicular Technology 2025年

作者： Tang, Changbing Pan, Linchao Chen, Jie Liu, Yang Lai, Jingang The College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China The School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China The Hangzhou School of Automation Zhejiang Normal University Hangzhou311231 China The College of Electronic Engineering National University of Defense Technology Hefei230037 China The School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Education Ministry of China China Huazhong University of Science and Technology Wuhan430074 China

To execute a variety of collaborative tasks, the cooperation for unmanned aerial vehicles (UAVs) with complicated interactions under dynamic environments is a challenging and critical issue. This paper studies the cooperation issue for UAVs under dynamic environments through an approach of game theory-reinforcement learning (GT-RL) approach, which combines the advantages of both game theory and reinforcement learning. First, to cope with the complicated interactions of UAVs, the cluster of UAVs is modeled as a public goods game with asymmetrical environmental feedback. Then, reinforcement learning is adopted to optimize decision-making of UAVs under unknown and dynamic environments through comparing the dynamic behaviors of UAVs, where a novel dynamics system offers a more comprehensive understanding on cooperative behavior among UAVs. Finally, the simulation results show that the GT-RL approach can effectively promote cooperation among UAVs in completing the collaborative tasks. © 1967-2012 IEEE.

关键词： Adversarial machine learning

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Fast Candidate Region Extraction for SAR Ship Target

Fast Candidate Region Extraction for SAR Ship Target

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.

关键词： Deep learning Training Redundancy Radar polarimetry Inference algorithms Indexes Marine vehicles

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Channel Reflection: Knowledge-Driven Data Augmentation for Eeg-Based Bcis

SSRN

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SSRN 2024年

作者： Wang, Ziwei Li, Siyang Luo, Jingwei Liu, Jiajing Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China China Electronic System Technology Co. Ltd. Beijing100089 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: 1) CR is effective, i.e., it can noticeably improve the classification accuracy;2) CR is robust, i.e., it consistently outperforms other data augmentation approaches in the literature;and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Head-free, Human Gaze-driven Assistive Robotic System for Reaching and Grasping 40

Head-free, Human Gaze-driven Assistive Robotic System for Re...

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40th Chinese control Conference (CCC)

作者： Yang, Bo Huang, Jian Sun, Menglin Huo, Jun Li, Xiaolong Xiong, Caihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9789881563804

Patients with limb dysfunction have limited mobility, which prevents them from performing daily activities. We have developed an assistive robot system with an intuitive head free gaze interface. The system consists of multiple modules, including 3D gaze estimation, head free coordinate transformation, intention recognition, and robot trajectory planning. The robotic assistive system obtains clues from the user's gaze to decode their intentions and implement actions. This allows the user only needs to look at the objects to make the robot system reach, grasp, and bring them to the user. The 3D gaze estimation is evaluated with 5 subjects, showing an overall accuracy of 5.53 +/- 1.2 cm. The integrated system's experimental results show that the success rate is 96% in the implementation of automatic trajectory planning, and the success rate is 92% in the implementation of fixation-based trajectory planning. Finally, the results and work required to improve the system are discussed.

关键词： 3D gaze estimation Intention estimation Gaze-based control Assistive robot

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