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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yikun Zhang Jian Huang Jinqi Yu Yaonan Zhu Yasuhisa Hasegawa Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen China Department of Micronano Mechanical Science and Engineering Nagoya University Nagoya Japan

The relative-posture-fixed human following is critical for the human-robot interaction and cooperation in daily scenes such as domestic service and healthcare. However, few of previous researches have discussed the difficulties faced by mobile robots in the real world, including the limitation of sensing range and hindrance from obstacles. For practicality, in this paper we investigate the relative-posture-fixed following control with collision avoidance and visibility constraints. The control task is formulated as a receding-horizon optimization problem and solved under the Nonlinear Model Predictive control (NMPC) framework, while an Extended Kalman Filter (EKF) based human trajectory predict algorithm is integrated into the robot controller. The effectiveness of the proposed algorithm is verified in a simulation, and the results demonstrate a promising performance of our controller for the human-following task in complicated environments.

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CPTNet: Cascade Pose Transform Network for Single image Talking Head Animation 15th

CPTNet: Cascade Pose Transform Network for Single Image Talk...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhang, Jiale Xian, Ke Liu, Chengxin Chen, Yinpeng Cao, Zhiguo Zhong, Weicai Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huawei CBG Consumer Cloud Service Search Product and Big Data Platform Department Xi’an China

ISBN: (纸本)9783030695378

We study the problem of talking head animation from a single image. Most of the existing methods focus on generating talking heads for human. However, little attention has been paid to the creation of talking head anime. In this paper, our goal is to synthesize vivid talking heads from a single anime image. To this end, we propose cascade pose transform network, termed CPTNet, that consists of a face pose transform network and a head pose transform network. Specifically, we introduce a mask generator to animate facial expression (e.g., close eyes and open mouth) and a grid generator for head movement animation, followed by a fusion module to generate talking heads. In order to handle large motion and obtain more accurate results, we design a pose vector decomposition and cascaded refinement strategy. In addition, we create an anime talking head dataset, that includes various anime characters and poses, to train our model. Extensive experiments on our dataset demonstrate that our model outperforms other methods, generating more accurate and vivid talking heads from a single anime image. © 2021, Springer Nature Switzerland AG.

关键词： Animation

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Finite-Time Synchronization of Markovian Coupled Neural Networks With Delays via Intermittent Quantized control: Linear Programming Approach

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5268-5278页

作者： Tang, Rongqiang Su, Housheng Zou, Yi Yang, Xinsong Chongqing Univ Coll Comp Sci Chongqing 400044 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 Minist Educ Wuhan 430074 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China

This article is devoted to investigating finite-time synchronization (FTS) for coupled neural networks (CNNs) with time-varying delays and Markovian jumping topologies by using an intermittent quantized controller. Due to the intermittent property, it is very hard to surmount the effects of time delays and ascertain the settling time. A new lemma with novel finite-time stability inequality is developed first. Then, by constructing a new Lyapunov functional and utilizing linear programming (LP) method, several sufficient conditions are obtained to assure that the Markovian CNNs achieve synchronization with an isolated node in a settling time that relies on the initial values of considered systems, the width of control and rest intervals, and the time delays. The control gains are designed by solving the LP. Moreover, an optimal algorithm is given to enhance the accuracy in estimating the settling time. Finally, a numerical example is provided to show the merits and correctness of the theoretical analysis.

关键词： Delay effects Synchronization Delays Artificial neural networks Uncertainty Topology Linear programming Finite-time synchronization (FTS) linear programming (LP) Markovian topology quantized intermittent control time delay

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Lite-HRNet: A lightweight high-resolution network

arXiv

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arXiv 2021年

作者： Yu, Changqian Xiao, Bin Gao, Changxin Yuan, Lu Zhang, Lei Sang, Nong Wang, Jingdong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Microsoft

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. We find that the heavily-used pointwise (1 × 1) convolutions in shuffle blocks become the computational bottleneck. We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1 × 1) convolutions in shuffle blocks. The complexity of channel weighting is linear w.r.t the number of channels and lower than the quadratic time complexity for pointwise convolutions. Our solution learns the weights from all the channels and over multiple resolutions that are readily available in the parallel branches in HRNet. It uses the weights as the bridge to exchange information across channels and resolutions, compensating the role played by the pointwise (1 × 1) convolution. Lite-HRNet demonstrates superior results on human pose estimation over popular lightweight networks. Moreover, Lite-HRNet can be easily applied to semantic segmentation task in the same lightweight manner. The code and models have been publicly available at https://***/HRNet/Lite-HRNet. © 2021, CC BY.

关键词： Convolution

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Large-scale group decision-making to facilitate inter-rater reliability of human-factors analysis for the railway system

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期228卷

作者： Zhou, Jian-Lan Tu, Ren-Fang Xiao, Hai 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 China Three Gorges Univ Engn Res Ctr Ecoenvironm Three Gorges Reservoir Re Minist Educ Yichang 443002 Peoples R China

The interdependency analysis of human factors usually needs to identify and label factors from accident/incident reports using a binary framework. The labeling procedure involves enormous knowledge and requires a large group of raters, but the raters' opinions usually highly contradict each other. Thus, inter-rater reliability should be implemented to improve the labeling consistency. However, the inter-rater reliability coefficient is commonly computed on a category-by-category basis, which is inefficient especially when there are many raters. To overcome this deficiency, a feedback-autonomy-based consensus model is proposed to determine the inter-rater reliability of human-factors labeling results involving a large group of raters. The proposed model can manage various non-cooperative behaviors and provide feedback adjustment suggestions to raters for the labeling procedure. Notably, the autonomy mechanism allows raters to freely choose and adjust labels referring to the reference opinions without causing over-adjustment issue. The inter-rater reliability and the interdependency results are performed on 279 accident/incident reports. We apply a commercial tool to obtain the reference results, which are consistent with the unanimous group opinions. Besides, other comparative studies clarify the advantages of the proposed consensus model. The proposed model is convenient for reducing conflicts among raters that usually exist in human-factors analysis.

关键词： Inter -rater reliability Human -factors analysis HFACS Large-scale group decision -making Railway system

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Echo State Network-Enhanced Super-Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第6期27卷 5107-5118页

作者： Zhang, Mengshi Huang, Jian Cao, Yu Xiong, Cai-Hua Mohammed, Samer Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Res Inst Shenzhen 518060 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 Univ Paris Est Creteil F-94400 Vitry Sur Seine France

In this article, a robust trajectory tracking control method is proposed for the passive gait training exoskeleton system driven by pneumatic muscles (PMs). Conventional model-based controllers suffer from limitations with respect to model uncertainties and external disturbances caused by PMs and complex robotic systems. An echo state network (ESN) is used in this study to approximate the model uncertainties and external disturbances of the exoskeleton system. Based on the approximation of ESN, a super-twisting control (STC) algorithm is designed to guarantee accurate tracking control at both hip and knee joint levels. Because there are both weight error and global approximation error in neural network approximation, a standard quadratic form cannot be obtained which plays an important role in the stability analysis of the traditional super twisting algorithm. To solve this issue, a dedicated positive definite matrix is constructed in this article, which bridges the ESN and STC by providing a parameter selection criteria. The stability with respect to the tracking problem of the exoskeleton system is then guaranteed according to the Lyapunov theorem. Both numerical simulations and experimental results present better tracking accuracy and robustness compared with the traditional sliding mode control and STC.

关键词： Echo state network (ESN) lower limb exoskeleton pneumatic muscle (PM) actuator rehabilitation robotics super-twisting control (STC)

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Testbed implementation of reinforcement learning-based demand response

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APPLIED ENERGY 2021年 297卷 117131-117131页

作者： Zhang, Xiongfeng Lu, Renzhi Jiang, Junhui Hong, Seung Ho Song, Won Seok Hanyang Univ Dept Elect Engn Ansan 15588 South Korea Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hanyang Univ Nestfield Co Ltd 211 BI CtrERICA Campus Ansan 15588 South Korea

Demand response (DR) has been acknowledged as an effective method to improve the stability, and financial efficiency of power grids. During operation of a DR program, there are usually multiple interactions among different grid entities, which complicates decision-making processes with respect to grid operations. Recently, reinforcement learning (RL) has attracted increasing attention for managing complex decision-making problems, owing to its self-learning capacity. Several theoretical RL-based approaches have been proposed for addressing various DR issues, but the practical feasibility of these theoretical approaches remains to be proven. In this paper, a conceptual architecture is firstly proposed to support DR management of a diversified facility in the context of a price-based DR environment. Secondly, exhaustive guidelines are provided to illustrate how to implement a multi-agent RL-based algorithm in the constructed DR management system. Afterwards, a laboratory-level testbed was set up to evaluate the effectiveness of the deployed DR algorithm. The experimental evaluation results show that the RL-based DR algorithm takes about 20s and 50 episodes to achieve optimal load control policy. By executing the optimal operation policy, the overall energy consumption during the highest price period (i.e., 15:00-18:00) is significantly reduced by 133.6% compared with the lowest price period (i.e., 02:00-05:00).

关键词： Reinforcement learning (RL) Demand response (DR) Energy management Practical implementation

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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Multistability of delayed fractional-order competitive neural networks

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NEURAL NETWORKS 2021年 140卷 325-335页

作者： Zhang, Fanghai Huang, Tingwen Wu, Qiujie Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar Anhui Univ Sch Internet Hefei Peoples R China

This paper is concerned with the multistability of fractional-order competitive neural networks (FCNNs) with time-varying delays. Based on the division of state space, the equilibrium points (EPs) of FCNNs are given. Several sufficient conditions and criteria are proposed to ascertain the multiple O(t(-alpha))-stability of delayed FCNNs. The O(t(-alpha))-stability is the extension of Mittag-Leffler stability of fractional-order neural networks, which contains monostability and multistability. Moreover, the attraction basins of the stable EPs of FCNNs are estimated, which shows the attraction basins of the stable EPs can be larger than the divided subsets. These conditions and criteria supplement and improve the previous results. Finally, the results are illustrated by the simulation examples. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Fractional-order competitive neural networks Multistability Attraction basins Delays

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Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022年第5期16卷 926-938页

作者： Yang, Chao Wang, Xiaoping Chen, Zhanfei Zhang, Sen Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Classical conditioning (CC) and operant conditioning (OC), also known as associative memory, are two of the most fundamental and critical learning mechanisms in the biological brain. However, the existing designs of associative memory memristive circuits mainly focus on CC, and few studies have used memristors to imitate OC at the behavioral level, as well as the OC-CC cascaded associative memories that are widespread in biological learning processes. This work proposes an OC-CC cascaded circuit composed of OC and CC circuits. With the OC memristive circuit, bio-like functions such as random exploration, feedback learning, experience memory, and experience-based decision-making are achieved, which enables the circuit to continuously reshape its own memories and actions to adapt to changing environments. By cascading it with the CC memristive circuit that has the functions of associative learning, forgetting, generalization, and differentiation, the OC-CC cascaded circuit implements richer associative memories and has stronger environmental adaptability. Finally, the proposed circuits can perform on-line in-situ learning and in-memory computing. This is a more brain-like processing method, which is different from the von Neumann architecture. The simulation results of the proposed circuits in PSPICE show that they can simulate the above functions and have advantages in power consumption and hardware overhead. This work provides a possible realization idea for large-scale bionic learning.

关键词： Memristors Associative memory Mice Electric shock Behavioral sciences Integrated circuit modeling In-memory computing Associative memory bionic learning classical conditioning in-memory computing memristor operant conditioning

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