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Neural-Network-Based Nonlinear Model Predictive Tracking control of a Pneumatic Muscle Actuator-Driven Exoskeleton

IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1478-1488页

作者： Yu Cao Jian Huang IEEE Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation ***,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic *** this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb *** dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain *** facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control *** stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system *** and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

关键词： Echo state Gaussian process model predictive control neural network pneumatic muscle actuators-driven exoskeleton

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Switching mechanism for TiO_2 memristor and quantitative analysis of exponential model parameters

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Chinese Physics B 2015年第8期24卷 598-604页

作者：王小平陈敏沈轶 School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

The memristor, as the fourth basic circuit element, has drawn worldwide attention since its physical implementation was released by HP Labs in 2008. However, at the nano-scale, there are many difficulties for memristor physical realization. So a better understanding and analysis of a good model will help us to study the characteristics of a memristor. In this paper, we analyze a possible mechanism for the switching behavior of a memristor with a Pt/TiO2/Pt structure, and explain the changes of electronic barrier at the interface of Pt/TiO2. Then, a quantitative analysis about each parameter in the exponential model of memristor is conducted based on the calculation results. The analysis results are validated by simulation results. The efforts made in this paper will provide researchers with theoretical guidance on choosing appropriate values for(α, β, χ, γ) in this exponential model.

关键词： memristor switching behavior electronic barrier exponential model

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RLSESN-based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM-TS actuators

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International Journal of intelligent Computing and Cybernetics 2012年第1期5卷 91-110页

作者： Jun Wu Jian Huang Yongji Wang Kexin Xing Key Laboratory of Image Processing and Intelligent Control Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring(PM-TS)for finger *** has complex nonlinear dynamics,which makes PM modelling *** realize high-accurate tracking for the robotic hand,an Echo State Network(ESN)-based PID adaptive controller is proposed,even though the plant model is ***/methodology/approach-To drive a single joint of rehabilitation robotic hand,the paper proposes a new PM-TS actuator comprising a Pneumatic Muscle(PM)and a Torsion Spring(TS).Based on the novel actuator,a wearable robotic hand is *** employing the model-free approximation capability of ESN,the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic *** ESN together with Recursive Least Square(RLS)is called a RLSESN,where the ESN output weight matrix is updated by the online RLS learning ***–Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID *** addition,they also verify the effectiveness of the proposed rehabilitation robotic ***/value–A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is *** utilizing the new PM-TS actuator,a novel wearable rehabilitation robotic hand for finger therapy is *** on the unknown plant model,the RLSESN_PID controller is proposed to attain satisfactory performance.

关键词： Robotics control systems controllers Actuators Adaptive system theory Wearable rehabilitation robotic hand Echo state network Pneumatic muscle Trajectory tracking

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A membrane-inspired algorithm with a memory mechanism for knapsack problems

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第8期14卷 612-622页

作者： Juan-juan HE Jian-hua XIAO Xiao-long SHI Tao SONG Key Laboratory of Image Processing and Intelligent Control School of AutomationHuazhong University of Science and Technology The Logistics Research Center Nankai University

Membrane algorithms are a class of distributed and parallel algorithms inspired by the structure and behavior of living cells. Many attractive features of living cells have already been abstracted as operators to improve the performance of algorithms. In this work, inspired by the function of biological neuron cells storing information, we consider a memory mechanism by introducing memory modules into a membrane algorithm. The framework of the algorithm consists of two kinds of modules (computation modules and memory modules), both of which are arranged in a ring neighborhood topology. They can store and process information, and exchange information with each other. We test our method on a knapsack problem to demonstrate its feasibility and effectiveness. During the process of approaching the optimum solution, feasible solutions are evolved by rewriting rules in each module, and the information transfers according to directions defined by communication rules. Simulation results showed that the performance of membrane algorithms with memory cells is superior to that of algorithms without memory cells for solving a knapsack problem. Furthermore, the memory mechanism can prevent premature convergence and increase the possibility of finding a global solution.

关键词： Membrane algorithm Memory mechanism Knapsack problem

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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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Research of invariant moments and improved support vector machine in micro-targets identification

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Journal of Applied Sciences 2008年第21期8卷 3969-3974页

作者： Zeng, Xiangjin Huang, Xinhan Wang, Min Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

In order to identify multi micro objects, an improved support vector machine algorithm is present, which employs invariant moments based edge extraction to obtain feature attribute and then presents a heuristic attribute reduction algorithm based on rough set's discernibility matrix to obtain attribute reduction, with using support vector machine to identify and classify the targets. According to the feature attribute, the effectiveness of identifying multi micro objects using support vector machine is compared with the proposed improved support vector machine classification method. The experiment results under micro vision environment show that the proposed improved support vector machine classification method can meet the system application requirement, with the resolution is 95%. © 2008 Asian Network for Scientific Information.

关键词： Support vector machines

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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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Research advances and prospect of spiking neural P systems

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第12期31卷 2090-2096页

作者： Pan, Lin-Qiang Zhang, Xing-Yi Zeng, Xiang-Xiang Wang, Jun Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Spiking neural P systems are a new computing model inspired from the biological phenomena that in the brain the neurons cooperate to deal with spikes by axons. Since it has been shown that they have powerful computational capability and potential capability in solving computationally hard problems, more and more people begin to get interested in this field. This paper firstly introduces the formal definition of standard spiking neural P systems and some notions which are often used in this area;then, several extensions of the original spiking neural P systems are summarized, that are: Extented SN P system;SN P system with exhaustive use of rules;Asynchronous SN P system;Sequential SN P system. Also, the results on the topic of spiking neural P systems are briefly recalled in two aspects: computational completeness and computational efficiency. In the end, two more important future research directions on spiking neural P systems are pointed out. Specifically, one interesting topic is to develop a new computing model which is more "realistic";another topic is to consider how to use these models in biological modeling and simulation.

关键词： Computational efficiency

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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Dynamic Color Transform Networks for Wheat Head Detection

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Plant Phenomics 2022年第1期4卷 395-408页

作者： Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of EducationSchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina

Wheat head detection can measure wheat traits such as head density and head *** wheat breeding largely relies on manual observation to detect wheat heads,yielding a tedious and inefficient *** emergence of affordable camera platforms provides opportunities for deploying computer vision(CV)algorithms in wheat head detection,enabling automated measurements of wheat *** wheat head detection,however,is challenging due to the variability of observation circumstances and the uncertainty of wheat head *** this work,we propose a simple but effective idea—dynamic color transform(DCT)—for accurate wheat head detection.

关键词： Wheat breeding wheat

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