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第三十八届中国控制会议

作者： Haoyang Zhang Yan Yuan Xin Chen School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the optimal trajectory planning for a 6 degree-of-freedom(DOF) manipulator, where the energy minimization is considered under the constraint of power. Firstly, based on the establishment of kinematics model, the influence of main factors on power is analyzed by using Jacobian matrix and Lagrange equation. Then the energy consumption is calculated as the objective function by the power consumption of each joint, and in order to obtain the desired trajectory for the arm, each joint locus is approximated by a 3-5-3 spline interpolation curve. To this end, in order to obtain the most reliable solutions for trajectory planning, the improved adaptive genetic algorithm(IAGA) is used to optimize the energy under the constraint of power, speed and acceleration, and the crossover rate and mutation rate are effectively adjusted with ***, we conduct the simulation by using the dynamics analysis software-ADAMS. The results demonstrate the effectiveness of minimizing the optimized energy in the case of constrained power mainly.

关键词： Energy optimization trajectory planning improved adaptive genetic algorithm

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Voltage Sensitivity of Tag Antennas for Inductively Coupled RFID systems

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Cheng Jin Jixuan Zhu School of Automation China University of Geosciences (Wuhan) Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

This paper describes a parameter of voltage sensitivity to recognize the performance differences of tag antennas for inductively coupled RFID systems. Based on the equivalent circuit model of the RFID tag and reader, an expression for the voltage sensitivity is educed. Then, the design steps of measuring platform to obtain the voltage sensitivity of tag antenna are introduced in detail. Finally, the feasibility of the proposed method was verified with the market available tag antennas through the measuring platform.

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A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 961-968页

作者： Xiuqin Shang Dayong Shen Fei-Yue Wang Timo R. Nyberg the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences (CAS)Beijing 100190China the Qingdao Academy of Intelligent Industries Qingdao 266109China IEEE the Beijing Enineering Research Center of Intelligent Systems and Technology Institute of AutomationCASBeijing 100190China the Cloud Computing Center CASDongguan 523808China the School of Science at Aalto University AaltoFinland

We study the fabric spreading and cutting problem in apparel *** the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment *** reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be *** propose an iterated greedy algorithm for solving the fabric spreading and cutting *** algorithm contains a constructive procedure and an improving *** the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay *** improving loop will run until it cannot obtain any smaller lay set or the time limit is *** experiment results on 500 cases show that the proposed algorithm is effective and efficient.

关键词： Construction and improvement cutting and packing fabric spreading and cutting heuristic algorithm

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Large-scale hyperspectral image clustering using contrastive learning

arXiv

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arXiv 2021年

作者： Cai, Yaoming Zhang, Zijia Liu, Yan Ghamisi, Pedram Li, Kun Liu, Xiaobo Cai, Zhihua School of Computer Science China University of Geosciences Wuhan430074 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria College of Electronic Science and Technology National University of Defense Technology Changsha410073 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging withincluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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AI Graphical Programming Learning Platform for Children

AI Graphical Programming Learning Platform for Children

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Chinese automation Congress (CAC)

作者： Hongxia Zhao Xiwei Liu Xiaoyan Gong Qiang Li Sifeng Jing Yaofeng Xue The Stale Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China University of Chinese Academy of Sciences Beijing China East China Normal University Shanghai China

ISBN: (纸本)9781665426480

In this era of rapid development of artificial intelligence, the truth of competition in the future is the talent. Countries all over the world attach great importance to the programming and artificial intelligence education of children. In the future, the theoretical development and practical application of this emerging field will usher in a period of rapid development. This paper presents an artificial intelligence graphical programming learning platform based on Jetson Nano, which combines artificial intelligence education with modular software programming, provides students with a more open, intelligent, and interesting programming environment through the cross-platform development mode of "cloud service + intelligent hardware", and adopts visual programming to reduce the difficulty of artificial intelligence programming. The platform can fully tap learning resources, reduce education costs, and make artificial intelligence education more intuitive and humanized.

关键词： Visualization Costs automation Education Learning (artificial intelligence) Games Software

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Nonparametric Different-Feature Selection Using Wasserstein Distance

Nonparametric Different-Feature Selection Using Wasserstein ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Wenbo Zheng Fei-Yue Wang Chao Gou State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

In this paper, we propose a feature selection method that characterizes the difference between two kinds of probability distributions. The key idea is to view the feature selection problem as a sparsest k-subgraph problem that considers Wasserstein distance between the studied two probability distributions. Our method does not presume any specific parametric models on the data distribution and is non-parametric. It outperforms existing Kullback-Leibler divergence based approaches, since we do not require two distributions to overlap. This relaxation makes our method work in many problems in which Kullback-Leibler divergence based methods fail. We also design a fast calculation algorithm using dynamic programming. Our experimental results show that our method outperforms the current method in both computation accuracy and speed.

关键词： Heuristic algorithms Conferences Tools Feature extraction Probability distribution Dynamic programming Parametric statistics

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A pigeon-inspired optimization algorithm for many-objective optimization problems

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Science China(Information Sciences) 2019年第7期62卷 131-138页

作者： Zhihua CUI Jiangjiang ZHANG Yechuang WANG Yang CAO Xingjuan CAI Wensheng ZHANG Jinjun CHEN Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Beijing Institute of Petrochemical Technology Faculty of Information Technology Beijing University of Technology State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences School of Software University of Technology Sydney

Dear editor,Swarm intelligence optimization algorithms are inspired by the behaviour of biological groups in nature. Such algorithms have the advantages of a clear structure, simple operation, comprehensible principles, strong parallelism, effective search abilities, and strong robustness. They can effectively solve difficult problems that traditional methods cannot. Pigeon-inspired optimization (PIO), a novel biomimetic swarm intelligence optimization algorithm, was proposed by Duan and Qiao in

关键词： A pigeon-inspired optimization algorithm for many-objective optimization problems Pareto

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Research on Vibration Isolation control of Six wheel-legged Robot Based on Impedance control

Research on Vibration Isolation Control of Six wheel-legged ...

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第三十九届中国控制会议

作者： Binkai Yue Shoukun Wang Zhihua Chen Kang Xu Junzheng Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology

The electric parallel six wheel-legged robot（EPSWLR） is characterized by heavy load and fast moving speed. But the body will shake and vibrate while passing through rough roads, which affects its stability. In order to solve this problem, the single-leg structure based on Stewart structure was equivalent to the impedance system composed of mass block, spring and damping in this paper. Due to changes in environmental forces and environmental impedance parameters caused by environmental changes, the traditional impedance control（IC） still causes force error at the foot end of robot and thus results in poor vibration isolation effect. Therefore, a new adaptive algorithm was proposed in this paper and added to IC algorithm to reduce the force error caused by environmental changes and thus improve the vibration isolation effect. In this paper, the characteristic equation of adaptive impedance control（AIC） algorithm was first obtained and the condition for system stability was calculated based on Routh-Hurwitz stability criterion. Finally, the stability of this system was verified. The simulation experiment combining MATLAB and Adams was designed and performed with EPSWLR. The effectiveness of AIC was proved by monitoring the changes in the force of single leg and the attitude of robot body.

关键词： Electric parallel six wheel-legged robot Impedance control Adaptive algorithm Stewart structure

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Adaptive control for Fractional-order Interconnected systems

Adaptive Control for Fractional-order Interconnected Systems

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第三十八届中国控制会议

作者： Bingyun Liang Shiqi Zheng Zichao Yang Feng Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems

This paper focuses on a class of fractional-order interconnected system. A decentralized adaptive controller is designed based on the fractional Lyapunov direct method. In order to solve the interactions between subsystems, a compensation term is presented. In addition, the various uncertainties, such as unknown parameters, disturbances and nonlinear functions, are considered and estimated by the proposed fractional-order update laws. Especially, neural networks are adopted to approximate the unknown nonlinear functions. Furthermore, a fractional-order auxiliary system is constructed to deal with the input nonlinearities with backlash and saturation. Finally, the effectiveness of the proposed control scheme is verified by some simulation examples.

关键词： Adaptive control fractional-order system interconnected system input nonlinearity neural networks

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