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Chinese control Conference (CCC)

作者： Meijia Shang Xiaoping Wang Mian Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology and also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The traditional Pavlov associative memory circuit realizes the law of learning and forgetting in classical conditioned reflex. In addition, the law of generalization and differentiation also belongs to classical conditioned reflex, so the process of associative memory can be more effectively simulated by adding generalization and differentiation theory on the basis of traditional associative memory. In this paper, a memristor-based circuit is designed to implement generalization and differentiation based on Pavlov associative memory. The circuit can be applied to simple classification recognition. Based on the features of objects as input, the output of the circuit is used as the classification result to achieve the function of classification and recognition. Finally, the accuracy of classification recognition on the generalization and differentiation circuit proposed in this paper can be verified by the simulation results in PSPICE.

关键词： Neurons Threshold voltage Associative memory Dogs Memristors Logic gates Circuit synthesis

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Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

arXiv

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arXiv 2020年

作者： Cui, Yuqi Wang, Huidong 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 School of Management Science and Engineering Shandong University of Finance and Economics Jinan250014 China

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Fuzzy clustering

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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A bi-level optimization model of integrated energy system considering waste treatment under uncertainty

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Energy 2025年

作者： Jie Yang Jichen Wang Kai Ma Qian Zheng Wenkai Ji Shiliang Guo School of Electrical Engineering Yanshan University No. 438 West Hebei Avenue Qinhuangdao 066004 Hebei China Hebei Key Laboratory of Power Electronics for Energy Conservation and Drive Control Yanshan University No. 438 West Hebei Avenue Qinhuangdao 066004 Hebei China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University No. 438 West Hebei Avenue Qinhuangdao 066004 Hebei China

The phenomenon of garbage pollution is becoming increasingly serious worldwide, leading to a growing interest in incorporating waste treatment into integrated energy system. The hydrothermal carbonization technology can convert biomass into Hydrochar with high calorific value under subcritical conditions. To integrate hydrothermal carbonization into integrated energy system, firstly, we establish a hydrothermal carbonization model for wet waste during normal or valley electricity pricing times. During peak electricity pricing times, we establish a waste incineration treatment model by adjusting the combustion ratio of dry waste and Hydrochar. Then, considering the uncertainties of wind, solar, and load, we propose a new bi-level optimization configuration model in this paper. Therefore, we adopt the beluga whale optimization algorithm and a two-stage robust optimization method to minimize both the annual total cost and daily operating cost. The case study shows that compared with the anaerobic fermentation of waste, the degree of load fluctuation in the proposed model is reduced to 66%, 78%, and 76% in three seasons, respectively. The findings from this simulation indicate that the proposed model has greater advantages in resource utilization, peak shaving, valley filling, and robustness compared with the anaerobic fermentation of waste.

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Distributed Observer Design for Achieving Asymptotical Omniscience over Time-variant Disconnected Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3565-3570页

作者： Haotian Xu Jingcheng Wang Hongyuan Wang Shangwei Zhao Hai Lin Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

This paper investigates the distributed observer design for linear system under time-variant disconnected communication network. By constructing basic eigenvectors of 0-eigensubspace of disconnected Laplacian Matrix and using LMIs method, we prove the distributed observer cannot achieve omniscience asymptotically under switching topology without constraining the system matrix or alternative topologies set. To deal with this problem, this paper investigates three kinds of constraints and the system matrix only needs to satisfy any one of them. Then a group of sufficient conditions corresponding to the asymptotic omniscience of distributed observer under switching topology are proved by Lyapunov analysis. Finally, a numerical simulation shows the validity of our method.

关键词： Distributed observer Continuous time system estimation Consensus Sensor networks Switching topology

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An Optimal Tracking control Method with Q-learning for Discrete-time Linear Switched System

An Optimal Tracking Control Method with Q-learning for Discr...

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第三十九届中国控制会议

作者： Shangwei Zhao Jingcheng Wang Hongyuan Wang Haotian Xu Automation Department Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

In this paper, a novel approach combining Q-learning method and two-stage optimization is proposed to solve linear quadratic tracking(LQT) problem for unknown discrete-time switched system. An augmented system consisting of switched subsystem and reference trajectory is built and Q-learning method is introduced to identify each subsystem without requiring any knowledge of augmented system state dynamics. Then, based on the optimal control laws for each subsystem obtained by Q-learning in advance, the optimal hybrid control law including switching mode and control input is obtained by the two-stage optimization framework. Furthermore, the optimality of the algorithm is proved. Finally, a simulation case is used to testify the effectiveness of the proposed algorithm.

关键词： Tracking control Linear switched system Q-learning

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Fault Detection of Lithium-Ion Batteries Subject to Probabilistic Parametric Uncertainties

Fault Detection of Lithium-Ion Batteries Subject to Probabil...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Yuhao Liu Yiming Wan Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

ISBN: (数字)9781728106816

ISBN: (纸本)9781728106823

The fault detection task in lithium-ion battery management system (BMS) is critical to the safety and reliability of rechargeable and hybrid electric vehicles. To explicitly account for inevitable errors of battery model parameters, a robust fault detection method is proposed in this paper. The residual generator is established by exploiting an equivalent circuit model. The effect of uncertainties on the residual is parametrized as polynomial dependence on probabilistic uncertain parameters and noises. Then Gaussian mixtures are adopted to derive the residual distribution. To account for residual uncertainties in the detection decision, the weighted sum of distances from the generated residual to each Gaussian component of the residual distribution is proposed. Simulation results illustrate that our proposed method outperforms the conventional approach that does not consider parametric uncertainties.

关键词： Uncertainty Batteries Integrated circuit modeling Uncertain systems Voltage measurement Electronic countermeasures State of charge

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Inverse linear-quadratic discrete-time finite-horizon optimal control for indistinguishable homogeneous agents: a convex optimization approach

arXiv

引用

arXiv 2021年

作者： Zhang, Han Ringh, Axel Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Gothenburg41296 Sweden

The inverse linear-quadratic optimal control problem is a system identification problem whose aim is to recover the quadratic cost function and hence the closed-loop system matrices based on observations of optimal trajectories. In this paper, the discrete-time, finite-horizon case is considered, where the agents are also assumed to be homogeneous and indistinguishable. The latter means that the agents all have the same dynamics and objective functions and the observations are in terms of "snap shots" of all agents at different time instants, but what is not known is "which agent moved where" for consecutive observations. This absence of linked optimal trajectories makes the problem challenging. We first show that this problem is globally identifiable. Then, for the case of noiseless observations, we show that the true cost matrix, and hence the closed-loop system matrices, can be recovered as the unique global optimal solution to a convex optimization problem. Next, for the case of noisy observations, we formulate an estimator as the unique global optimal solution to a modified convex optimization problem. Moreover, the statistical consistency of this estimator is shown. Finally, the performance of the proposed method is demonstrated by a number of numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

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Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain Stimulation Electrode Implantation

Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Weihang Huang Shenghao Dai Anze Xu Xianyi He Shaoping Huang Anzhu Gao School of Mechanical Engineering Shanghai Jiao Tong University Shanghai Jiao Tong University Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665405362

Deep brain stimulation (DBS) has been widely used in clinical practice as an effective therapy for neurological disorders. MR-conditional robot-assisted electrode implantation has attracted more attention for DBS because of its great accuracy and high-quality guiding images by using MRI. This paper develops a pneumatically actuated MR-safe parallel robot for deep brain electrode implantation. Its fully plastic components and MR-safe pneumatic actuators enable the robotic system to be MR-safe, posing no known hazards in MR environment. A 6-DOF parallel robot is developed using six slider-crank mechanisms for prismatic motions. Inverse kinematics is built to investigate its workspace and achieve the precise control. Experiments are carried out to validate the feasibility, and results shows that the average error of the motion is 0.11 mm. The developed parallel robot shows the potential advantages in the automatic and precise robot-assisted neurosurgical surgery in future.

关键词： Electrodes Pneumatic actuators Neurological diseases Parallel robots Magnetic resonance imaging Satellite broadcasting Deep brain stimulation

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