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arXiv 2018年

作者： Li, Wenqing Zhao, Chunhui Huang, Biao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G-2V4 Canada

For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the closed-loop control mechanism and only explore static characteristics, which thus are incapable of distinguishing between real process faults and nominal changes of operating conditions, leading to unnecessary alarms. In this regard, this paper proposes a distributed monitoring method for closed-loop industrial processes by concurrently exploring static and dynamic characteristics. First, the large-scale closed-loop process is decomposed into several subsystems by developing a sparse slow feature analysis (SSFA) algorithm which capture changes of both static and dynamic information. Second, distributed models are developed to separately capture static and dynamic characteristics from the local and global aspects. Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other. Case studies are conducted based on both benchmark data and real industrial process data to illustrate the effectiveness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Process monitoring

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An Improved-WM Method Based on Optimization of Centers of Output Fuzzy Subsets for Fuzzy Rules

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IFAC-PapersOnLine 2017年第1期50卷 7369-7374页

作者： Jin Y. Cao W. Wu M. She J. Yuan Y. School of Automation China University of Geosciences Wuhan 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

The generation of fuzzy rules from samples is significant for fuzzy modelling. To improve the robustness of Wang-Mendel (WM) method, an improved WM method to extract fuzzy rules from all the regularized sample data was proposed. However, the accuracy of the model with this method is degraded for the conflicting rules with small difference between support degrees. And the output subsets can only be chosen from the pre-defined ones. To solve these problems, we develop an improved-WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (COiWM). This method adopts the fuzzy c-means (FCM) clustering algorithm to divide the input and output spaces, and the improved WM method which replaces the original data by regularized data is used to calculate the support degrees. Then the support degrees are used as weights to optimize the centers of output fuzzy subsets with a method of weighted averages, so as to enhance the accuracy of a fuzzy model. Experimental results of a case study on short term daily maximum electric load forecasting prove that our proposed method enhances the accuracy of a fuzzy model. © 2017

关键词： Accuracy Conflicting rules Fuzzy c-means clustering algorithm Fuzzy rules Wang-Mendel method

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A four-stage localization method for epileptic seizure onset zones

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IFAC-PapersOnLine 2017年第1期50卷 4412-4417页

作者： Wan T. Wu M. Lai X. Wan X. She J. Du Y. School of Automation China University of Geosciences Wuhan Hubei 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan School of Automation Guangdong University of Technology Guangzhou Guangdong 510006 China

This paper presents a four-stage localization method for epileptic seizure onset zones (SOZs). This method combines the advantages of both the Shannon-entropy-based complex Morlet wavelet transform (SE-CMWT) and adaptive-genetic-algorithm-based matching pursuit (AGA-MP) methods to achieve satisfactory localization performance. The Hilbert transform method is used to identify events of interest. The SE-CMWT-based power spectral density method is applied to acquire channels of interest (CoIs). By using the Morlet wavelets as the dictionary elements, the AGA-MP method combined with Morlet wavelets is developed to detect high-frequency oscillations (HFOs) on CoIs with faster speed and higher accuracy than the AGA-MP method. And the characteristics of HFOs are used to localize SOZs based on the half-maximum method. A comparison shows that our method has higher sensitivity and specificity than four existing methods. © 2017

关键词： Epilepsy Four-stage localization method High-frequency oscillation (HFO) Seizure onset zone (SOZ)

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A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder

A Novel Deep Learning Approach: Stacked Evolutionary Auto-en...

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International Joint Conference on Neural Networks

作者： Yaoming Cai Zhihua Cai Meng Zeng Xiaobo Liu Jia Wu Guangjun Wang School of Computer Science China University of Geosciences Wuhan China Qinzhou University Guangxi China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Computing Macquarie University Sydney NSW Australia

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective Optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification problems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.

关键词： Training Optimization Decoding Neural networks Linear programming Feature extraction Stacking

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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

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IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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Intersection Scan Model and Probability Inference for Vision Based Small-scale Urban Intersection Detection 28

Intersection Scan Model and Probability Inference for Vision...

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IEEE intelligent Vehicles Symposium

作者： Yang Yi Li Hao Zhu Hao Shang Songtian Lyu Ningyi Song Wenjie School of Automation and State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

ISBN: (纸本)9781509048052

Large-scale intersections stamped on maps have diverse visual features for detection, while small-scale urban intersections are hard to be identified especially when GPS signals are missing. In this paper, we propose a Hidden Markov Model (HMM) based small-scale intersection detection method utilizing monocular vision. We extract visual cues of road transformations and dynamic vehicles' tracks, and then design an Intersection Scan Model to obtain the potential traversable direction of the current road, which is the primary criterion of the intersection estimation. For better performances, we take the detections of consecutive frames into consideration and finally integrate them into HMM to estimate the probabilities of intersections. Results from KITTI datasets and real-world experiments have shown the functionality of the presented approach.

关键词： Hidden Markov models Roads Global Positioning system Vehicle dynamics Semantics Image segmentation Feature extraction

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Impedance control with New Inner Torque loop for Compliant Joints and Its Performance Analysis 36

Impedance Control with New Inner Torque loop for Compliant J...

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第36届中国控制会议

作者： Yin Ke Jian Li Zonglin Huang Shuai Li Guodong Li The Bionic Robot and System Key Laboratory School of Mechatronic EngineeringBeijing Institute of Technology Key Laboratory for Intelligent Control and Decision on Complex Systems School of AutomationBeijing Institute of Technology Beijing Jingwei Hirain Technologies

ISBN: (纸本)9781538629185

For the purpose of enhancing compliance of robot joint,an impedance control strategy with new inner torque controller is *** inertia of the motor can be scaled down thanks to this new type of torque *** of different position（motor-side position 9 and link-side position q） feedback strategies are explored through theoretical deviation,simulation and static stiffness *** expressions of static stiffness under both position feedback strategies are obtained with validity verified by Simulink *** with 9 feedback strategy,static stiffness can exceed spring stiffness under q feedback strategy with high desired stiffness,which means wider variation range of static stiffness and this benefits robot applications that require wider variation range of impedance,such as rehabilitation robots and collaborative ***,several experiments with the compliant joint prototype verify the developed controllers and show the efficiency of the proposed control approach in terms of compliant behavior.

关键词： Compliant joint Impedance control Static stiffness

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Privacy Preservation for Distributed Nonsmooth Constrained Optimization Based on Pseudo-Subgradient

Privacy Preservation for Distributed Nonsmooth Constrained O...

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第37届中国控制会议

作者： Xianlin Zeng Shu Liang Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Advanced Innovation Center for Intelligent Robots and Systems (Beijing Institute of Technology) Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education

In this paper, we investigate a privacy preservation design in the distributed nonsmooth convex optimization with set constraints. To solve the distributed optimization problem while preserving the privacy, we use pseudo-subgradients involved with（non-integrable） set-valued functions. Based on pseudo-subgradients, we propose distributed nonsmooth optimization algorithms with keeping subgradient information confidential. Then we prove the correctness and convergence of the distributed privacy preservation optimization algorithms to achieve the exact solution of the original optimization problem.

关键词： Distributed nonsmooth convex optimization privacy preservation set constraints pseudo-subgradients

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Damage evaluation model and simulation study on functional systems of ultra low altitude aircraft 32

Damage evaluation model and simulation study on functional s...

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32nd Youth Academic Annual Conference of Chinese Association of automation, YAC 2017

作者： Xiaochen, Wu Chen, Chen School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing100081 China

ISBN: (纸本)9781538629017

The assessment of aircraft damage is a key technology in modern flight missions. For a kind of ultra low altitude aircraft, simulation study of the damage caused by the prefabricated fragments is carried out, which is based on the functional systems contained in the different components of the aircraft. In addition, the corresponding aircraft damage assessment simulation system is developed, which can complete the real-time damage calculation and quantify and classify the damage degree of the functional system. A new modeling idea and important research platform is provided for vulnerability assessment and damage assessment of specific targets. © 2017 IEEE.

关键词： Aircraft

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A Novel Forwarding Method for Full-Duplex Two-Way Relay Networks

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China Communications 2017年第4期14卷 65-72页

作者： Shuangshuang Han Xiang Cheng Liuqing Yang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China and Qingdao Academy of Intelligent Industries The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electrical and Computer Engineering Colorado State University

An estimate-and-forward(EF) scheme for single-input single-output(SISO) and multiple-input multiple-output(MIMO) full-duplex two-way relay networks is proposed and analyzed. The relay estimates the received signal from two terminal nodes by a minimum mean squared error(MMSE) estimation and forwards a scaled version of the MMSE estimate to the destination. The proposed EF outperforms conventional amplify-and-forward(AF) and decode-and-forward(DF) across all signal-to-noise ratio(SNR) region. Because its computational complexity is high for relays with a large number of antennas(large MIMO) and/or high order constellations, an approximate EF scheme, called list EF, are thus proposed to reduce the computational complexity. The proposed list EF computes a candidate list for the MMSE estimate by using a sphere decoder, and it approaches the performance of the exact EF relay at a negligible performance loss. The proposed forwarding approach also could be used to other relay networks, such as half-duplex, one-way or massive MIMO relay networks.

关键词： estimate-and-forward full-duplex two-way relay wireless communications

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