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Applying system Thinking to Learn from Accident of Modern Automatic control systems

Chinese Journal of Electronics 2014年第2期23卷 409-414页

作者： NIU Ru CAO Yuan GE Xiaocheng TANG Tao State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Department of Computer Science University of York

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events,rather than a simple cause-effect chain. We take railway Train control systems(TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP – one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Socio-technical automatic system Railway safety Accident analysis systems theoretic accident model and processes(STAMP)

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Wei Li Junfei Qiao Xiao-Jun Zeng College of Electronic Information and Control Engineering Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China School of Computer Science University of Manchester Manchester U.K

ISBN: (纸本)9781467376839

This paper provides a complete solution for the problem how to accurately compute the similarity between fuzzy sets with Gaussian membership functions, which is a fundamental issue for the identification and simplification of FNNs. It is shown that there are three different types of similarities between a pair of Gaussian membership functions dependent on the relative positioning between the given pair of membership functions, and the accurate and detailed computing formulas are given in each type. A simulation example is given to compare the proposed accurate similarity analysis method with the existing approximation approaches and to show how much more accuracy can be obtained than the approximation one in terms of absolute percentage approximation error.

关键词： Fuzzy sets Fuzzy neural networks Shape Computational modeling Bismuth Knowledge discovery

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Extended kalman filter-based sensor fault diagnosis in heat exchangers

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ICIC Express Letters, Part B: Applications 2015年第5期6卷 1397-1404页

作者： Zhang, Jianhua Chen, Yannan Zhang, Luyao Xiong, Jing Hou, Guolian State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources No. 2 Beinong Road Huilongguan Beijing China School of Control and Computer Engineering North China Electric Power University No. 2 Beinong Road Huilongguan Beijing China

The dynamics of heat exchanger is complex in terms of nonlinearity, timevarying, uncertainty, so extended Kalman filter (EKF) is utilized to diagnose the instrument faults existing in heat exchangers. EKF is employed to generate residuals for detecting sensor faults, and the residuals are then evaluated to isolate the location of faults using decision functions. Simulation results testify EKF based fault diagnosis method is suitable to identify sensor faults in heat exchangers. © 2015.

关键词： Failure analysis

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Corrigendum to “Mobile Target Tracking Based on Hybrid Open-Loop Monocular Vision Motion control Strategy”

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Discrete Dynamics in Nature and Society 2016年第1期2016卷

作者： Cao Yuan Ma Lianchuan Weigang Ma National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing 100044 *** School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 *** State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 *** Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 ***

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Research on Image Matching Technology for the Spherical Stereo Vision

Research on Image Matching Technology for the Spherical Ster...

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IEEE International Conference on Mechatronics and Automation

作者： Baofeng Zhang Na Liu Yingkui Jiao Yongchen Li Junchao Zhu Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education of China School of Computer and Communication Engineering Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin 300384 China

ISBN: (纸本)9781479970995

The technique of stereo matching is an important research goal of spherical stereo vision system, which is widely used in visual navigation, motion analysis and panoramic surveillance areas. Fisheye lens can get view images of large field, this paper uses two fisheye lens whose optical axis are perpendicular to construct spherical stereo vision system and does research in stereo matching of spherical stereo vision system. As the image that fish eye lens collected has lager distortion, this paper does stereo matching with the images the system collected without distortion correction using matching algorithm based on maximally stable extremal regions (MSER) and affine scale invariant feature transform (ASIFT) to reduce the correction error generated in the process. The experiment results show that the algorithm can be applied in stereo matching of fish eye image without distortion, and the results meets the need of 3D reconstruction.

关键词： Matching Spherical stereo vision Maximally Stable Extremal Regions (MSER) Affine Scale Invariant Feature Transform (ASIFT)

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The Application of Artificial Immune controllers in Intelligent Vehicle controlling

The Application of Artificial Immune Controllers in Intellig...

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2015 International Conference on control, Automation and Artificial Intelligence(CAAI 2015)

作者： Yan-ting LAN Jin-ying HUANG Xiao-dong CHEN North University of China Shool of Computer and Control Engineering North University of China School of Mechanical and Power Engineering Key Laboratory of Farming System Ministry of Agriculture of the People’s Republic of ChinaCollege of Agronomy and BiotechnologyChina Agricultural University

Relying on the two different regulatory effects of T cells of promoting or suppressing in varied immune phases,it ensures that the biological immune system responds rapidly and keeps its sound stability when different types of antigens invade,and thereby remains the balance within the biology *** for reference of the collaborative immune mechanism of the T cells and B cells in the biological specific immunity process,this paper presents an artificial immune approach and have it applied into the intelligent vehicle *** the model of the intelligent vehicle steering system is established,and then biological immune mechanism is interpreted with feedback control theory to achieve the intelligent control by artificial immune *** the end system simulation is conducted through MATLAB software and comparisons are made as well to the control effect of normal PID *** show that the artificial controllers work much better than the normal PID controllers in both response rate and dynamic *** approach provides a new feasible way for intelligent vehicle controlling.

关键词： Intelligent vehicle Steering system model Immune mechanism Artificial immune controller

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A statistical method for no-reference image quality assessment

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Journal of Computational Information systems 2014年第16期10卷 7203-7212页

作者： Lu, Fangfang Zhao, Qunfei Liu, Huanxi College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

This paper proposes a statistical method for no-reference image quality assessment using steerable pyramid decomposition without any prior knowledge about the distortions of the original image. Because the means of (log2 of) subband coefficient amplitudes (MLSCAs) of the natural images have certain statistical properties independent of their contents, a predicted MLSCAs based on these properties can be considered as a reference index for describing the distorted images. A subband distortion is then defined as the difference between the predicted and real MLSCA of the distorted image. A set of weights for the subbands is used to combine the subband distortions into a quality score. Experimental results show that the proposed method outperforms six no-reference algorithms using natural scene statistics. 1553-9105/Copyright © 2014 Binary Information Press

关键词： Statistics

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A new automatic mass detection method for breast cancer with false positive reduction

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Neurocomputing 2015年 152卷 388-402页

作者： Xiaoming Liu Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 China

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristi

关键词： Mass detection Breast cancer Level set Local binary pattern Support vector machine

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A new approach of conditions on δ2s(Φ) for s-sparse recovery

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Science China(Information Sciences) 2014年第4期57卷 107-113页

作者： CEN YiGang ZHAO RuiZhen MIAO ZhenJiang CEN LiHui CUI LiHong School of Computer & Information Technology Beijing Jiaotong University School of Information Science and Engineering Central South University Key Laboratory of System Control and Information Processing Ministry of Education Department of Mathematics Beijing University of Chemical Technology

In this paper,we provide a unified expression to obtain the conditions on the restricted isometry constantδ2s(Φ).These conditions cover the important results proposed by Candes et *** each of them is a sufficient condition for sparse signal *** the noiseless case,whenδ2s(Φ)satisfies any one of these conditions,the s-sparse signal can be exactly recovered via(l1)constrained minimization.

关键词： concentration of mutilated vector in kerΦ s-largest mutilated vector restricted isometry constant δ2s(Φ) exact recovery of s-sparse signals via(l1) maximum value of single variable function

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RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

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Natural Hazards 2015年第2期77卷 1055-1068页

作者： Olatomiwa, Lanre Mekhilef, Saad Shamshirband, Shahaboddin Petkovic, Dalibor Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Electrical and Electronic Engineering Federal University of Technology Minna Nigeria Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Nis Serbia

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

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