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Chinese control Conference (CCC)

作者： Haifeng Li Zunhe Hu Xinwei Chen College of Computer Science and Technology Civil Aviation University of China Tianjin Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou Department of Computer Science Minjiang University Fuzhou

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this paper. To improve the quality of the noisy optical flows, a correction method across multiple frames is developed. Furthermore, the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation estimation. Finally, a RANSAC approach for robot ego-motion estimation is proposed. Physical experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： Adaptive optics Image motion analysis Computer vision Optical imaging Optical sensors Cameras Optical noise

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Exploiting structural similarity of log files in fault diagnosis for Web service composition

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CAAI Transactions on Intelligence Technology 2016年第1期1卷 61-71页

作者： Xu Han Binyang Li Kam-Fai Wong Zhongzhi Shi Beijing Key Laboratory of Electronic System Reliability Technology Capital Normal University Beijing China University of International Relations Beijing China Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

With increasing deployment of Web services, the research on the dependability and availability of Web service composition becomes more and more active. Since unexpected faults of Web service composition may occur in different levels at runtime, log analysis as a typical data- driven approach for fault diagnosis is more applicable and scalable in various architectures. Considering the trend that more and more service logs are represented using XML or JSON format which has good flexibility and interoperability, fault classification problem of semi-structured logs is considered as a challenging issue in this area. However, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the accuracy of fault classification, we exploit structural similarity of log files and propose a similarity based Bayesian learning approach for semi-structured logs in this paper. Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network （CBN）, uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN. Experimental results show that our approach outperforms other learning approaches on structural log datasets.

关键词： Web services composition Fault diagnosis Combined Bayesian network （CBN） Similarity Probability

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Image super-resolution using non-local Gaussian process regression

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Neurocomputing 2016年 194卷 95-106页

作者： Haijun Wang Xinbo Gao Kaibing Zhang Jie Li State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi׳an 710071 China School of Computer and Information Science Hubei Engineering University Xiaogan 432000 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi׳an 710071 China

Gaussian process regression (GPR) is an elegant statistical learning method for nonlinear mappings. Despite its effectiveness, GPR suffers from intensive computational complexity. To improve the efficiency, most existing GPR-based super-resolution (SR) methods are local patch-based, which cannot make full use of self-similarities existing in natural images. To address this problem, we propose a non-local self-learning SR framework based on GPR, which learns only one non-local GPR model instead of multiple local patch-based models for SR reconstruction. In the proposed framework, an anisotropic linear kernel is introduced to construct a new kernel function for capturing more structure similarity. Furthermore, we disclose that a simple grid patch sampling with moderate sampling interval can be used to speed up the SR processing significantly without compromise of reconstruction quality. In addition, we make a theoretical discussion on two important factors of the predictive variance and the condition number of the kernel matrix that relate to the performance of the GPR. Experimental results on the benchmark test images show that our proposed method is superior to other state-of-art competitors in terms of both quantitative and qualitative measurements.

关键词： Gaussian process regression Grid sampling Image super-resolution Kernel Non-local self-learning

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A Target Tracking Method Based On Tangential and Normal Acceleration Variable-Structure Multiple Model

A Target Tracking Method Based On Tangential and Normal Acce...

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第三十四届中国控制会议

作者： Huang Dayu Qiu Jier Wu Shaofeng Cai Yunze Science and Technology on Avionics Integration Laboratory Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467374439

Considered the maneuvering target motion characteristics, a motion model according to the decomposition of the acceleration is investigated. Compared with traditional model set, this model complies with the need of variable-structure multiple model(VSMM) to model set. In this paper, we present a tangential and normal acceleration VSMM target tracking algorithm with the advantage of active digraph switching algorithm. Simulation results show that the performance of the algorithm is better than that of traditional IMM algorithm.

关键词： Variable-Structure Multiple Model,Target Tracking,Tangential and Normal Acceleration

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Time-Varying Linear Programming via LVI-PDNN with Numerical Examples

Time-Varying Linear Programming via LVI-PDNN with Numerical ...

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2016 IEEE 13th International Conference on Signal processing（ICSP2016）

作者： Sheng Wu Liu He Maotai Zou Jian Li Yunong Zhang School of Information Science and Technology Sun Yat-sen University SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous System and Networked Control Ministry of Education

Playing a fundamental role in mathematic optimization, linear programming(LP) problems have been widely encountered in various scientific disciplines and industrial applications. Although static LP problems have been investigated extensively and applied to abundant scientific fields through the last decades, researches concerning time-varying linear programming(TVLP) problem solving are in relatively small *** this paper, the TVLP problems are solved by a linearvariational-inequality based primal-dual neural network(LVIPDNN), which is originally designed for static LP problem solving. Numerical examples and computer simulations further reveal that LVI-PDNN could approach the theoretical solution when solving TVLP problems subject to equality, inequality and bound constraints simultaneously.

关键词： Time-varying linear programming primal-dual neural network linear variational inequality

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Automatic building reconstruction from high resolution InSAR data using stochastic geometrical model

Automatic building reconstruction from high resolution InSAR...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kun Fu Yue Zhang Xian Sun Wenhui Diao Bin Wu Hongqi Wang Institute of Electronic Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, a fully automatic building reconstruction method for high resolution interferometric synthetic aperture radar (InSAR) data is presented. This method is based on stochastic geometrical model. Firstly, a building detection procedure is implemented on the big image and the entire scene is divided into building clips. After that, the reconstruction process is utilized for each building clip. In the reconstruction process, a building in 3D space is projected to the image plane and then decomposed to feature regions including layover, corner line, roof and shadow. We explore the statistic properties of the each region, and include it in the posterior function, together with the edge term and the prior we defined. Finally, in order to overcome local optima, a group of special transmission kernels are designed. The experimental results on TanDEM-X data demonstrate the effectiveness of our method.

关键词： Buildings Image reconstruction Kernel Synthetic aperture radar Feature extraction Optimization Image resolution

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Towards cyber-physical systems:Distributed model predictive control

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Science Foundation in China 2015年第3期23卷 42-61页

作者： LI ShaoYuan ZHENG Yi WEI YongSong Key Laboratory of System Control and Information Processing Department of AutomationShanghai Jiao Tong University

In this paper,we provide a review of recent results in the design of distributed model predictive control(DMPC).DMPC not only inherits the advantage of model predictive control but also has characteristics of distributed control *** review the work on DMPC from two aspects:unconstrained DMPC and the design methods of stabilized DMPC with ***,some proposed algorithms are illustrated through two industrial processes.

关键词： Cyber-physical system Model predictive control Dis

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Nonlinear Fermentation Process Fault Monitoring Based on JITL Strategy

Nonlinear Fermentation Process Fault Monitoring Based on JIT...

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第28届中国控制与决策会议

作者： Yakun Zhang Xuejin Gao YaFen Li XiChang Wang Pu Wang College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

PCA,as the representative of the traditional global fault monitoring algorithm,is a linear feature extraction *** global algorithm may cause a lot of false alarms in fault monitoring of fermentation process with nonlinear and multi-stage *** this paper,the Just In Time Learning(JITL) strategy is introduced into the Kernel Principal Component Analysis(KPCA) *** local model is established with the data similar to the current online *** the local model can represent the current state of the system,it is not necessary to identify the stages before *** the same time,the local KPCA model can be used to feature extract nonlinear *** data generated by the PenSimv2.0 simulation platform is used for verifying the *** results show that this method has a better effect than KPCA algorithms.

关键词： JITL KPCA Fermentation Fault Monitoring

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A target tracking method based on tangential and normal acceleration variable-structure multiple model 34

A target tracking method based on tangential and normal acce...

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34th Chinese control Conference, CCC 2015

作者： Dayu, Huang Jier, Qiu Shaofeng, Wu Yunze, Cai Science and Technology on Avionics Integration Laboratory Shanghai China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9789881563897

Considered the maneuvering target motion characteristics, a motion model according to the decomposition of the acceleration is investigated. Compared with traditional model set, this model complies with the need of variable-structure multiple model (VSMM) to model set. In this paper, we present a tangential and normal acceleration VSMM target tracking algorithm with the advantage of active digraph switching algorithm. Simulation results show that the performance of the algorithm is better than that of traditional IMM algorithm. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Tangential and Normal Acceleration Target Tracking Variable-Structure Multiple Model

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Fault Detection of Batch Process Based on MSICA-OCSVM

Fault Detection of Batch Process Based on MSICA-OCSVM

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第28届中国控制与决策会议

作者： Xuejin Gao Rong Ma College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

When we use the traditional multi-scale independent component analysis method to extract independent component ICA on each scale,and then,using the ICA decomposition on the reconstructed data to construct monitoring statistics,however,data of the reconstruction on the nature was already independent component,it is meaningless to extract them by *** on the shortcoming,this paper proposes a MSICA-OCSVM method that was combined with Multi-scale Independent Component Analysis(MSICA) and One-class Support Vector Machine(OCSVM) to monitor the ***,we can use the wavelet transform decomposition to monitor data at different *** then,the data was processing by threshold denoising,and was monitored on each scale extraction by using ICA independent principal ***,we can use the wavelet transform coefficients for each scale would scale back on the reconstruction of the new signal matrix(?) Finally,new OCSVM model was constructed by the reconstructed matrix *** can make the use of determined hyper-plane to construct a nonlinear statistic,and the appropriate control limits was determined by using kernel density *** is more,this method is applied to penicillin fermentation process simulation platform,the experimental results show that this method can effectively utilize the structure information data compared to traditional MSICA fault monitoring method,the failure rate of false positives,false negative rate was significantly reduced.

关键词： batch process one-class support vector machines(OCSVM) fault detection Multi-scale independent component analysis(MSICA)

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