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International Conference on intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training Image edge detection Vehicle detection Meteorology Radio frequency

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Real-time moving object detection and tracking based on improved mean shift

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 823-828页

作者： Lei, Ying-Hui Zhang, Juan Yang, Wen-Jia Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education School of Automation Beijing Institute of Technology Beijing 100081 China

An improved real-time target detection and tracking method was proposed based on moving foreground object in the servo monitoring system. This method extracts moving object based on adaptive mixture of Gaussian when the object comes into the video scene, then tracks the moving object using improved MeanShift algorithm, and makes it in the center of the scene. The algorithm not only ensures the real-timing of the detection and tracking, but also enlarges the sight of the camera when the object is tracked. The experiment results show that this method can automatically detect moving object and do servo tracking.

关键词： Bandpass filters

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Moving object detection in framework of compressive sampling

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Journal of systems Engineering and Electronics 2010年第5期21卷 740-745页

作者： Jing Li JunzhengWang Wei Shen Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing 100081 P. R. China

Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algorithm for moving object and region detection in video using a compressive sampling is *** algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background *** algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated *** leads to a computationally efficient method and a system compared with the existing motion estimation *** experimental results show that the sampling rate can reduce to 25% without sacrificing performance.

关键词： compressive sampling compressive measurements moving object detection.

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An algorithm based on ODLLTSA and SVM classier for door plate number recognition

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 789-793页

作者： Ma, Li-Ling Ji, Li-Jun Wang, Jun-Zheng School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education Beijing Institute of Technology Beijing 100081 China

A new method is proposed, through combining the algorithm of orthogonal discriminant linear local tangent space alignment (ODLLTSA) and the support vector machine (SVM), to improve the accuracy of recognizing door plate numbers. The feature of door plate characters is first extracted by the ODLLTSA and then this extracted feature is used to train the SVM classifier. Finally, the new plate characters are classified by the trained SVM. Using the algorithm, a high recognition rate can be achieved. Experimental results show that this method is effective and robust in the real applications.

关键词： Support vector machines

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T-S fuzzy observer for nonlinear magnetic levitation system with uncertain variable friction coefficient

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 114-118页

作者： Zhou, Li-Jie Guo, Shu-Li Gao, Zhe School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex system Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

An adaptive fuzzy observer for nonlinear magnetic levitation system with uncertain friction coefficient is proposed. First, a T-S fuzzy model of the nonlinear magnetic levitation system is proposed. An observer is given by LMI method and the Lyapunov method. Through adding an auxiliary variable, it relaxes the constraint of the design of observer. The proposed approach is a more relaxed condition than others. Simulation results show the effectiveness of this approach. The results show that the position of magnetic levitation system is estimated effectively with unknown friction coefficient. There is some reference value for a kind of nonlinear systems with unknown parameter.

关键词： Lyapunov methods

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Optical encoder fault diagnosis based on characteristics of data and fuzzy logic

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 876-882页

作者： Deng, Fang Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

The fault diagnosis for a class of widely used digital parallel output optical encoder were focused. After definition of the optical encoder, the main features of the optical encoder's output data were analyzed. A fault diagnosis method which did not rely on the system model where optical encoder used was proposed. The changes of optical encoder's output data were analyzed. Then, the inherent characteristics were calculated. The fuzzy logic was utilized to determine the fault type and locate the fault location. Theoretical analysis and experimental results show that this method can diagnose and isolate optical encoder fault accurately without disassembly.

关键词： Fuzzy logic

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Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

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电子学报(英文版) 2010年第3期19卷 451-456页

作者： LI Bo LIU Wenju DOU Lihua School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.

关键词：高斯混合模型图像分割无监督学习应用概率密度函数椭球等高分布模型估计高斯密度

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An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization

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Science china(Information Sciences) 2010年第5期53卷 980-989页

作者： XIN Bin 1,2 ? , CHEN Jie 1,2* , PENG ZhiHong 1,2 & PAN Feng 1,2 1 School of automation, Beijing Institute of Technology, Beijing 100081, china 2 key laboratory of complex system intelligent control and Decision, Ministry of Education, Beijing 100081, china 1. School of Automation Beijing Institute of Technology Beijing 100081 China2. Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.

关键词： global optimization statistical learning differential evolution particle swarm optimization hybridization adaptation rotated function

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Identification of interval models for a class of uncertain systems via linear programming

Identification of interval models for a class of uncertain s...

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作者： Zhang, Guozhu Chen, Jie Li, Zhiping Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology School of Automation Beijing 100081 China

ISBN: (纸本)9781424474264

The interval models of uncertain plants are frequently used in the field of robust control. In this paper, a novel interval model identification method based on linear programming is proposed. By certain prepossessing on the measured data of the plant, the interval model identification problem is transformed into several linear programming problems, which can be easily solved through various algorithms. We have proved that the resultant interval model has a known error bound, provided that certain conditions on the measured data are satisfied. To demonstrate its effectiveness, the proposed method is applied to identify the interval model of a servo system with variant load and inertia. © 2010 AACC.

关键词： Linear programming

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A theorem for relationship between the MA process and its inversion for ARMAX identification

A theorem for relationship between the MA process and its in...

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作者： Xin, Bin Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

ISBN: (纸本)9787894631046

The paper presents a theorem to show the relationship between the parameters of the Moving Average (MA) process and those of its inversed process. The theorem can be used for the parameter identification of the MA process. It is further shown in this paper that the parameter identification of autoregressive moving average with exogenous variable model (ARMAX), based on the identification of its MA part, can be easily achieved. The approach, at first, achieves the identification of the ARX part by directly using least-square estimations to find out a straightforward relationship between estimated parameters and observed data. Then, the inversed model of the MA part is identified in a similar way. Finally, the noise variance can be computed by using identified MA parameters. Numerical simulations validate the effectiveness and efficiency of the proposed approach.

关键词： Least squares approximations

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