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2009 IEEE/RSJ International Conference on intelligent Robots and systems, IROS 2009

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

ISBN: (纸本)9781424438044

In this paper the problem of path following control of autonomous vehicles subject to sliding is addressed. First a kinematic model is built which takes sliding effects into account by introducing two additional tire sliding angles. Since the tire sliding angles cannot be directly measured by sensors, an adaptive robust Luenberger observer is designed. With this observer, the tire cornering stiffness instead of the sliding angles is identified in presence of time-varying lateral disturbance. The Lyapunov stability theory guarantees that the estimated cornering stiffness would converge to a neighborhood of the real value when control inputs excitated the system persistently. But due to the existence of the lateral disturbance which causes loss of accuracy of the sliding angle reconstruction, the previously designed anti-sliding controller whose effectiveness completely depends on the estimation of the sliding angles cannot yield satisfactory results. To overcome this problem a tire-oriented kinematic model is built in which the inaccuracy of the sliding angle reconstruction is modeled in form of additive disturbances to the kinematic model. By transforming the tire-oriented kinematic model into a perturbed chained system, a sliding mode controller, which is robust to both the sliding effects and the negative effects of the lateral disturbance is designed with the help of the natural algebraic structure of the chained systems. Simulation results show that the proposed methods can provide accurate estimation of the sliding angles and guarantee high anti-sliding control accuracy even in presence of time-varying lateral disturbance. © 2009 IEEE.

关键词： Tires

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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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Short-term PV generation system direct power prediction model on wavelet neural network and weather type clustering

Short-term PV generation system direct power prediction mode...

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2013 5th International Conference on intelligent Human-Machine systems and Cybernetics, IHMSC 2013

作者： Yang, Ying Dong, Lei School of Automation Beijing Institute of Technology China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

ISBN: (纸本)9780769550114

With the increase of the capacity of PV generated systems, how to eliminate the problem caused by the randomness of power output for photovoltaic system becomes more significant. Most of the existing photovoltaic prediction is Based on the solar radiation. However, it's difficult to implement in China due to insufficient solar radiation station available and poor forecasting performance. In addition, indirect forecasting cannot consider the factors related with PV system. A novel power forecasting model using historical power is proposed to solve the problems. Furthermore, in order to adapt sudden weather changes, the future weather type was recognized by using self-organizing feature map(SOM). Then, PV power generation in each weather type could be forecasted from its corresponding forecast network and the over fitting issue of single network model could be addressed. Wavelet neural network is combined with wavelet analysis and neural network. It is compatible with the good time-frequency property and good fault tolerant ability of neural network. Wavelet neural network can optimize the forecasting model. The experimental results indicate that the prediction has high precision and can be applied in stable operation of photovoltaic generation system. © 2013 IEEE.

关键词： Solar radiation

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Dependency-Enhanced Reordering Model for Chinese-English SMT 35

Dependency-Enhanced Reordering Model for Chinese-English SMT

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第35届中国控制会议

作者： WANG Miaomiao XIE Guo DU Jinhua Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dependency tree and corresponding word alignments between the source-side and the target-side *** a maximum entropy classifier is used to calculate the orientation probability in terms of swap or monotone between two *** a result,a reordering rule set is *** different ways are proposed to filter out the rule ***,the dependency parsing trees of the training data,development set and the test set are traversed,and if the syntactic sub-tree structure matches the rules in the rule set,the word orders will be ***,a reordered source-side sentence is generated and then fed into an SMT system for *** conducted on NIST Chinese-English MT data sets show that the proposed method significantly improves translation performance by 0.46 BLEU compared to the baseline system.

关键词： Statistical Machine Translation Word Alignment Dependency Syntactic Parsing

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A Positioning Algorithm of Autonomous Car Based on Map-matching and Environmental Perception 33

A Positioning Algorithm of Autonomous Car Based on Map-match...

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第三十三届中国控制会议

作者： XU Qian WANG Meiling DU Zhifang ZHANG Yi School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex System Chongqing Jialing Huaguang Photoelectric Technology Co.Ltd.

ISBN: (纸本)9789881563842

Autonomous car is an important tool for transportation and military in the future,and its precise positioning is the basis of autonomous *** positioning algorithms based on map-matching for autonomous car make little use of environmental perception *** solve this problem,a positioning algorithm is proposed in this paper,which is based on map-matching and environmental perception for autonomous *** algorithm includes macroscopic road matching and microscopic precise *** for macroscopic road matching,the algorithm makes use of computational geometry to match the position of autonomous car to the corresponding road,based on GPS point and map information of the road *** for microscopic precise positioning,the algorithm makes use of the environmental perception,which is detected by the autonomous car to make precise *** road matching provides matching road to microscopic precise positioning,and microscopic precise positioning eliminates gross error produced in macroscopic road *** real car tests,the algorithm can match map quickly,improving the positioning precision with strong real-time.

关键词： Autonomous car,Map-matching,Environmental perception,Computational geometry

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Design and Implementation of the Knowledge Base for Battlefield Task Management system 33

Design and Implementation of the Knowledge Base for Battlefi...

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第三十三届中国控制会议

作者： Zhang Jia Wang Xiang School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education

ISBN: (纸本)9781479947249

In order to reduce the operators on the battlefield, and obtain adequate knowledge and improve reaction speed to enhance combat efficiency, the paper builds a knowledge base of battlefield mission management system to manage a variety of required knowledge on the battlefield and supervise each mission profile. Knowledge is presented by production method and is reasoned by forward reasoning method. According to different levels of battlefield tasks, task links are used to organize knowledge. Take combat preparation mission profile as an example, we design every data sheet of the knowledge base. The knowledge base of the battlefield management system could enhance the reusability, sharing and timeliness of knowledge on the battlefield.

关键词： Battlefield Task Management Knowledge Base Task Links Mission Profile

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An Online Parameter Estimation Method for a class of Nonlinear systems Based on Particle Filter 34

An Online Parameter Estimation Method for a class of Nonline...

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第三十四届中国控制会议

作者： XIE Guo ZHANG Dan HEI Xinhong QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781467374439

Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.

关键词： Nonlinear system,Particle filter,Parameter set,Online parameter identification

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The Identification of Stochastic Time-varying Parameters for Nonlinear system 35

The Identification of Stochastic Time-varying Parameters for...

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第35届中国控制会议

作者： XIE Guo ZHANG Dan QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functions(PDFs) of the parameters are known. This parameter identification and states estimation method is realized based on expectation maximization(EM) algorithm and particle filter. Firstly, parameter particles are generated randomly according to the PDF of parameter. Secondly, the particle filter is employed to estimate system states corresponding to each group of the parameters, and the weight of each group parameters is calculated according to the Bayesian theory. Then the new iteration of parameter is obtained by adopting the expectation maximization algorithm. Lastly, the real parameters are obtained along with system operation. Numerical illustrations are presented to exhibit the effectiveness of the method proposed herein, and the performance of the method is examined.

关键词： Stochastic time-varying parameter Parameter identification Nonlinear system Expectation Maximization algorithm(EM)

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Modeling and Simulation of Thermoelectric Power Generation system Based on Finite Element Method 33

Modeling and Simulation of Thermoelectric Power Generation S...

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第三十三届中国控制会议

作者： LI YONG DENG FANG School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (纸本)9781479947249

The temperature distribution is the crucial part in the design of the thermoelectric power generation system. Based on the temperature distribution, the system efficiency and stability could be effectively improved by using structure design of the thermoelectric power generation system. In this paper, the differential equation model and the finite element governing equation of thermoelectric power generation system is established, the model is analyzed through the finite element method, and the commercial finite element analysis software Ansys is used to simulate the system temperature distribution, with the assistant of APDL(Ansys Parametric Design Language), a parametric model is built and then the temperature field distribution is *** simulation results are not only beneficial to provide guidance for the optimum of thermoelectric power generation system,but also meaningful to evaluate the feasibility of the design scheme of the system.

关键词： Thermoelectric Power Generator system,Finite Element Method,Temperature Distribution,Structure Optimum,system Evaluation

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Adaptive object tracking based on vision sensor and milli-meter wave radar

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Journal of Computational Information systems 2014年第8期10卷 3283-3290页

作者： Jin, Lu Fu, Mengyin Yang, Yi Key Laboratory of Intelligent Control and Decision of Complex Systems Ministry of Education Beijing 100081 China School of Automation Beijing Institute of Technology Beijing 100081 China

The object tracking for driver assistant system by the multi-sensors fusion has drawn much attention recently. With the complexity of space alignment, a new space alignment algorithm is proposed to project the object by millimeter wave radar onto the image plane. The method needs not to estimate interior and exterior parameters between two sensors. Considering alignment result as the measurement, a new tracking algorithm based on the Mean Shift with Unscented Kalman Filter and Information Measure of Multi-Scale Image is proposed. The Unscented Kalman Filter firstly is used to predict the initial iteration position for Mean Shift algorithm. After the object position is located by Mean Shift algorithm, the size of tracking window is adjusted adaptively through the size change ratio of adjacent frame. The results show that the proposed algorithm can track accurately the object whose size is gradually increasing or decreasing, and the algorithm also reduces the iteration times, the location time and the tracking error. © 2014 Binary Information Press.

关键词： Alignment

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