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A force feedback system based on robot control

A force feedback system based on robot control

引用

World Congress on intelligent Control and automation (WCICA)

作者： Yang Zehong Jia Peifa Zhao Yannan Yang Yuandong The State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing China Tsinghua University Beijing Beijing CN Dept. of Comput. Sci. & Technol. Tsinghua Univ. Beijing China

This paper mainly introduces the designed force feedback system based on robot control. It first introduces the framework of the force feedback system, the higher and lower level control system, and some of the relate... 详细信息

关键词： Robot control Force feedback Laboratories intelligent systems intelligent robots Computer science state feedback Level control Virtual reality intelligent control

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Learning bayesian networks with hidden variables using the combination of em and evolutionary algorithms 5th

引用

5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001

作者： Tian, Fengzhan Lu, Yuchang Shi, Chunyi The State Key Laboratory of Intelligent Technology and System The Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)3540419101

In this paper, a new method, called EM-EA, is put forward for learning Bayesian network structures from incomplete data. This method combines the EM algorithm with an evolutionary algorithm (EA) and transforms the incomplete data to complete data using EM algorithm and then evolve network structures using the evolutionary algorithm with the complete data. In order to learn Bayesian networks with hidden variables, a new mutation operator has been introduced and the function of the crossover has been correspondingly expanded. The results of the experiments show that EM-EA is more accurate and practical than other network structure learning algorithms that deal with the incomplete data. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Evolutionary algorithms

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Incremental learning of bayesian networks with hidden variables

引用

作者： Tian, Fengzhan Zhang, Hongwei Lu, Yuchang Shi, Chunyi State Key Laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University 100084 Beijing China

ISBN: (纸本)0769511198

In this paper, an incremental method for learning Bayesian networks based on evolutionary computing, IEMA, is put forward. IEMA introduces the evolutionary algorithm and EM algorithm into the process of incremental learning, can not only avoid getting into local maxima, but also incrementally learn Bayesian networks with high accuracy in presence of missing values and hidden variables. In addition, we improved the incremental learning process by Friedman et al. The experimental results verified the validity of IEMA. In terms of storage cost, IEMA is comparable with the incremental learning method of Friedman et al, while it is more accurate. © 2001 IEEE.

关键词： Bayesian networks

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Vehicle detection in static road images with PCA-and-Wavelet-Based classifier

Vehicle detection in static road images with PCA-and-Wavelet...

引用

International Conference on intelligent Transportation

作者： Junwen Wu Xuegong Zhang Jie Zhou Department of Automation State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing China

Detecting vehicles from static road images is a difficult task since motion information is no longer usable. This paper presents an algorithm for this task with a pattern classifier built on the principal component analysis or PCA technique. Wavelet transform is adopted in feature extraction phase. Experiments on real road images show the effectiveness of this algorithm.

关键词： Vehicle detection Vehicles Principal component analysis Wavelet transforms Feature extraction Image sequences Roads Image recognition intelligent systems Videos

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SVM-based detection of moving vehicles for automatic traffic monitoring

SVM-based detection of moving vehicles for automatic traffic...

引用

International Conference on intelligent Transportation

作者： Dashan Gao Jie Zhou Leping Xin Department of Automation State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing China

A traffic surveillant system must be capable of working in all kinds of weather and illumination conditions, such as shadows in a sunny day, vehicle reflections in a rainy day and vehicle headlights in the evening. In this paper we propose a robust algorithm to detect real moving vehicles and eliminate the influence of shadows and vehicle headlights by using a pattern classification method. On account of its simple but efficient representation, the histogram of a difference image is used as the features for classification. The classifier is designed based on support vector machine (SVM) due to its high generalization performance. The final experiment shows that the real traffic monitoring system based on our algorithm can detect moving vehicles and separate shadows and headlights robustly and effectively in different weather and illumination conditions.

关键词： Vehicle detection Vehicles Lighting Robustness Support vector machines Support vector machine classification Reflection Pattern classification Histograms Condition monitoring

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Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR

Kernel MSE algorithm: a unified framework for KFD, LS-SVM an...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jianhua Xu Xuegong Zhang Yanda Li Department of Automation State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

ISBN: (纸本)0780370449

We generalize the conventional minimum squared error (MSE) method to yield a new nonlinear learning machine by using the kernel idea and adding different regularization terms. We name it kernel minimum squared error (KMSE) algorithm, which can deal with linear and nonlinear classification and regression problems. With proper choices of the output coding schemes and regularization terms, we prove that KMSE is identical to the kernel Fisher discriminant (KFD) except for an unimportant scale factor, and it is directly equivalent to the least square version for support vector machine (LS-SVM). For continuous real output values, we find that KMSE is the kernel ridge regression (KRR) with a bias. Therefore KMSE can act as a general framework that includes KFD, LS-SVM and KRR as its particular cases. In addition, we simplify the formula to estimate the projecting direction of KFD. Experiments on artificial and real world data sets in numerical computation aspects demonstrate that KMSE is a class of powerful kernel learning machines.

关键词： Kernel Machine learning Least squares methods Support vector machines automation intelligent systems Learning systems Support vector machine classification Least squares approximation Bayesian methods

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Editing support vector machines

Editing support vector machines

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Haixin Ke Xuegong Zhang Department of Automation State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

ISBN: (纸本)0780370449

A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. In this paper, we present an improved version of the method, called editing support vector machine (ESVM), which removes some samples near the boundary from the training set. Experiments show that for cases that the two classes are overlapped, ESVM can get better generalizing ability, and ESVM is also more robust with noises.

关键词： Support vector machines Kernel automation intelligent systems Machine intelligence Noise robustness Risk management Error analysis Nearest neighbor searches

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A novel algorithm of adaptive background estimation

A novel algorithm of adaptive background estimation

引用

IEEE International Conference on Image Processing

作者： Da-shan Gao Jie Zhou Le-ping Xin State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780367251

We propose an adaptive background estimation algorithm for an outdoor video surveillance system. In order to enhance the ability of adaptation to illumination changes and variant noise in long-term running, an improved Kalman filtering model based on the local-region is discussed to dynamically estimate a background image, in which the parameters are predicted by a recursive-least-square adaptive filter. The experimental results on real-world video show that the algorithm can perform robustly and effectively.

关键词： Lighting Adaptive filters Vehicle detection Pixel Kalman filters Filtering Recursive estimation Change detection algorithms Video surveillance Background noise

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Statistical learning and analyses of Chinese ancient books for information retrieval

Statistical learning and analyses of Chinese ancient books f...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Min Zhang Shao-Ping Ma Zhe Jiang Ke Huang State Key laboratory of Intelligent Technology and System Department of Computer Science and Technology Tsinghua University Beijing China

The technique of full text retrieval for modern Chinese has been studied for a long time, but the same cannot be said for ancient Chinese books, especially in China. This paper tries to find the characteristics of Chinese ancient books which can be used for information retrieval. Statistical analysis was carried out on ancient Chinese books of over 35,000,000 words, including most of the works in common use. Based on these experiments some characteristics of ancient Chinese works are analyzed and compared with modern Chinese, including the basic unit of ancient works, the proportion of double character words, sentence length, and the field dependency of ancient Chinese works. We then give conclusions on ancient Chinese which is useful for information retrieval, especially when building inverted indexes and selecting the index unit. Depending on the conclusion, a full-text retrieval system for ancient Chinese books has been designed and realized. It shows that statistical learning and analyses are a great help in ancient Chinese information retrieval.

关键词： Statistical learning Information analysis Books Information retrieval Modems Natural languages Statistical analysis Continents Frequency History

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Incremental learning of Bayesian networks with hidden variables

引用

IEEE International Conference on Data Mining (ICDM)

作者： Fengzhan Tian Hongwei Zhang Yuchang Lu Chunyi Shi The State Key Laboratory of Intelligent Technology and System The Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)0769511198

An incremental method for learning Bayesian networks based on evolutionary computing, IEMA, is put forward. IEMA introduces the evolutionary algorithm and EM algorithm into the process of incremental learning; it can avoid getting into local maxima, and also incrementally learn Bayesian networks with high accuracy in the presence of missing values and hidden variables. In addition, we improved the incremental learning process by N. Friedman and M. Goldschmidt (1997). The experimental results verified the validity of IEMA. In terms of storage cost, IEMA is comparable with the incremental learning method of Friedman et al, while it is more accurate.

关键词： Bayesian methods Statistics Learning systems Evolutionary computation Laboratories intelligent systems intelligent networks Computer science Computer networks Costs

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