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Incremental Clustering Algorithm via cross-entropy

Journal of systems Engineering and Electronics 2005年第4期000卷

作者： Guan Tao Xu Jiucheng Feng Boqin State Key Laboratory of Intelligent Technology and Systems，Dept. of Computer Science and Technology， Tsinghua Univ.， Beijing 100080， P. R. China School of Electronics and Information Engineering， Xi'an Jiaotong Univ.， Xi'an 710049， P. R. China Coll. of Computer and Information Technology， Henan Normal Univ.， Xinxiang 453007， P. R. China School of Electronics and Information Engineering， Xi'an Jiaotong Univ.， Xi'an 710049， P. R. China

A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on (fuzzy) cross-entropy(CE). This algorithm is divided into two parts: initial clustering process and incremental clustering process. The former calculates fuzzy cross-entropy or cross-entropy of one point relative to others and a hierachical method based on cross-entropy is used for clustering static data sets. Moreover, it has the lower time complexity. The latter assigns new points to the suitable cluster by calculating membership of data point to existed centers based on the cross-entropy measure. Experimental comparisons show the proposed method has lower time complexity than common methods in the large-scale data situations or dynamic work environments.

关键词： incremental clustering (fuzzy)cross-entropy hierachical clustering

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Noise reduction in coherence cube

Noise reduction in coherence cube

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2004 Society of Exploration Geophysicists Annual Meeting, SEG 2004

作者： Lu, Wenkai Zhang, Shanwen Xiao, Huanqin State Key Laboratory of Intelligent Technology and Systems Dept. of Automation Tsinghua University Beijing100084 China Shengli Oilfield Limited Company Dongying Shandong Province257100 China

Coherence analysis techniques play a key role in seismic interpretation. To suppress the lower-coherence-strip along the strata (LCSAS) and the random noise, multi-traces and large temporal window for coherence analysis are needed. However the coherence cube will lose its resolution in space-time domain when multi-traces and large temporal window are applied. In this paper, a novel coherence cube image enhancement technique based on coherent filter is proposed. Undesired LCSAS and random noise attenuation by our method makes it possible to exploit less seismic traces and to use small temporal window in coherence analysis algorithms and obtain fault information with high resolution and high signal-noise-ratio(SNR) at the same time. Taking advantage of the direction difference between LCSAS and the desired lower-coherence-strip along the faults(LCSAF), our method suppresses the undesired LCSAS and random noise by local dip scanning and polynomial filter. Application results on real data set show that our method can obtain real faults image with high resolution and SNR. © 2004 SEG Annual Meeting. All rights reserved.

关键词： Image enhancement

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Hidden geology information mining based on spatial deblurring of 3D seismic data

Hidden geology information mining based on spatial deblurrin...

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2004 Society of Exploration Geophysicists Annual Meeting, SEG 2004

Three-dimensional seismic data after migration provides a great amount of information for seismic interpretation. The seismic data is blurred in the space domain after migration because of the migration aperture. The blurred seismic data will hide a lot of geological information. This paper presents an image deblurring based technique to improve the space resolution of the seismic data, which enables the following coherence analysis algorithm give more details of the hidden geology information. Applications on real dataset show that promising results are obtained by the proposed method. © 2004 SEG Annual Meeting. All rights reserved.

关键词： Image enhancement

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A reasoning algorithm of applying causality diagram to fault diagnosis of complex hybrid systems

A reasoning algorithm of applying causality diagram to fault...

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World Congress on intelligent Control and Automation (WCICA)

作者： Xinghua Fan Maosong Sun State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

ISBN: (纸本)0780382730

Fault diagnosis of a complex hybrid system, which bears causality loops, non-deterministic system dynamics, presence of not-directly-observable system states, delay of fault effects, and the mixture of discrete and continuous variables in the observed signals, is a very challenging task. This paper presents a novel method based on the causality diagram model for this problem. The method extends the causality diagram to the 2-time-slice causality diagram (2-TSCD), which represents both the fault propagation and the conditional probability distribution of system states in two consecutive time-slices. In the 2-TSCD, basic event variables represent failures; node event variables represent system states at time-slice t+l; some system states at time-slice t are introduced as basic event variables to represent the delay of fault effects. We develop a reasoning algorithm for the 2-TSCD: firstly, the candidate fault modes at time-slice t+l are acquired by using fault propagation; then all system states at time-slice t, which are introduced as basic event variables and cannot be observed directly, are predicted based on the 2-TSCD in previous time-slice; and the posterior probabilities of candidate fault modes are calculated and ranked at last. The advantages of the proposed method are the flexibility of knowledge representation and the rapidity of diagnosis.

关键词： Fault diagnosis Delay effects Knowledge representation Sun Laboratories Hybrid intelligent systems Delay systems Probability distribution Uncertainty

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Robust speech recognition based on neighborhood space

Robust speech recognition based on neighborhood space

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International Conference on Signal Processing Proceedings (ICSP)

作者： Binfeng Yan Xiaoyan Zhu State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

In this paper, we present an approach to robust speech recognition based on neighborhood space. To achieve performance robustness under mismatch between training and testing conditions, we propose to use neighborhood space of each underlying model to produce Bayesian predictive density as observation probability density. Experimental results show that the proposed method improves the performance robustness.

关键词： Robustness Speech recognition Hidden Markov models Space technology Acoustic testing Predictive models Bayesian methods Automatic speech recognition Acoustic noise Laboratories

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A recognition based system for segmentation of touching handwritten numeral strings

A recognition based system for segmentation of touching hand...

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International Workshop on Frontiers in Handwriting Recognition

作者： Yun Lei C.S. Liu X.Q. Ding Qiang Fu State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

A novel recognition-based system for segmentation of touching handwritten numeral strings is proposed. In this paper, we combine external contour analysis and projection analysis to find candidate segmentation points. With internal contour analysis, the candidate segmentation points is utilized to determine the corresponding candidate segmentation lines with which the numeral string is over-segmented. Each sub-image of the over segmented string is defined as a fragment. The combination of one or more adjacent fragments is defined as a clique. Thus, each candidate segmentation result is composed of one or more cliques. Subsequently, all the candidate segmentation results are described in a probabilistic model, and a classifier is embedded to recognize each clique. Finally, with the maximum a posterior (MAP) criterion, the optimal segmentation result is selected from all candidate segmentation results. This scheme is effective and robust for both single and multiple touching numerals. Experiment results on collection of samples from NIST SD19 show that our system can achieve a correct rate of 97.72% without rejection, which compares favorably with those reported in the literature.

关键词： Handwriting recognition Image segmentation Skeleton Data mining Laboratories intelligent systems Robustness NIST Image reconstruction Character recognition

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fIntegrating confidence scores for utterance verification

fIntegrating confidence scores for utterance verification

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World Congress on intelligent Control and Automation (WCICA)

作者： Binfeng Yan Xiaoyan Zhu State Key Laboratory of Intelligent Technology & Systems Tsinghua University Beijing China

ISBN: (纸本)0780382730

In this paper we present an approach to integrating confidence scores for utterance verification based on neural network. In addition to the confidence scores computed at the phonetic level, we use various novel confidence scores, including segmental confidence scores, likelihood ratio and recognition results of LPC feature. We describe a method to combine different confidence scores via a neural work for word hypotheses. Our experimental evaluation shows that the novel confidence scores are effective and the proposed combination method gives better results than other classification method in keyword spotting system.

关键词： Neural networks Laboratories intelligent systems intelligent networks Electronic mail Computer networks Linear predictive coding Speech recognition

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Switching ARIMA model based forecasting for traffic flow

Switching ARIMA model based forecasting for traffic flow

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guoqiang Yu Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University China

ISBN: (纸本)0780384849

Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where the switching ARIMA (autoregressive integrated moving average) model is a special case. Short-term forecasting of traffic flows is an essential part of intelligent traffic systems (ITS). We apply the switching ARIMA model to a traffic flow series. We have observed that the conventional switching model is inappropriate to describe the pattern changing. Thus, the variable of duration is introduced and we use the sigmoid function to describe the influence of duration to the transition probability of the patterns. Based on the switching ARIMA model, a forecasting algorithm is presented. We apply the proposed model to real data obtained from UTC/SCOOT systems in Beijing's traffic management bureau. The experiments show that our proposed model is applicable and effective.

关键词： Predictive models Traffic control Telecommunication traffic intelligent systems Laboratories Automation Stochastic processes Probability distribution Artificial intelligence Artificial neural networks

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Mamdanian logic

Mamdanian logic

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Yalin Zheng Changshui Zhang Xing Yi State Key Laboratory for Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780383532

In the framework of stratified fuzzy propositional logic F, the regular harmonious neighborhood structure determined by the Mamdanian approximate function Hgt is the typical and best paradigm of approximate reasoning neighborhood algorithms. The advantage of Mamdanian algorithm comes from the regular harmoniousness property of Hgt presented by us in this paper, which guarantees that when the input of the obtained new approximate knowledge is close enough to that of the standard knowledge, not only the approximate reasoning consequence of the new knowledge is adequately close to that of the standard knowledge, but also the new knowledge itself is close enough to the standard knowledge.

关键词： Fuzzy reasoning Fuzzy logic Fuzzy control Humans Artificial intelligence Automatic control Laboratories intelligent systems Automation Impedance matching

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Short-term traffic flow forecasting using Sampling Markov Chain method with incomplete data

Short-term traffic flow forecasting using Sampling Markov Ch...

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IEEE Symposium on intelligent Vehicle

作者： Shiliang Sun Guoqiang Yu Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780383109

Short-term traffic flow forecasting is an important problem in the research area of intelligent transportation system. In practical situations, flow data may be incomplete, that is, partially missing or unavailable, where few methods could implement forecasting successfully. A method called Sampling Markov Chain is proposed to deal with this circumstance. In this paper, the traffic flow is modeled as a high order Markov Chain; and the transition probability from one state to the other state is approximated by Gaussian Mixture Model (GMM) whose parameters are estimated with Competitive Expectation Maximum (CEM) algorithm. The incomplete data in forecasting the trend of Markov Chain is represented by enough points sampled using the idea of Monte Carlo integration. Experimental results show that the Sampling Markov Chain method is applicable and effective for short-term traffic flow forecasting in case of incomplete data.

关键词： Sampling methods Traffic control intelligent transportation systems Predictive models Monte Carlo methods Parameter estimation state estimation Sun Prediction methods Neural networks

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