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Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network system

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Defence technology（防务技术） 2009年第2期5卷 152-157页

作者：杨晨光涂序彦陈杰 Systems Engineering Research Institute China State Shipbuilding Corporation Department of Automation School of Information Science and Technology Beijing Institute of Technology Ministry of Education of Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are *** calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly *** such method,we can avoid the inconformity of the description obtained from the traditional index *** new indexes are also proposed,*** index,overlap index and cover index,which help manage the relationship among several *** normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well ***,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement *** is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

关键词：网络系统优化问题破坏概率算法改进核武器蜜蜂婚姻 Sigmoid函数

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Shannon Entropy-Based Adaptive Fusion Particle Filter for Visual Tracking

Shannon Entropy-Based Adaptive Fusion Particle Filter for Vi...

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Chinese Conference on Pattern Recognition (CCPR)

作者： Yu Song Qingling Li Fuchun Sun State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing China Institute of Automation Chinese Academy and Sciences Beijing China

Shannon entropy is effective uncertainty measurement criterion for stochastic system. In this paper, adaptive fusion particle filter is proposed for visual tracking by introduced Shannon entropy in particle filter framework. Firstly, the particle filter, which is considered as the process of particles assimilating negative entropy to reduce uncertainty, is surveyed from viewpoint of information theory. Secondly, maximum negative entropy criterion is proposed to select tracking feature form features pool online. At last, color histogram and edge orientation histogram features are utilized in experiments, tracking results show that the proposed algorithm is a robust and accuracy tracking algorithm.

关键词： Particle filters Particle tracking Entropy Histograms Electronic mail Laboratories intelligent systems automation Measurement uncertainty Stochastic systems

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Inverse Q filtering to enhance seismic resolution

Inverse Q filtering to enhance seismic resolution

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ning Tu Wen-kai Lu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University China

Due to medium absorption, the resolution of a seismic profile gradually decreases as seismic waves propagate in the earth, known as the attenuation effect. In order to obtain high-resolution seismic profiles which are desirable for characterization of the subsurface formation, we have to quantify the medium absorption effect with medium quality factor Q. After Q is estimated, an inverse Q filter can be designed to enhance the resolution of the seismic profile. Estimation Q from surface seismic data can be difficult since the spectra of the reflected seismic waves are often distorted by thin beds in the formation. In order to remove the influence of thin beds, we apply our HOS based wavelet extraction method here for reliable Q estimation and inverse Q filtering. Synthetic examples verified the effectiveness of the proposed method.

关键词： Filtering Seismic waves Attenuation Absorption Surface waves Data mining Reflectivity Q factor Filters Reflection

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A method of correction for marine seismic acquisition

A method of correction for marine seismic acquisition

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ji Wang Wen-kai Lu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Beijing China

Data of towed streamer acquiring suffer from influences of ocean currents and other environment factors. Variations in lateral and time positions of receivers will produce acquisition errors and reduce resolution of stack sections. In this paper, we proposed a method to correct those variations in sub-pixel domain by interpolation and correlate calculation. We applied the method on an actual dataset acquired at an offshore field in China. Results demonstrate the method could correct variations effectively and enhance the consistence and the resolution of the acquired dataset.

关键词： Oceans Interpolation Sonar equipment Tides Laboratories Marine technology Streaming media Reflection intelligent systems Information science

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Probabilistic Labeled Semi-supervised SVM

Probabilistic Labeled Semi-supervised SVM

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Mingjie Qian Feiping Nie Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList) Department of Automation Tsinghua University Beijing China

Semi-supervised learning has been paid increasing attention and is widely used in many fields such as data mining, information retrieval and knowledge management as it can utilize both labeled and unlabeled data. Laplacian SVM (LapSVM) is a very classical method whose effectiveness has been validated by large number of experiments. However, LapSVM is sensitive to labeled data and it exposes to cubic computation complexity which limit its application in large scale scenario. In this paper, we propose a multi-class method called Probabilistic labeled Semi-supervised SVM (PLSVM) in which the optimal decision surface is taught by probabilistic labels of all the training data including the labeled and unlabeled data. Then we propose a kernel version dual coordinate descent method to efficiently solve the dual problems of our Probabilistic labeled Semi-supervised SVM and decrease its requirement of memory. Synthetic data and several benchmark real world datasets show that PLSVM is less sensitive to labeling and has better performance over traditional methods like SVM, LapSVM (LapSVM) and Transductive SVM (TSVM).

关键词： Support vector machines Semisupervised learning Data mining Information retrieval Knowledge management Laplace equations Large-scale systems Training data Kernel Labeling

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Stock Price Forecasting by Combining News Mining and Time Series Analysis

Stock Price Forecasting by Combining News Mining and Time Se...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xiangyu Tang Chunyu Yang Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

Stock price forecasting has aroused great concern in research of economy, machine learning and other fields. Time series analysis methods are usually utilized to deal with this task. In this paper, we propose to combine news mining and time series analysis to forecast inter-day stock prices. News reports are automatically analyzed with text mining techniques, and then the mining results are used to improve the accuracy of time series analysis algorithms. The experimental result on a half year Chinese stock market data indicates that the proposed algorithm can help to improve the performance of normal time series analysis in stock price forecasting significantly. Moreover, the proposed algorithm also performs well in stock price trend forecasting.

关键词： Time series analysis Economic forecasting Text mining intelligent agent Data mining Algorithm design and analysis Feature extraction technology forecasting Stock markets Conferences

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Regular simplex criterion: A novel feature extraction criterion

Regular simplex criterion: A novel feature extraction criter...

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

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList) Department of Automation Tsinghua University Beijing China

Feature extraction is an important topic in machine learning. There are two representative criterions for feature extraction, i.e. Fisher Criterion and Maximum Margin Criterion. In this paper, we propose a new criterion, called Regular Simplex Criterion. This criterion requires that samples from the same class are projected to the same point, while samples from different classes have unit distance. Under this criterion, we present a novel dimensionality reduction method, namely Linear Simplex Analysis (LSA). LSA is solved by multivariate linear regression with a specific definition of class indicator matrix which has a strong geometrical interpretation, i.e. each column of this matrix corresponds to a vertex of a regular simplex. Several variants of LSA, e.g. Regularized Simplex Analysis (RSA) and Kernel Simplex Analysis (KSA), are also proposed. Encouraging experimental results on UCI machine learning database indicate that the new criterion as well as the proposed methods are very effective.

关键词： Feature extraction Machine learning Kernel Linear discriminant analysis Laboratories Linear regression Spatial databases Principal component analysis intelligent systems Learning systems

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Two dimensional Maximum Margin Criterion

Two dimensional Maximum Margin Criterion

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

Maximum Margin Criterion is a well-known method for feature extraction and dimensionality reduction. In this paper, we propose a novel feature extraction method, namely Two Dimensional Maximum Margin Criterion (2DMMC), specifically for matrix representation data, e.g. images. 2DMMC aims to find two orthogonal projection matrices to project the original matrices to a low dimensional matrix subspace, in which a sample is close to those in the same class but far from those in different classes. Both theoretical analysis and experiments on benchmark face recognition data sets illustrate that the proposed method is very effective and efficient.

关键词： Linear discriminant analysis Feature extraction Principal component analysis Covariance matrix Scattering Laboratories intelligent systems Information science automation Face recognition

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Two Dimensional Nonnegative Matrix Factorization

Two Dimensional Nonnegative Matrix Factorization

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IEEE International Conference on Image Processing

作者： Quanquan Gu Jie Zhou Department of Automation Tsinghua University Beijing China State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

Nonnegative Matrix Factorization (NMF) has been widely used in computer vision and pattern recognition. It aims to find two nonnegative matrices whose product can well approximate the original matrix, which naturally leads to parts-based representation. In this paper, we propose a Two Dimensional Nonnegative Matrix Factorization (2DNMF), specifically for a sequence of matrices. In contrast to NMF which applies for only a single matrix, and finds only one base matrix, 2DNMF aims to find two base matrices to represent the input matrices in a low dimensional matrix subspace. It not only inherits the advantages of NMF, but also owns the properties low computational complexity, as well as high recognition accuracy. Experiments on benchmark image recognition data sets illustrate that the proposed method is very effective and efficient.

关键词： Sparse matrices Image recognition Principal component analysis Laboratories Computer vision Pattern recognition Computational complexity intelligent systems Information science automation

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Multiple Kernel Maximum Margin Criterion

Multiple Kernel Maximum Margin Criterion

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IEEE International Conference on Image Processing

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList Department of Automation Tsinghua University Beijing China

Maximum Margin Criterion (MMC) is an efficient and robust feature extraction method, which has been proposed recently. Like other kernel methods, when MMC is extended to Reproducing Kernel Hilbert Space via kernel trick, its performance heavily depends on the choice of kernel. In this paper, we address the problem of learning the optimal kernel over a convex set of prescribed kernels for Kernel MMC (KMMC). We will give an equivalent graph based formulation of MMC, based on which we present Multiple Kernel Maximum Margin Criterion (MKMMC). Then we will show that MKMMC can be solved via alternative optimization schema. Experiments on benchmark image recognition data sets show that the proposed method outperforms KMMC via cross validation, as well as some state of the art methods.

关键词： Kernel Feature extraction Covariance matrix Hilbert space Principal component analysis Scattering Laboratories Space technology Image recognition intelligent systems

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