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This paper proposed a model and algorithm to mine local association rules from existing spatial dataset while fully taking the fact that spatial heterogeneity may widely exist in reality. The essential part of the model is the calculation localized measure of association strength (LMAS) which is used to quantify local association patterns. Spatial association relations are specifically defined as...
In this paper, in order to overcome the deficiencies of Polana's method and fuzzy alternation entropy method, we presented a technique of image segmentation that integrates these both into a system. The experiment results show that our method is robust and effective.
In this paper, a novel classifier is proposed to classify microarray data using principal curves. Principal curves are the non-linear generalization of principal components. Intuitively, a principal curve `passes through the middle of the data cloud'. As a kind of new classification technique, Principal Curve-based classifier (PC) involves a novel way of computing a principal curve for each class...
The pattern recognition of remote sensing images is based on the different spectral characteristics of surface features to identify the features type, mainly including the supervised classification and unsupervised classification. In this paper, the TM remote sensing images are preprocessed firstly, and then land-covered features are classified using the maximum likelihood method, the minimum distance...
Aiming at the online fault diagnoses, the texture features which are usually used in image processing are firstly applied in the early fault signal recognition problems. After the parameter R based on gray-level co-occurrence matrix is defined, the parameter R extraction method of texture features is presented. Then, the novel fault signal recognition algorithm based on the parameter R of the texture...
Aims to provide general technicians who manage pects in production with a convenient way to recognize them, a novel method to classify insects by analyzing color histogram and GLCM (Gray-Level Co-occurrence Matrices) of wing images is proposed. The wing image of lepidopteran insect is preprocessed to get the ROI (Region of Interest); then the color image is first converted from RGB(Red-Green-Blue)...
The feature extracted by the rotation invar -iants of radial Tchebichef moments could make the image analysis more effectively. However, the classical method adopted integral point sampling which has defects of too many sampling points in the centre of unit circle and insufficient samplings on the edge of the circle. It reduces the efficiency of feature extraction. In order to resolve this problem,...
This paper mainly addresses the issue of determining the number of clusters. Over a data set, a minimum spanning tree is constructed, and then m-ary tree is employed to search clusters on the minimum spanning tree. The number of large and well separate m-ary tree is the number of clusters. Numerical experiments show that the performance of the proposed method is promising.
In the pattern recognition subspace method, the researcher has paid more attention to extract feature subspace, then expressed individual prototype with the training sample mean. Because the number of training sample is limited, there is certain difference between the sample mean and the individual prototype. In order to reduce this difference, a sample restraint clustering algorithm was proposed,...
The pattern information (PI) method was reasonably modified and firstly introduced to the observation data processing of electromagnetic satellite in this paper. Taking the moderate-strong earthquakes as examples, the IAP data recorded by the France DEMETER electromagnetic satellite were systematically processed with the modified PI method. We can find that the variation in non-seismic regions and...
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler...
The health status of Yantai urban water-based system(Yantai UWBS) from 1996 to 2005 is evaluated with fuzzy pattern recognition model based on relative membership degree theory. Firstly, with AHP method, the index system is divided into four hierarchies: object hierarchy, criteria hierarchy, factor hierarchy and index hierarchy. And reference to relative national normative criterions or the index...
A multi-attribute comprehensive evaluation with vagueness and imprecision can be made flexibly by intuitionistic fuzzy set (IFS). In this paper, the general operator in intuitionistic fuzzy comprehensive evaluation (IFCE) is presented in accordance with the theory that vague set (VS) is equivalent to IFS and fuzzy set (FS) is a special case of IFS. The general operator is more reasonable and easier...
The loan risk is the possibility of loan and interest losses caused by being unable to recover the loans on schedule. So a set of modern quantitative methods to effectively identify the credit risk is urgently needed to fully reveal the future solvency capability of evaluation object. This paper has firstly constructed a set of credit evaluation index system, and on that basis, using a large number...
Although GPS-based travel survey has been studied by many, automated travel mode detection still remains a technical challenge. This paper proposed and tested a fuzzy approach to travel mode recognition from the GPS travel data collected from 32 volunteers for 142 days in Shanghai. Four speed-related fuzzy variables were selected to characterize five movement patterns (walk, bike, bus, rail, and rest)...
A generalised fuzzy approach to statistical modelling techniques for pattern recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to third-order two-dimensional hidden Markov mode(2-D HMM). 2-D HMM is an extension of 1-D HMM to 2-D, it provides a reasonable statistical method to model matrix data. By...
In this paper, we study the problems about fuzzy degrees of fuzzy n - cell numbers, which can be used in ranking uncertain multi-channel digital signal information. Firstly, we introduce concepts of vector-valued fuzzy degrees and weight (number-valued) fuzzy degrees of fuzzy n - cell numbers, and then investigate their properties. At last, in order to be conveniently used in applications, we give...
When the ship is damaged after weapon attack, it is necessary for commanders to recognise its unsinkability grade quickly. Through unsinkability classification, we can know whether the ship will sink or not and its sinking probability. The unsinkability classification is a N-class pattern recognition problem. The fuzzy support vector machine (FSVM) is used to distinguish a certain unsinkability grade...
In order to solve the problem of feature extraction in the gear fault pattern recognition, a method of feature extraction based on atomic decomposition was proposed. Signals are rapidly decomposed using matching pursuit with the constructed Gabor dictionary. The frequency parameters and respective correlation values of the selected atoms constitute the feature vector of signal. Binary Tree Support...
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