For the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for useful patterns in the data. the a...
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As the development of internet, the risk of exposing to malicious intrusion via network grows higher and higher. To make it possible defense such a malicious intrusion, it is indispensable to simulate and analyze vuln...
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ISBN:
(纸本)8955191197
As the development of internet, the risk of exposing to malicious intrusion via network grows higher and higher. To make it possible defense such a malicious intrusion, it is indispensable to simulate and analyze vulnerabilities of network. In this paper, we propose network intrusion model and intrusion scenario tree lor detecting and analyzing intrusion patterns and verify how it can be adopted to the simulation.
this paper presents a novel approach to the analysis of detection signals from optical sensors. the proposed technique is based on the patternrecognition algorithms that belong to the family of composite linear filte...
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this paper presents a novel approach to the analysis of detection signals from optical sensors. the proposed technique is based on the patternrecognition algorithms that belong to the family of composite linear filters and is compared against a MAP-estimate approach. Our study is focused on the information recovery from the reflectivity spectrum of a Bragg sensor measured with an optical spectrum analyzer. However we note that the same scheme would be equally useful for the extraction of information obtained with a polarimetric fibre sensor or an optical chemical sensor, where in general the detection signal is multidimensional and includes data related to several measured parameters.
this paper is concerned withthe application of fuzzy neural networks to fault diagnosis systems for rotary machines. In practical fault diagnosis, it is very difficult to improve the recognition rate of pattern recog...
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this paper is concerned withthe application of fuzzy neural networks to fault diagnosis systems for rotary machines. In practical fault diagnosis, it is very difficult to improve the recognition rate of patternrecognition, especially when the sample data are similar. To solve these difficulties, a fault diagnosis system using fuzzy neural networks is proposed in this research. A fault diagnosis system with fuzzy neural networks is based on a series of standard fault pattern pairings between fault symptoms and fault. Fuzzy neural networks are trained to memorize these standard pattern pairs. Unlike other neural networks, fuzzy neural networks adopt bi-directional association. they make use of information from boththe fault symptoms and the fault patterns, which can improve recognition rate greatly. When an unknown sample becomes the input for a trained fault diagnosis system, the fault diagnosis system can make fault diagnosis by bi-directional association of fuzzy neural networks. through experiments with a rotor testing table and applications in monitoring and fault diagnosis of water pump sets of oil plant, it is verified that fuzzy neural networks have a well distinguished ability and are effective to perform fault diagnosis of rotary machines.
this paper describes a method to accelerate the generation of shape primitives for N-dimensional images XN. these shape primitives can be used in conditions for topology preserving erosion or skeletonization in Ndimen...
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We use a mathematical morphology approach to compute the surface and curve skeletons of a 3D *** focus on the behaviour of the surface skeleton, in particular the reversibility for the case when the skeleton is, and i...
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the proceedings contain 23 papers. the special focus in this conference is on Data Structures and Representation. the topics include: Construction of combinatorial pyramids;on graphs with unique node labels;maximal in...
ISBN:
(纸本)354040452X
the proceedings contain 23 papers. the special focus in this conference is on Data Structures and Representation. the topics include: Construction of combinatorial pyramids;on graphs with unique node labels;maximal independent directed edge set;functional modeling of structured images;building of symbolic hierarchical graphs for feature extraction;comparison and convergence of two topological models for 3D image segmentation;tree edit distance from information theory;self-organizing graph edit distance;graph edit distance with node splitting and merging, and its application to diatom identification;orthonormal kernel kronecker product graph matching;theoretical analysis and experimental comparison of graph matching algorithms for database filtering;a comparison of three maximum common subgraph algorithms on a large database of labeled graphs;swap strategies for graph matching;graph matching using spectral seriation and string edit distance;graph polynomials, principal pivoting, and maximum independent sets;graph partition for matching;spectral clustering of graphs;comparison of distance measures for graph-based clustering of documents;some experiments on clustering a set of strings;a new median graph algorithm;graph clustering using the weighted minimum common supergraph;ACM attributed graph clustering for learning classes of images and a competitive winner-takes-all architecture for classification and patternrecognition of structures.
Log-polar images have been being used for patternrecognition and active vision tasks for some years. these images are obtained either from true retina-like sensors or from conventional cartesian images by software co...
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We measure the sharpness of natural (complex) images using Gaussian models. We first locate lines and edges in the image. We apply Gaussian derivatives at different scales to the lines and edges. this yields a respons...
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