High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. this paper presents a practical patternrecognition based algorithm for electric distribution high impedance fault...
详细信息
High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. this paper presents a practical patternrecognition based algorithm for electric distribution high impedance fault detection. the scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with HIF the analysis using discrete wavelet transform (DWT) yields three phase voltage and current in the low frequency range which fed to a classifier for patternrecognition. the classifier is based on the algorithm using nearest neighbour rule approach. A HIF model was also developed, where the random nature of the arc was simulated using MATLAB. (c) 2006 Elsevier B.V. All rights reserved.
Log-Polar transform is mentioned as one of the models of the central-fovea visual sensor in a creature sight. Up to now, in the field of image recognition, Log-Polar transform is known widely and is especially used we...
详细信息
ISBN:
(纸本)0889865981
Log-Polar transform is mentioned as one of the models of the central-fovea visual sensor in a creature sight. Up to now, in the field of image recognition, Log-Polar transform is known widely and is especially used well in recognition of face images. However, since it is the recognition method eternal only to a scaling and a rotation, it mainly remains in application in a 2D space. In this paper, a new Log-Polar transform algorithm which can detect even affine transform parameters is proposed. By using this transform, the patternrecognition within 3D space is possible.
the high Reynolds number wake topology of a square prism is experimentally investigated using wavelet analysis. It is shown that a systematic application of one-dimensional continuous wavelet techniques, including a r...
详细信息
the high Reynolds number wake topology of a square prism is experimentally investigated using wavelet analysis. It is shown that a systematic application of one-dimensional continuous wavelet techniques, including a relatively new wavelet patternrecognition technique can reveal important three-dimensional features of the flow, assessing the validity of previously proposed wake topological models. Present results suggest that the high Reynolds number turbulent wakes are topologically similar, but not identical, to their laminar counterparts.
the proceedings contain 39 papers. the topics discussed include: vectorization and parity errors;a vectorization system for architecture engineering drawings;musings on symbol recognition;symbol spotting in technical ...
详细信息
ISBN:
(纸本)9783540347118
the proceedings contain 39 papers. the topics discussed include: vectorization and parity errors;a vectorization system for architecture engineering drawings;musings on symbol recognition;symbol spotting in technical drawings using vectorial signatures;an extended system for labeling graphical documents using statistical language models;symbol recognition combining vectorial and statistical features;segmentation and retrieval of ancient graphic documents;a method for 2D bar code recognition by using rectangle features to allocate vertexes;region-based pattern generation scheme for DMD based maskless lithography;global discrimination of graphic styles;recognition for ocular fundus based on shape of blood vessel;adaptive noise reduction for engineering drawings based on primitives and noise assessment;extraction of index components based on contents analysis of journal's scanned cover page;crosscheck of passport information for personal identification;string extraction based on statistical analysis method in color space;and interactive system for origami creation.
In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music recognition technology is not developed as succe...
详细信息
ISBN:
(纸本)0769525288
In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones.
the proceedings contain 53 papers. the topics discussed include: a study on human gaze detection based on 3D eye model;robust fake iris detection;a spatio-temporal metric for dynamic mesh comparison;facetoface: an iso...
详细信息
ISBN:
(纸本)354036031X
the proceedings contain 53 papers. the topics discussed include: a study on human gaze detection based on 3D eye model;robust fake iris detection;a spatio-temporal metric for dynamic mesh comparison;facetoface: an isometric model for facial animation;matching two-dimensional articulated shapes using generalized multidimensional scaling;further developments in geometrical algorithms for ear biometrics;composition of complex motion models from elementary human motions;acquisition of articulated human body models using multiple cameras;mobile path and spin 3D tracking and reconstruction;collision detection through deconstruction of articulated objects;learning deformations of human arm movement to adapt to environmental constraints;three-dimensional mapping from stereo images with geometrical rectification;boundary fragment matching and articulated pose under occlusion;and gender identification on the teeth based on principal component analysis representation.
Unsupervised segmenting region of interest in images is very useful in image indexing for content-based retrieval and target recognition. the proposed method is designed to separate the focused object from blurred for...
详细信息
ISBN:
(纸本)0889865981
Unsupervised segmenting region of interest in images is very useful in image indexing for content-based retrieval and target recognition. the proposed method is designed to separate the focused object from blurred foreground or background in low depth of field (DOF) images automatically. the proposed method is based on the shift of mean value in combination with detecting the edges using wavelet modulus maxima. Experiments testify that the focused object regions can be segmented withthe method acceptably.
the ability to detect or predict the structural class of a protein based on its primary sequence has been a major objective for researchers working in bioinformatics. Within the bioinformatics community, the prevailin...
详细信息
the ability to detect or predict the structural class of a protein based on its primary sequence has been a major objective for researchers working in bioinformatics. Within the bioinformatics community, the prevailing belief seems to be that support vector machines (SVMs) are the most effective solution for sequence-based structure prediction. the current state-of-the-art involves SVMs that employ kernel functions designed to compute the similarity between proteins based on profiles generated by the PSI-BLAST alignment algorithm. While effective for problems such as fold recognition or remote homology detection, these kernels are essentially a "black-box" solution to the structure prediction problem. they do not yield a representation that is independent of the SVM. this prevents the user from testing alternative classification algorithms or from using the features for other applications. For example, there may be instances where a researcher is interested in a compact representation of a protein sequence that can be used for problems such as range queries or nearest-neighbor retrieval. We describe such a representation in this work. Using the frequency scores returned by PSI-BLAST, we create a wavelet-based summary. this stand-alone, normalized feature vector drastically reduces the amount of information that needs to be stored for each protein. though our results are preliminary, empirically, we find that this representation performs well in both experiments dealing with fold recognition and provides accuracy comparable to the state-of-the-art for remote homology detection. At the same time, we find that it is also effective for protein indexing and retrieval.
the nearest-neighbor (NN) classifier has long been used in patternrecognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results withthis technique is the choice of...
详细信息
ISBN:
(纸本)9780769527017
the nearest-neighbor (NN) classifier has long been used in patternrecognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results withthis technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper a new ensemble technique is proposed to improve the performance of NN classifier the proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). these feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). the proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI Machine Learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).
Privacy-Preserving Data Mining is an important area that studies privacy issues of data mining. When the goal is to share data mining results, two privacy-related problems may arise. the first one is how to compute th...
详细信息
ISBN:
(纸本)9780769527017
Privacy-Preserving Data Mining is an important area that studies privacy issues of data mining. When the goal is to share data mining results, two privacy-related problems may arise. the first one is how to compute the data-mining results among several parties without sharing the data. Cryptography-based primitives are the basic tool used to develop ad-hoc secure multi-party computation protocols that share information as less as possible during the computation under different adversary models. the second one is how to produce data mining results that provably do not contain threats to the anonymity of individuals. the concept of k-anonymity has been used to discover anonymity-preserving frequent patterns, and centralized algorithms have been developed. In this paper and for the first time, we study how to produce anonymity-preserving data mining results in a distributed environment. We present two privacy-preserving strategies and show their feasibility through experimental analysis.
暂无评论