• Title, Summary, Keyword: pattern classification

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OptiNeural System for Optical Pattern Classification

  • Kim, Myung-Soo
    • Journal of Electrical Engineering and information Science
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    • v.3 no.3
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    • pp.342-347
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    • 1998
  • An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

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Pattern Classification of Load Demand for Distribution Transformer (배전용 변압기 부하사용 패턴분류)

  • Yun, Sang-Yun;Kim, Jae-Chul;Lee, Young-Suk
    • Proceedings of the KIEE Conference
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    • pp.89-91
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    • 2001
  • This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.

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A Study on Efficient Classification of Pattern Using Object Oriented Relationship between Design Patterns

  • Kim Gui-Jung;Han Jung-Soo
    • International Journal of Contents
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    • v.2 no.3
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    • pp.11-17
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    • 2006
  • The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

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Feature Impact Evaluation Based Pattern Classification System

  • Rhee, Hyun-Sook
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.11
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    • pp.25-30
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    • 2018
  • Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

Multiple SVM Classifier for Pattern Classification in Data Mining (데이터 마이닝에서 패턴 분류를 위한 다중 SVM 분류기)

  • Kim Man-Sun;Lee Sang-Yong
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.3
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    • pp.289-293
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    • 2005
  • Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

A Study on a Pattern Classification of HDD (Hard Disk Drive) Defect Distribution (HDD (Hard Disk Drive) 결함 분포의 패턴 분류에 관한 연구)

  • Kwon, Hyun-Tae;Moon, Un-Chul;Lee, Seung-Chul
    • Proceedings of the KIEE Conference
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    • pp.2846-2848
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    • 2005
  • This paper proposes a pattern classification algorithm for the defect distribution of Hard Disk Drive (HDD). In the HDD productions, the defect pattern of defective HDD set is important information to diagnosis of defective HDD set. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A fuzzy inference system is proposed, the inputs of which are 5 characteristic values and the outputs are the possibilities that the input pattern is classified to the standard patterns. Classification result is the pattern with maximum possibility. The proposed algorithm is implemented with a PC system for defective HDD sets and shows its effectiveness.

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Availability Verification of Feature Variables for Pattern Classification on Weld Flaws (용접결함의 패턴분류를 위한 특징변수 유효성 검증)

  • Kim, Chang-Hyun;Kim, Jae-Yeol;Yu, Hong-Yeon;Hong, Sung-Hoon
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.16 no.6
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    • pp.62-70
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    • 2007
  • In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

Efficient two-step pattern matching method for off-line recognition of handwritten Hangul (필기체 한글의 오프라인 인식을 위한 효과적인 두 단계 패턴 정합 방법)

  • 박정선;이성환
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.4
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    • pp.1-8
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    • 1994
  • In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

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Design Classification and Development of Pattern Searching Algorithm Based on Pattern Design Elements - With focus on Automatic Pattern Design System for Baseball Uniforms Manufactured under Custom-MTM System - (패턴설계요소기반의 디자인 분류 및 패턴탐색 알고리즘개발 - 맞춤양산형 야구복 자동패턴 설계시스템을 위한 -)

  • Kang, In-Ae;Choi, Kueng-Mi;Jun, Jung-Ill
    • Fashion & Textile Research Journal
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    • v.13 no.5
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    • pp.734-742
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    • 2011
  • This study has been undertaken as a basic research for automatic pattern design for baseball uniforms manufactured under custom-MTM system, propose building up of a system whereby various partial patterns are combined under an automatic design system and develop a multi-combination type pattern searching algorithm which allows development of a various designs. As a result of this, type classification based on pattern design elements includes side, open, collar, facing and panel type. Design have been divided into coarse classification ranging from level 1 to 7 according to pattern design elements, based on a design distribution chart. Out of 7 such levels, 3 major types determining design which are, more specifically, level 1 sleeve type, level 2 open type and level 3 collar type, have been taken and combined to determine a total of 12 types to be used for design classification codes. Respective name of style and patterns have been coded using alphabet and numerals. Totally, pattern searching algorithm of multi-combination type has been developed whereby combination of patterns belonging to a specific style can be retrieved automatically once that style name is designated on the automatic pattern design system.