DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Ki K. | ko |
dc.contributor.author | Yoon, Wan Chul | ko |
dc.date.accessioned | 2013-03-07T19:45:30Z | - |
dc.date.available | 2013-03-07T19:45:30Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008-03 | - |
dc.identifier.citation | INFORMATION SCIENCES, v.178, no.5, pp.1372 - 1390 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | http://hdl.handle.net/10203/91125 | - |
dc.description.abstract | Deflection yoke (DY) is one of the core components of a cathode ray tube (CRT) in a computer monitor or a television that determines the image quality. Once a DY anomaly is found from beam patterns on a display in the production line of CRTs, the remedy process should be performed through three steps: identifying misconvergence types from the anomalous display pattern.. adjusting manufacturing process parameters, and fine tuning. This study focuses on discovering a classifier for the identification of DY misconvergence patterns by applying a coevolutionary classification method. The DY misconvergence classification problems may be decomposed into two subproblems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two subproblems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by using a genetic algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with real-field data acquired from the production line of a computer monitor manufacturer in Korea, showing superior performance to other methods such as k-nearest neighbors, decision trees, and neural networks. (C) 2007 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | FEATURE-SELECTION | - |
dc.subject | ELLIPSOIDAL REGIONS | - |
dc.subject | OPTIMIZATION | - |
dc.title | A classifier learning system using a coevolution method for deflection yoke misconvergence pattern classification problem | - |
dc.type | Article | - |
dc.identifier.wosid | 000253032200009 | - |
dc.identifier.scopusid | 2-s2.0-37449005762 | - |
dc.type.rims | ART | - |
dc.citation.volume | 178 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 1372 | - |
dc.citation.endingpage | 1390 | - |
dc.citation.publicationname | INFORMATION SCIENCES | - |
dc.identifier.doi | 10.1016/j.ins.2007.10.018 | - |
dc.contributor.localauthor | Yoon, Wan Chul | - |
dc.contributor.nonIdAuthor | Lee, Ki K. | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | deflection yoke | - |
dc.subject.keywordAuthor | pattern classification | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | ELLIPSOIDAL REGIONS | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
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