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Cited 4 time in webofscience Cited 4 time in scopus
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dc.contributor.authorLee, H-
dc.contributor.authorJun, CH-
dc.contributor.authorSong, J-
dc.contributor.authorHong, JW-
dc.date.accessioned2016-04-01T01:37:22Z-
dc.date.available2016-04-01T01:37:22Z-
dc.date.created2009-02-28-
dc.date.issued2007-05-15-
dc.identifier.issn0169-7439-
dc.identifier.other2007-OAK-0000006936-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/23342-
dc.description.abstractSeveral iron-zinc phases are usually present in a galvannealed steel sheet. Since manufacturers require the identification of phases for the most suitable coatings, the quantitative determination of phase fractions is necessary. There are some qualitative and quantitative methods for phase analysis, but the quantitative procedure of obtaining phase fractions is not available. By using the independent component analysis applied to the X-ray diffraction spectra of galvannealed steel sheets, the present phases were identified by extracting a pure spectrum of each phase. Furthermore, a procedure for estimating phase fractions was proposed, which is based on the regression model subject to the non-negativity constraint on coefficients. Simulation experiments were carried out to demonstrate the performance of the proposed approach. (C) 2006 Elsevier B.V. All rights reserved.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.relation.isPartOfCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS-
dc.subjectindependent component analysis-
dc.subjectnon-negative least squares-
dc.subjectpure components-
dc.subjectX-ray diffraction spectrum-
dc.subjectBLIND SEPARATION-
dc.subjectMIXTURES-
dc.subjectSPECTRA-
dc.subjectSET-
dc.titleThe estimation of phase fractions in a galvannealed steel sheet using independent component analysis-
dc.typeArticle-
dc.contributor.college산업경영공학과-
dc.identifier.doi10.1016/j.chemolab.2006.10.001-
dc.author.googleLee, H-
dc.author.googleJun, CH-
dc.author.googleSong, J-
dc.author.googleHong, JW-
dc.relation.volume87-
dc.relation.issue1-
dc.relation.startpage81-
dc.relation.lastpage87-
dc.contributor.id10070938-
dc.relation.journalCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v.87, no.1, pp.81 - 87-
dc.identifier.wosid000247222000013-
dc.date.tcdate2019-01-01-
dc.citation.endPage87-
dc.citation.number1-
dc.citation.startPage81-
dc.citation.titleCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS-
dc.citation.volume87-
dc.contributor.affiliatedAuthorJun, CH-
dc.identifier.scopusid2-s2.0-34247641822-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc4-
dc.type.docTypeArticle-
dc.subject.keywordPlusBLIND SEPARATION-
dc.subject.keywordPlusSPECTRA-
dc.subject.keywordPlusSET-
dc.subject.keywordAuthorindependent component analysis-
dc.subject.keywordAuthornon-negative least squares-
dc.subject.keywordAuthorpure components-
dc.subject.keywordAuthorX-ray diffraction spectrum-
dc.relation.journalWebOfScienceCategoryAutomation & Control Systems-
dc.relation.journalWebOfScienceCategoryChemistry, Analytical-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryInstruments & Instrumentation-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAutomation & Control Systems-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaInstruments & Instrumentation-
dc.relation.journalResearchAreaMathematics-

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전치혁JUN, CHI HYUCK
Dept of Industrial & Management Enginrg
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