Process control of time-varying systems using parameter-less self-organizing maps
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choung, Young Jae | - |
dc.contributor.author | Kang, Jihoon | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.date.accessioned | 2021-09-03T07:22:44Z | - |
dc.date.available | 2021-09-03T07:22:44Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-04 | - |
dc.identifier.issn | 0959-1524 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/83802 | - |
dc.description.abstract | Traditional control charts, such as Hotelling's T-2, are effective in detecting abnormal patterns. However, most control charts do not take into account a time-varying property in a process. In the present study, we propose a parameter-less self-organizing map-based control chart that can handle a situation in which changes occur in the distribution or parameter of the target observations. The control limits of the proposed chart are determined by estimating the empirical level of significance on the percentile using the bootstrap method. Experimental results obtained by using simulated data and actual process data from the manufacturing process for a thin-film transistor-liquid crystal display demonstrate the effectiveness and usefulness of the proposed algorithm. (C) 2017 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | MULTIVARIATE CONTROL CHARTS | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | PCA | - |
dc.subject | IDENTIFICATION | - |
dc.title | Process control of time-varying systems using parameter-less self-organizing maps | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1016/j.jprocont.2017.02.005 | - |
dc.identifier.scopusid | 2-s2.0-85012915687 | - |
dc.identifier.wosid | 000399849400005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF PROCESS CONTROL, v.52, pp.45 - 56 | - |
dc.relation.isPartOf | JOURNAL OF PROCESS CONTROL | - |
dc.citation.title | JOURNAL OF PROCESS CONTROL | - |
dc.citation.volume | 52 | - |
dc.citation.startPage | 45 | - |
dc.citation.endPage | 56 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | MULTIVARIATE CONTROL CHARTS | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | PCA | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordAuthor | Multivariate process control | - |
dc.subject.keywordAuthor | Control chart | - |
dc.subject.keywordAuthor | Self-organizing map | - |
dc.subject.keywordAuthor | Time-varying process | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Machine learning | - |
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