Random-forest-based real-time contrasts control chart using adaptive breakpoints with symbolic aggregate approximation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, In-seok | - |
dc.contributor.author | Park, Seung Hwan | - |
dc.contributor.author | Baek, Jun-Geol | - |
dc.date.accessioned | 2021-08-30T08:41:14Z | - |
dc.date.available | 2021-08-30T08:41:14Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-11-15 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/51500 | - |
dc.description.abstract | For high yield management, process monitoring has become an increasingly important task. The real-time contrasts (RTC) control chart uses the real-time classification method for process monitoring and outperforms the existing real-time control chart. The original RTC control chart identifies the cause of faults using a random forest classifier. However, the random forest provides discrete monitoring statistics that could make the overall performance less efficient. To improve the performance of the RTC control chart, we propose a random-forest-based RTC control chart that uses adaptive breakpoints with symbolic aggregate approximation (ABP-SAX). The monitoring statistics of the RTC control chart indicate the process condition, and the quality of the monitoring statistics is determined by the classification performance of the classifier. Therefore, to improve the classification performance of individual decision trees, we proposed ABP-SAX. The original SAX causes time-information loss and distortions in the data slope and pattern. We prevent these problems using the mean squared error to minimize the difference between the represented and original data. After the applied ABP-SAX representation, the raw data are represented by categorical values that preserve information from the original data, and the represented data improve the performance of the RTC control chart. Therefore, the proposed RTC control chart could detect shifts more quickly and identify the cause of the faults. Our improvements can contribute to high yield management and quick response to abnormalities. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | STATISTICAL PROCESS-CONTROL | - |
dc.subject | DIMENSIONALITY REDUCTION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | SAX | - |
dc.title | Random-forest-based real-time contrasts control chart using adaptive breakpoints with symbolic aggregate approximation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baek, Jun-Geol | - |
dc.identifier.doi | 10.1016/j.eswa.2020.113407 | - |
dc.identifier.scopusid | 2-s2.0-85084950926 | - |
dc.identifier.wosid | 000571732700012 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.158 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 158 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | STATISTICAL PROCESS-CONTROL | - |
dc.subject.keywordPlus | DIMENSIONALITY REDUCTION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SAX | - |
dc.subject.keywordAuthor | Real-time contrasts (RTC) | - |
dc.subject.keywordAuthor | Control chart | - |
dc.subject.keywordAuthor | Process monitoring | - |
dc.subject.keywordAuthor | Symbolic aggregate approximation (SAX) | - |
dc.subject.keywordAuthor | Adaptive breakpoints-SAX (ABP-SAX) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.