Multidisciplinary Inverse Design using Deep Learning: a Case Study of Brake Systems

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dc.contributor.authorKim Seong-shinko
dc.contributor.authorJwa, Min-youngko
dc.contributor.authorLee, Sko
dc.contributor.authorPark, Sko
dc.contributor.authorKang, Namwooko
dc.date.accessioned2021-12-09T06:54:43Z-
dc.date.available2021-12-09T06:54:43Z-
dc.date.created2021-12-06-
dc.date.issued2021-09-08-
dc.identifier.citationAsia Pacific Conference of the Prognostics and Health Management Society (PHMAP2021)-
dc.identifier.urihttp://hdl.handle.net/10203/290364-
dc.languageEnglish-
dc.publisherKSPHM (Korean Society for Prognostics and Health Management) and PHM Society-
dc.titleMultidisciplinary Inverse Design using Deep Learning: a Case Study of Brake Systems-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameAsia Pacific Conference of the Prognostics and Health Management Society (PHMAP2021)-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationRamada Plaza Hotel Jeju & Online-
dc.contributor.localauthorKang, Namwoo-
dc.contributor.nonIdAuthorKim Seong-shin-
dc.contributor.nonIdAuthorJwa, Min-young-
dc.contributor.nonIdAuthorLee, S-
dc.contributor.nonIdAuthorPark, S-
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GT-Conference Papers(학술회의논문)
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