확률적 모델 학습을 통한 냉장고 내부 온도 모델 구현
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이동규 | - |
dc.contributor.author | 정재원 | - |
dc.date.accessioned | 2021-09-27T06:14:35Z | - |
dc.date.available | 2021-09-27T06:14:35Z | - |
dc.date.created | 2021-08-27 | - |
dc.date.issued | 2021-06-25 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/132987 | - |
dc.description.abstract | The purpose of this study was to get high level of performance in system identification of refrigerator. This research conducted several data-driven methods like as multi-layer perceptron (MLP) and long short-term memory (LSTM) of deep learning methods and equation based bayesian optimization and hyper-band. The real data was gathered by several experiments and established system identification resulting in accuracy of 0.27, 0.18 and 0.02 as LSTM, MLP and equation based bayesian optimization. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | (사)대한설비공학회 | - |
dc.title | 확률적 모델 학습을 통한 냉장고 내부 온도 모델 구현 | - |
dc.title.alternative | System Identification of Refrigerator with Statistical Model | - |
dc.type | Conference | - |
dc.contributor.affiliatedAuthor | 정재원 | - |
dc.identifier.bibliographicCitation | 대한설비공학회 2021 하계학술발표대회, pp.40 - 42 | - |
dc.relation.isPartOf | 대한설비공학회 2021 하계학술발표대회 | - |
dc.relation.isPartOf | 대한설비공학회 2021 하계학술발표대회 논문집 | - |
dc.citation.title | 대한설비공학회 2021 하계학술발표대회 | - |
dc.citation.startPage | 40 | - |
dc.citation.endPage | 42 | - |
dc.citation.conferencePlace | KO | - |
dc.citation.conferencePlace | 휘닉스 평창 | - |
dc.citation.conferenceDate | 2021-06-22 | - |
dc.type.rims | CONF | - |
dc.description.journalClass | 2 | - |
dc.identifier.url | https://www.auric.or.kr/User/Rdoc/DocRdoc.aspx?returnVal=RD_R&dn=407256#.YRxmQN9t-Ul | - |
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