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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 493 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 481 | - |
dc.citation.title | 상하수도학회지 | - |
dc.citation.volume | 34 | - |
dc.contributor.author | 표종철 | - |
dc.contributor.author | 박상훈 | - |
dc.contributor.author | 조경화 | - |
dc.contributor.author | 백상수 | - |
dc.date.accessioned | 2023-12-21T16:37:32Z | - |
dc.date.available | 2023-12-21T16:37:32Z | - |
dc.date.created | 2021-04-15 | - |
dc.date.issued | 2020-12 | - |
dc.description.abstract | Deep learning models, which imitate the function of human brain, have drawn attention from many engineering fields (mechanical, agricultural, and computer engineering etc). The major advantages of deep learning in engineering fields can be summarized by objects detection, classification, and time-series prediction. As well, it has been applied into environmental science and engineering fields. Here, we compiled our previous attempts to apply deep learning models in water-environment field and presented the future opportunities. | - |
dc.identifier.bibliographicCitation | 상하수도학회지, v.34, no.6, pp.481 - 493 | - |
dc.identifier.issn | 1225-7672 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/52757 | - |
dc.language | 한국어 | - |
dc.publisher | 대한상하수도학회 | - |
dc.title.alternative | Deep learning model in water-environment field | - |
dc.title | 수 환경 분야에서의 딥러닝 모델 적용사례 | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.identifier.kciid | ART002654815 | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Multi-dimensional image | - |
dc.subject.keywordAuthor | Water-environment | - |
dc.subject.keywordAuthor | 합성곱 신경망 | - |
dc.subject.keywordAuthor | 딥러닝 | - |
dc.subject.keywordAuthor | 다차원 이미지 | - |
dc.subject.keywordAuthor | 수환경 | - |
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