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조경화

Cho, Kyung Hwa
Water-Environmental Informatics Lab.
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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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