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교통신호제어기 유지보수를 위한 수명예측
  • 김은영 ;
  • 이기한 ;
  • 장중순 ;
  • 박상철
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dc.contributor.author김은영-
dc.contributor.author이기한-
dc.contributor.author장중순-
dc.contributor.author박상철-
dc.date.issued2018-09-
dc.identifier.issn1598-2475-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37301-
dc.identifier.urihttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002393192-
dc.description.abstractThis paper proposes a methodology for the effective maintenance planning of traffic signal controllers, which are important for the safety of vehicles and pedestrians. For the effective maintenance planning, it is necessary to estimate the lifespan of a traffic signal controller. Since a traffic signal controller operates outdoors, the lifespan depends on the environmental factors including temperature, humidity, and salinity. <br>We devise a deep learning model to estimate the lifespan of a traffic signal controller by using environmental conditions. The proposed maintenance planning methodology computes the optimal maintenance period which provides the lowest maintenance cost.-
dc.language.isoKor-
dc.publisher한국설비안전학회-
dc.title교통신호제어기 유지보수를 위한 수명예측-
dc.title.alternativeLifespan Estimation for the Maintenance of Traffic Signal Controllers-
dc.typeArticle-
dc.citation.endPage32-
dc.citation.number3-
dc.citation.startPage27-
dc.citation.title한국설비안전학회지-
dc.citation.volume23-
dc.identifier.bibliographicCitation한국설비안전학회지, Vol.23 No.3, pp.27-32-
dc.subject.keywordLifespan Estimation-
dc.subject.keywordMaintenance Planning-
dc.subject.keywordDeep Learning-
dc.subject.keywordTraffic Signal Controller-
dc.type.otherArticle-
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Park, SangChul박상철
Department of Industrial Engineering
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