Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

통계적 접근 방법을 이용한 저속비대선 및 컨테이너선의 동력 성능 추정

Full metadata record
DC Field Value Language
dc.contributor.author김유철-
dc.contributor.author김건도-
dc.contributor.author김명수-
dc.contributor.author황승현-
dc.contributor.author김광수-
dc.contributor.author연성모-
dc.contributor.author이영연-
dc.date.accessioned2023-12-22T10:30:21Z-
dc.date.available2023-12-22T10:30:21Z-
dc.date.issued2021-08-
dc.identifier.issn1225-1143-
dc.identifier.urihttps://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9639-
dc.description.abstractIn this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.-
dc.format.extent9-
dc.language한국어-
dc.language.isoKOR-
dc.publisher대한조선학회-
dc.title통계적 접근 방법을 이용한 저속비대선 및 컨테이너선의 동력 성능 추정-
dc.title.alternativePowering Performance Prediction of Low-Speed Full Ships and Container Carriers Using Statistical Approach-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.3744/SNAK.2021.58.4.234-
dc.identifier.bibliographicCitation대한조선학회 논문집, v.58, no.4, pp 234 - 242-
dc.citation.title대한조선학회 논문집-
dc.citation.volume58-
dc.citation.number4-
dc.citation.startPage234-
dc.citation.endPage242-
dc.identifier.kciidART002744449-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorPower prediction(동력 예측)-
dc.subject.keywordAuthorLinear regression(선형 회귀)-
dc.subject.keywordAuthorMachine learning(기계 학습)-
dc.subject.keywordAuthorHull form variables(선형 변수)-
dc.subject.keywordAuthorFullship(저속비대선)-
dc.subject.keywordAuthorContainer carrier(컨테이너선)-
Files in This Item
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yeon, Seong Mo photo

Yeon, Seong Mo
지능형선박연구본부
Read more

Altmetrics

Total Views & Downloads

BROWSE