Article(id=1242138728865538435, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242138709710156485, articleNumber=null, orderNo=13, doi=10.3981/j.issn.1000-7857.2020.21.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1539532800000, receivedDateStr=2018-10-15, revisedDate=1548345600000, revisedDateStr=2019-01-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1605595274917, onlineDateStr=2020-11-17, pubDate=1605196800000, pubDateStr=2020-11-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1605595274917, onlineIssueDateStr=2020-11-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078550780, creator=sys-migrate, updateTime=1774078550780, updator=sys-migrate, issue=Issue{id=1242138709710156485, tenantId=1146029695717560320, journalId=1146031591421210625, year='2020', volume='38', issue='21', pageStart='1', pageEnd='196', issueExtLink='null', onlineDate='null', pubDate='1605196800000', pubDateStr='2020-11-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078546212, creator='sys-migrate', updateTime=1774078546212, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=91, endPage=95, ext={EN=ArticleExt(id=1242138731210158901, articleId=1242138728865538435, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Classification method of ship navigation condition based on clustering analysis, columnId=1242138716576232151, journalTitle=Science & Technology Review, columnName=Exclusive: System Engineering, runingTitle=null, highlight=null, articleAbstract=With the development trend of intelligent ship and shipping as well as accumulation of ship big data, it is urgent to build a special model of navigation economic analysis through data-driven means to solve the problem of energy consumption evaluation and optimization and maximize ship energy efficiency. In this paper, combined with the relationship between ship's sailing conditions and main engine's fuel consumption characteristics, and considering the factors of draft and relative wind speed, K-means clustering analysis method is used to realize the division of different sailing conditions of the influencing factors of fuel consumption. The historical data of a VLCC are used to verify the actual application. Based on the data of ship's voyage loading and external environment weather conditions, the classification analysis of navigation conditions and the determination of influencing factor parameter interval under each working condition are realized, which provides a more refined analysis basis for the construction of matching model of marine main engine fuel consumption by different working conditions., authors=TAN Xiao, GUAGN Wenyuan, LI Han, LI Yongjie, XUE Chen, authorsList=TAN Xiao, GUAGN Wenyuan, LI Han, LI Yongjie, XUE Chen, authorCompany=Systems Engineering Research Institute, China State Shipbuilding Corporation Limited, Beijing 100036, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=yC2bl8nsd1pDWGIOBHHysw==, pdfFileSize=1039385, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242138730153194278, articleId=1242138728865538435, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=一种基于聚类分析的船舶航行工况划分方法, columnId=1242137113915891796, journalTitle=科技导报, columnName=专题：体系工程, runingTitle=null, highlight=null, articleAbstract=结合船舶航行工况与主机燃油消耗特性的关系，考虑吃水和相对风速影响因素，采用K-means聚类分析方法，实现油耗影响因素的不同航行工况的划分。以某超大型油轮为例，基于船舶航行装载与外部环境天气状态数据，实现了航行工况分类分析以及各工况下影响因素参数区间的确定，为船舶主机燃油消耗分工况匹配模型构建提供了更加精细化的分析基础。, authors=谭笑, 关文渊, 李晗, 李永杰, 薛晨, authorsList=谭笑, 关文渊, 李晗, 李永杰, 薛晨, authorCompany=中国船舶工业系统工程研究院, 北京 100036, correspAuthors=null, authorNote=谭笑,工程师,研究方向为通用工业大数据分析,电子信箱:tanxiao2016cyn@163.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=iPsTu/z2l/tCJak47dBbng==, pdfFileSize=1039385, pdfExtLink=null, 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