{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T14:15:13Z","timestamp":1777472113971,"version":"3.51.4"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Research on Intelligent Ship Testing and Verification","award":["2018\/473"],"award-info":[{"award-number":["2018\/473"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51479017"],"award-info":[{"award-number":["51479017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3037251","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T21:36:39Z","timestamp":1605044199000},"page":"206719-206733","source":"Crossref","is-referenced-by-count":8,"title":["Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8349-9094","authenticated-orcid":false,"given":"Hui","family":"Cao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-2659","authenticated-orcid":false,"given":"Jundong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yiru","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"39","article-title":"Gas turbine fault diagnosis based on improved support vector machine","volume":"33","author":"qiu","year":"2018","journal-title":"J Eng for Thermal Energy and Power"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/computers7040069"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2954899"},{"key":"ref36","first-page":"1792","article-title":"Comparison of multi-class support vector machines","volume":"32","author":"xue","year":"2011","journal-title":"Eng Comput"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/72.991427"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/BIC-TA.2011.51"},{"key":"ref10","first-page":"105","article-title":"Ship machinery condition monitoring using performance data through supervised learning","author":"gkerekos","year":"2017","journal-title":"Proc Smart Ship Tech Conf"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.10.015"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2017.09.014"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1177\/1475090214540874"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2017.12.002"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.04.015"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/17445302.2018.1443694"},{"key":"ref17","first-page":"140","article-title":"Design of intelligent diagnosis system for ship power equipment","volume":"13","author":"zhang","year":"2018","journal-title":"Chinese Journal of Ship Research"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2019.106220"},{"key":"ref19","first-page":"48","article-title":"Realtime power quality evaluation system of the electric propulsion ship based on AHP-fuzzy comprehensive evaluation method","volume":"14","author":"zhang","year":"2019","journal-title":"Chinese Journal of Ship Research"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.09.308"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1080\/20464177.2004.11020175"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010313"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S1359-4311(00)00006-5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2009.10.031"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2987865"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00502-009-0639-z"},{"key":"ref8","first-page":"969","article-title":"Dynamic risk and reliability assessment of ship machinery and equipment","volume":"2016","author":"dikis","year":"0","journal-title":"Proc 26th Int Ocean Polar Eng Conf Rhodes Greece"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-05173-9_23"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/17445302.2018.1500189"},{"key":"ref9","first-page":"103","article-title":"Ship machinery condition monitoring using vibration data through supervised learning","author":"gkerekos","year":"2016","journal-title":"Proc Int Conf Maritime Saf Oper"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/17445302.2010.480899"},{"key":"ref20","first-page":"69","article-title":"Main marine engine fault diagnosis method based on rough set theory and optimized DAG-SVM","volume":"15","author":"liu","year":"2020","journal-title":"Chinese Journal of Ship Research"},{"key":"ref22","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","author":"kohavi","year":"1995","journal-title":"Proc 14th Int Joint Conf Artif Intell"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2006.12.007"},{"key":"ref24","article-title":"An idea based on honey bee swarm for numerical optimization","author":"karaboga","year":"2005"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2020.122781"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3001299"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908662"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09253367.pdf?arnumber=9253367","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:57:01Z","timestamp":1642003021000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9253367\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3037251","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}