{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:31:33Z","timestamp":1771525893398,"version":"3.50.1"},"reference-count":38,"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\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M662399"],"award-info":[{"award-number":["2019M662399"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Applied Research Project for Postdoctoral Researchers in Qingdao","award":["01020240604"],"award-info":[{"award-number":["01020240604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2987933","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T20:59:11Z","timestamp":1586897951000},"page":"71475-71485","source":"Crossref","is-referenced-by-count":40,"title":["A Novel Transfer Learning Method for Fault Diagnosis Using Maximum Classifier Discrepancy With Marginal Probability Distribution Adaptation"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-1045","authenticated-orcid":false,"given":"Sixiang","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8690-0672","authenticated-orcid":false,"given":"Jinrui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7367-6253","authenticated-orcid":false,"given":"Baokun","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-2995","authenticated-orcid":false,"given":"Guowei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3819-2015","authenticated-orcid":false,"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8796-8185","authenticated-orcid":false,"given":"Jingtao","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref32","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","article-title":"Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis","author":"cheng","year":"2019","journal-title":"arXiv 1903 06753"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.08.099"},{"key":"ref37","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"key":"ref36","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02925-8_18"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3901\/JME.2018.05.094"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2731945"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2766235"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.06.012"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2917604"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2017.2669947"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2017.2661250"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.049"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.07.032"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.03.025"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2754287"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2019.05.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890566"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907997"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2012.07.001"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.04.010"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044445"},{"key":"ref8","article-title":"ENet: A deep neural network architecture for real-time semantic segmentation","author":"paszke","year":"2016","journal-title":"ArXiv 1606 02147"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2900296"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2900503"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2017.19.AVM-023"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929094"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2951409"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905264"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2720965"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ab2296"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09066838.pdf?arnumber=9066838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:51:24Z","timestamp":1639770684000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9066838\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2987933","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}