{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:52:51Z","timestamp":1772232771549,"version":"3.50.1"},"reference-count":32,"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51977082"],"award-info":[{"award-number":["51977082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Project of Qingyuan Power Supply Bureau, Guangdong Power Supply Company Ltd","award":["GDKJXM20183511"],"award-info":[{"award-number":["GDKJXM20183511"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFE0208400"],"award-info":[{"award-number":["2018YFE0208400"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201906150017"],"award-info":[{"award-number":["201906150017"]}],"id":[{"id":"10.13039\/501100004543","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.2020.3011689","type":"journal-article","created":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T20:27:57Z","timestamp":1595622477000},"page":"136487-136497","source":"Crossref","is-referenced-by-count":8,"title":["Electrical Equipment Identification Method With Synthetic Data Using Edge-Oriented Generative Adversarial Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4859-1511","authenticated-orcid":false,"given":"Zhewen","family":"Niu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4783-0717","authenticated-orcid":false,"given":"Marek Z.","family":"Reformat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1823-2355","authenticated-orcid":false,"given":"Wenhu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9653-3316","authenticated-orcid":false,"given":"Baining","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-41822-8_33"},{"key":"ref31","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2691013"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.03.025"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2016.2618756"},{"key":"ref12","article-title":"Data augmentation in emotion classification using generative adversarial networks","author":"zhu","year":"2017","journal-title":"arXiv 1711 00648"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.10.109"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"ref16","first-page":"2672","article-title":"Generative adversarial nets","volume":"3","author":"goodfellow","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.01.001"},{"key":"ref18","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref19","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","first-page":"2","article-title":"Conditional generative adversarial nets for convolutional face generation","volume":"2014","author":"gauthier","year":"2014","journal-title":"Class Project Stanford CS231N Convolutional Neural Netw Vis Recognit Winter Semester"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2015.05.010"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ITOEC.2017.8122336"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TDEI.2015.004741"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TDEI.2016.7736846"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2015.08.019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2859048"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916461"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TDEI.2015.004696"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2757030"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2013.03.006"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2924003"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2885365"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890693"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2917604"},{"key":"ref26","first-page":"4467","article-title":"The weld image edge-detection algorithm combined with Canny operator and mathematical morphology","author":"lu","year":"2013","journal-title":"Proc 32nd Chin Control Conf"},{"key":"ref25","article-title":"Segmenting objects in day and night: Edge-conditioned CNN for thermal image semantic segmentation","author":"li","year":"2019","journal-title":"arXiv 1907 10303"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09146829.pdf?arnumber=9146829","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:57:37Z","timestamp":1642003057000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9146829\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3011689","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}