{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T04:04:20Z","timestamp":1768622660311,"version":"3.49.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571252"],"award-info":[{"award-number":["61571252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1109\/tnnls.2019.2905082","type":"journal-article","created":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T21:15:02Z","timestamp":1581023702000},"page":"475-487","source":"Crossref","is-referenced-by-count":37,"title":["Flow Adversarial Networks: Flowrate Prediction for Gas\u2013Liquid Multiphase Flows Across Different Domains"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2441-6057","authenticated-orcid":false,"given":"Delin","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-4454","authenticated-orcid":false,"given":"Jinku","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7398-0431","authenticated-orcid":false,"given":"Yinyan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8855-4520","authenticated-orcid":false,"given":"Yi","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref38","first-page":"971","article-title":"Self-normalizing neural networks","author":"klambauer","year":"2017","journal-title":"Proc 30th Adv Neural Inf Process Syst"},{"key":"ref33","author":"ganin","year":"2015","journal-title":"Unsupervised Domain Adaptation by Backpropagation"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref31","author":"qi","year":"2017","journal-title":"Loss-sensitive generative adversarial networks on lipschitz densities"},{"key":"ref30","author":"berthelot","year":"2017","journal-title":"BEGAN Boundary Equilibrium Generative Adversarial Networks"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"ref36","year":"2003"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref34","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmultiphaseflow.2014.01.009"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/S0301-9322(00)00022-7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/19\/1\/015401"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmultiphaseflow.2014.08.012"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2045934"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2013.2280485"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmultiphaseflow.2016.08.004"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmultiphaseflow.2015.12.010"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1959.sp006308"},{"key":"ref28","author":"gulrajani","year":"2017","journal-title":"Improved training of wasserstein gans"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/0301-9322(82)90071-4"},{"key":"ref27","author":"arjovsky","year":"2017","journal-title":"Wasserstein GAN"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/24\/1\/012003"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.expthermflusci.2015.03.020"},{"key":"ref29","author":"radford","year":"2015","journal-title":"Unsupervised Representation learning with deep convolutional generative adversarial networks CoRR"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.flowmeasinst.2013.07.014"},{"key":"ref8","year":"2012"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIM.2011.2117190","article-title":"Wet-gas flow modeling for the straight section of throat-extended venturi meter","volume":"60","author":"xu","year":"2011","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref2","year":"2015"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.01.011"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/8\/7\/001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref22","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref21","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc 25th Adv Neural Inf Process Syst (NIPS)"},{"key":"ref24","author":"he","year":"2016","journal-title":"Identity mappings in deep residual networks"},{"key":"ref23","author":"szegedy","year":"2014","journal-title":"Going Deeper with Convolutions"},{"key":"ref26","author":"arjovsky","year":"2017","journal-title":"Towards Principled Methods for Training Generative Adversarial Networks"},{"key":"ref25","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst 27"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/8984609\/08685789.pdf?arnumber=8685789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:19:47Z","timestamp":1651079987000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8685789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2]]},"references-count":38,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2019.2905082","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2]]}}}