{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:15:30Z","timestamp":1730294130219,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,17]]},"DOI":"10.1109\/safeprocess52771.2021.9693695","type":"proceedings-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T15:50:05Z","timestamp":1643730605000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Subspace-based domain adaptation for few-shot fault diagnosis"],"prefix":"10.1109","author":[{"given":"Ge","family":"Yu","sequence":"first","affiliation":[]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Siamese Neural Networks for One-shot Image Recognition","author":"koch","year":"2016","journal-title":"Vinyals Oriol & Blundell Charles & Lillicrap Timothy & Kavukcuoglu Koray & Wierstra Daan"},{"journal-title":"Matching Networks for One Shot Learning Snell Jake & Swersky Kevin & Zemel Richard","year":"2017","key":"ref11"},{"journal-title":"Prototypical Networks for Few-shot Learning Sung Flood & Yang Yongxin & Zhang Li & Xiang Tao & Torr Philip & Hospedales Timothy","year":"2018","key":"ref12"},{"journal-title":"Learning to compare Relation network for few-shot learning","first-page":"1199","year":"0","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2599532"},{"key":"ref17","first-page":"1548","article-title":"Graph regularized nonnegative matrix factorization for data representation[J]","volume":"33","author":"cai","year":"2014","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref18","first-page":"97c105","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc ICML"},{"journal-title":"Gaussian Mixture Models 10 1007\/978-1-4471-5779-32","year":"2015","author":"dong","key":"ref19"},{"key":"ref4","article-title":"Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation[J]","author":"li","year":"2018","journal-title":"Journal of Intelligent Manufacturing"},{"key":"ref3","article-title":"A General Transfer Framework based on Industrial Process Fault Diagnosis under Small Samples[J]","volume":"pp","author":"liu","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3053106"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107043"},{"key":"ref8","article-title":"Data Augmentation in Fault Diagnosis Based on the Wasserstein Generative Adversarial Network with Gradient Penalty[J]","author":"gao","year":"2019","journal-title":"Neurocomputing"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2019.01.001"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/S0967-0661(02)00064-3","article-title":"Fault Detection and Diagnosis in Engineering Systems[J]","volume":"10","author":"gertler","year":"2002","journal-title":"Control Engineering Practice"},{"journal-title":"IEEE conference on computer vision & pattern recognition","article-title":"Deep Residual Learning for Image Recognition[C]","year":"2016","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2933171"},{"key":"ref20","article-title":"Robustified expected maximum production frontiers","author":"daouia","year":"2020","journal-title":"Econometric Theory 37 1-42 10 1017\/S0266466620000171"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/504325"},{"journal-title":"Learning from Very Few Samples A Survey","year":"2020","author":"jiang","key":"ref21"},{"key":"ref24","first-page":"11711","author":"jamal","year":"2019","journal-title":"Task Agnostic Meta-Learning for Few-Shot Learning"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2828811"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13560-1_76"}],"event":{"name":"2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)","start":{"date-parts":[[2021,12,17]]},"location":"Chengdu, China","end":{"date-parts":[[2021,12,18]]}},"container-title":["2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9693491\/9693537\/09693695.pdf?arnumber=9693695","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:58:57Z","timestamp":1652187537000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693695\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,17]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/safeprocess52771.2021.9693695","relation":{},"subject":[],"published":{"date-parts":[[2021,12,17]]}}}