{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:05:45Z","timestamp":1763535945640,"version":"3.28.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"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":[[2021,10,18]]},"DOI":"10.1109\/isgteurope52324.2021.9640006","type":"proceedings-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T16:09:40Z","timestamp":1640102980000},"page":"01-06","source":"Crossref","is-referenced-by-count":2,"title":["Fault Classification in Transmission Network with Semi-supervised Learning Method"],"prefix":"10.1109","author":[{"given":"Siyan","family":"Li","sequence":"first","affiliation":[]},{"given":"Yuhang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Robert C.","family":"Qiu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","author":"sajjadi","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref12","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref13","article-title":"Unsupervised data augmentation for consistency training","author":"xie","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref14","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.net.2014.12.005","article-title":"Semisupervised classification for fault diagnosis in nuclear power plants","volume":"47","author":"jiang","year":"2015","journal-title":"Nucl Eng Technol"},{"key":"ref16","first-page":"1013","article-title":"A kind of semi-supervised classifying method research for power transformer fault diagnosis","author":"chen","year":"0","journal-title":"2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2571680"},{"key":"ref18","article-title":"Validation of the Ornstein-Uhlenbeck process for load modeling based on PMU measurements","author":"roberts","year":"0","journal-title":"Power Systems Computation Conference"},{"key":"ref19","article-title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"berthelot","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref4","article-title":"Fault detection and classification based on co-training of semisupervised machine learning","author":"abdelgayed","year":"2017","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2913006"},{"key":"ref6","article-title":"Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"berthelot","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref8","article-title":"Pseudo-Label: The simple and efficient semi-supervised learning method for deep neural networks","author":"lee","year":"0","journal-title":"Workshop on Challenges in Representation Learning"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540120"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2303086"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2721922"},{"key":"ref20","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"}],"event":{"name":"2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)","start":{"date-parts":[[2021,10,18]]},"location":"Espoo, Finland","end":{"date-parts":[[2021,10,21]]}},"container-title":["2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9639895\/9639896\/09640006.pdf?arnumber=9640006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:54:55Z","timestamp":1652187295000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9640006\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/isgteurope52324.2021.9640006","relation":{},"subject":[],"published":{"date-parts":[[2021,10,18]]}}}