{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:20:14Z","timestamp":1780521614654,"version":"3.54.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"A&#x002A;STAR Graduate Scholarship"},{"name":"New Chongqing Talent Program"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62573071"],"award-info":[{"award-number":["62573071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2024CDJYXTD007"],"award-info":[{"award-number":["2024CDJYXTD007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Princeton School of Engineering and Applied Science"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1109\/tii.2025.3613385","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T17:28:05Z","timestamp":1759512485000},"page":"543-554","source":"Crossref","is-referenced-by-count":3,"title":["Learning More With Less: A Generalizable, Self-Supervised Framework for Privacy-Preserving Capacity Estimation With EV Charging Data"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9915-0302","authenticated-orcid":false,"given":"Anushiya","family":"Arunan","sequence":"first","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-9022","authenticated-orcid":false,"given":"Yan","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5757-1379","authenticated-orcid":false,"given":"U-Xuan","family":"Tan","sequence":"additional","affiliation":[{"name":"Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-131X","authenticated-orcid":false,"given":"H. Vincent","family":"Poor","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9307-2120","authenticated-orcid":false,"given":"Chau","family":"Yuen","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3184398"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10669-023-09958-3"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2021.06.005"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.130773"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2022.3229350"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2025.3580823"},{"key":"ref7","article-title":"ITFormer: Bridging time series and natural language for multi-modal QA with large-scale multitask dataset","volume-title":"Proc. 42nd Int. Conf. Mach. Learn.","author":"Wang","year":"2025"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2021.100081"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109455"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3541819"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2024.3459027"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-59217-z"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.14763\/2024.3.1790"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3419988"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.104560"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-38458-w"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103275"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109602"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2951843"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2020.101836"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113381"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.125814"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2021.3117841"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.120954"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.etran.2024.100361"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JESTPE.2021.3112754"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120160"},{"key":"ref28","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","volume":"33","author":"Chen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3530702"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.122332"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2024.100405"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2023.10.710"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-98915-8"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jechem.2024.08.037"},{"key":"ref37","article-title":"Learning to embed time series patches independently","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lee","year":"2024"},{"key":"ref38","first-page":"29996","article-title":"SimMTM: A simple pre-training framework for masked time-series modeling","volume":"36","author":"Dong","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467401"},{"key":"ref40","article-title":"TSLANet: Rethinking transformers for time series representation learning","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Eldele","year":"2024"},{"key":"ref41","article-title":"Evbattery: A large-scale electric vehicle dataset for battery health and capacity estimation","author":"He","year":"2022"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN48710.2020.00-33"},{"key":"ref43","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Kendall","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00945"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414389"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9424\/11339358\/11192575.pdf?arnumber=11192575","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:05:36Z","timestamp":1768255536000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11192575\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":45,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tii.2025.3613385","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}