{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:10:14Z","timestamp":1773961814068,"version":"3.50.1"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of Republic of Korea","doi-asserted-by":"publisher","award":["2022202090010A"],"award-info":[{"award-number":["2022202090010A"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3670058","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T20:53:40Z","timestamp":1772571220000},"page":"40114-40136","source":"Crossref","is-referenced-by-count":0,"title":["AcousticMoEKAN: Sparse Mixture-of-KAN Experts for Time\u2013Frequency Forecasting of OLTC Acoustic Signals"],"prefix":"10.1109","volume":"14","author":[{"given":"Donghyun","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Medical Artificial Intelligent, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-4434","authenticated-orcid":false,"given":"Jimyung","family":"Kang","sequence":"additional","affiliation":[{"name":"Korea Electrotechnology Research Institute, Ansan-si, Gyeonggi-do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7566-1137","authenticated-orcid":false,"given":"Yeji","family":"Hyun","sequence":"additional","affiliation":[{"name":"MICS Platforms, Yongin-si, Gyeonggi-do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6090-9537","authenticated-orcid":false,"given":"Hochul","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiological Science, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Enhancing data center low-voltage ride-through","author":"Xie","year":"2025","journal-title":"arXiv:2510.03867"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2004.837828"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/en15176435"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923809"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/en18195079"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2825219"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.prime.2022.100087"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1186\/s43067-023-00115-z"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/vibration7040051"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/s23167020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/en17010220"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/s24247960"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3550097"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/en17071665"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/math13111724"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12020409"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e40351"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3016888"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/s24227319"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1177\/1687814021996915"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/s20113095"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3036769"},{"key":"ref23","first-page":"1","article-title":"From noise to knowledge: A comparative study of acoustic anomaly detection models in pumped-storage hydropower plants","volume-title":"Proc. ACM IoT","author":"Khamaisi"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909756"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2024.054886"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/app142210404"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2928883"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/en18164269"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3578248"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/app14188229"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977885"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/s24227316"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012521"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935117"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/machines13020125"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3390\/app15041977"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2015.08.045"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM52003.2023.10252313"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3390\/machines11050531"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref41","article-title":"Revisiting long-term time series forecasting: An investigation on linear mapping","author":"Li","year":"2023","journal-title":"arXiv:2305.10721"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"ref43","article-title":"TimeGNN: Temporal dynamic graph learning for time series forecasting","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Dai"},{"key":"ref44","article-title":"Time series modeling and forecasting with sample convolution and interaction","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref45","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wu"},{"key":"ref46","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Nie"},{"key":"ref47","article-title":"iTransformer: Inverted transformers are effective for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref48","article-title":"KAN: Kolmogorov\u2013Arnold networks","author":"Liu","year":"2024","journal-title":"arXiv:2404.19756"},{"key":"ref49","article-title":"Are KANs effective for multivariate time series forecasting?","author":"Han","year":"2024","journal-title":"arXiv:2408.11306"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/s25237287"},{"key":"ref51","article-title":"MoBA: Mixture of block attention for long-context LLMs","author":"Lu","year":"2025","journal-title":"arXiv:2502.13189"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11418915.pdf?arnumber=11418915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:08:35Z","timestamp":1773950915000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11418915\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3670058","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}