{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T20:55:55Z","timestamp":1775163355137,"version":"3.50.1"},"reference-count":45,"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:\/\/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":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Project of China","doi-asserted-by":"publisher","award":["2025YFE0204600"],"award-info":[{"award-number":["2025YFE0204600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62403432"],"award-info":[{"award-number":["62403432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20328"],"award-info":[{"award-number":["U23A20328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LR26F030005"],"award-info":[{"award-number":["LR26F030005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tase.2026.3677399","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:59:14Z","timestamp":1774468754000},"page":"7137-7147","source":"Crossref","is-referenced-by-count":0,"title":["Meta-AWARE: Meta-Learning-Based Automatic Weighted Augmentation for Regression Enhancement in Soft Sensor Applications"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7468-3559","authenticated-orcid":false,"given":"Yuting","family":"Lyu","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7852-1370","authenticated-orcid":false,"given":"Le","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China"}]},{"given":"Zhongfa","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shandong Taihe Technology Company Ltd., Zaozhuang, China"}]},{"given":"Yanfei","family":"Hua","sequence":"additional","affiliation":[{"name":"Shandong Taihe Technology Company Ltd., Zaozhuang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4098-6479","authenticated-orcid":false,"given":"Zhihuan","family":"Song","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2301773"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2803727"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2024.3417019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3053128"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3065377"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3253285"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3523560"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2025.3638130"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2026.3651217"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3261330"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3414488"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2025.3597838"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2024.3504736"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2025.3579694"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2025.3528580"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00727-2"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3438277"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124582"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3621125"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2021.07.033"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2024.108707"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3427765"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3079214"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3309629"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2023.3238555"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3366991"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3319677"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2021.104903"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2021.3139194"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/polym14214769"},{"key":"ref31","first-page":"4334","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ren"},{"key":"ref32","first-page":"1917","article-title":"Meta-weight-Net: Learning an explicit mapping for sample weighting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shu"},{"key":"ref33","article-title":"Quadratic direct forecast for training multi-step time-series forecast models","author":"Wang","year":"2025","journal-title":"arXiv:2511.00053"},{"key":"ref34","first-page":"1","article-title":"DistDF: Time-series forecasting needs joint-distribution Wasserstein alignment","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-007-0176-2"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref37","first-page":"1","article-title":"How much data are augmentations worth? An investigation into scaling laws, invariance, and implicit regularization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Geiping"},{"key":"ref38","first-page":"3927","article-title":"Improved precision and recall metric for assessing generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Kynk\u00e4\u00e4nniemi"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.2307\/1931034"},{"key":"ref40","first-page":"1","article-title":"Fidelity-weighted learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dehghani"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.4324\/9780203209332-7"},{"key":"ref42","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17324"},{"key":"ref44","first-page":"107","article-title":"Supervised autoencoders: Improving generalization performance with unsupervised regularizers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Le"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2023.123396"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/11323516\/11456113.pdf?arnumber=11456113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T19:53:52Z","timestamp":1775159632000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11456113\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/tase.2026.3677399","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}