Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing

The rapid development of soft sensors has significantly enhanced industrial operations by promoting sustainability, safety, and efficiency. However, modern process industries involve highly dynamic systems with multimode and nonlinear data, posing challenges for conventional soft sensor models that...

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Vydané v:	IEEE internet of things journal Ročník 12; číslo 22; s. 47658 - 47666
Hlavní autori:	He, Yan-Lin, Zhou, Yang-Xiao-Yu, Xu, Yuan, Zhu, Qun-Xiong, Li, Xingyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 15.11.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autoencoders Data models Feature extraction Gas turbines Internet of Things Multimode process Predictive models Probabilistic models soft sensing Soft sensors Statistical analysis Time series analysis Training transfer learning transformer Transformers variational Bayesian network
ISSN:	2327-4662, 2327-4662
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