Industrial Process Soft Sensing Based on Bidirectional Optimization Learning of Data Augmentation and Prediction Models Under Limited Data

Soft sensing techniques are crucial for predicting key quality indicators in industrial processes. Despite the widespread application of deep learning in the soft sensing domain, challenges such as limited sampling and the complex nonlinear relationships among process variables limit the accuracy an...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 74; s. 1 - 11
Hlavní autoři:	Li, He, Wang, Zhaojing, Li, Li, Yan, Xiaoyun, Hu, Xinrong, Li, Lijun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Bidirectional optimization Complex variables Compressive strength Concrete properties Constraints Correlation Data augmentation Data models Decoding Deep learning Depth indicators Feature extraction improved autoencoders (AEs) industrial soft sensing limited data Machine learning Optimization prediction modeling Prediction models Predictions Predictive models Process variables Soft sensors Vectors
ISSN:	0018-9456, 1557-9662
On-line přístup:	Získat plný text
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