Stacked Dual-Guided Autoencoder: A Scalable Deep Latent Variable Model for Semi-Supervised Industrial Soft Sensing

Stacked autoencoders (SAEs) have been widely used in soft sensing of industrial process data. However, most of them extract features by minimizing the reconstruction error of the input data at each layer, leading to accumulated loss. Moreover, the extraction of quality-related features is crucial fo...

Celý popis

Podrobná bibliografie

Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 14
Hlavní autoři:	Yang, Zeyu, Hu, Tingting, Yao, Le, Ye, Lingjian, Qiu, Yi, Du, Shuxin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Data models Decoding Deep learning Encoding Feature extraction feature representation Monitoring Performance prediction Real variables semi-supervised learning soft sensor Soft sensors stacked autoencoder (SAE) Vectors
ISSN:	0018-9456, 1557-9662
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!