Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel sem...

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Bibliographic Details
Main Authors: Yuxuan Li, Chunjie Yang, Youxian Sun
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Sensors
Subjects:
LSTM
semi-supervised learning
FeO content
soft sensor
encoder-decoder
dynamic feature extraction
Online Access:https://www.mdpi.com/1424-8220/22/15/5861

Internet

https://www.mdpi.com/1424-8220/22/15/5861

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