Semi-Supervised Deep Conditional Variational Autoencoder for Soft Sensor Modeling-Reference-Cited by-同舟云学术

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Semi-Supervised Deep Conditional Variational Autoencoder for Soft Sensor Modeling

Published:2024-03-01 Issue:5 Volume:24 Page:7153-7164
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Tang Xiaochu¹^ORCID,Yan Jiawei¹^ORCID,Li Yuan²,Zhang Xinmin³^ORCID,Song Zhihuan³

Affiliation:

1. School of Automation, Shenyang Aerospace University, Shenyang, China

2. College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China

3. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10453373/10400433.pdf?arnumber=10400433

Reference36 articles.

1. Dynamic Modelling and Predictive Control in Solid Oxide Fuel Cells: First Principle and Data-Based Approaches

2. Data-driven Soft Sensors in the process industry

3. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry

4. Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

5. Regression on dynamic PLS structures for supervised learning of dynamic data

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder;Processes;2024-08-26

2. Stacked dynamic target regularization enhanced autoencoder for soft sensor in industrial processes;The Canadian Journal of Chemical Engineering;2024-08-20

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