A Quality-Related Concurrent Dual-Latent Variable Model and Its Semi-Supervised Soft Sensor Applications-Reference-Cited by-同舟云学术

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A Quality-Related Concurrent Dual-Latent Variable Model and Its Semi-Supervised Soft Sensor Applications

Published:2023 Issue: Volume:11 Page:47539-47551
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Yun¹^ORCID,Ying Ze²^ORCID,He Yuchen³^ORCID

Affiliation:

1. Department of Mechanical and Electrical Engineering, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China

2. College of Information Science and Engineering, Northeastern University, Shenyang, China

3. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Zhejiang Province Natural Science Foundation of China

Project of Department of Education of Zhejiang Province

Youth Fund of the Zhejiang Tongji Vocational College of Science and Technology

Fundamental Research Funds for the Provincial Universities of Zhejiang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10123707.pdf?arnumber=10123707

Reference42 articles.

1. Robust Mixture Probabilistic Partial Least Squares Model for Soft Sensing With Multivariate Laplace Distribution

2. Cooperative Deep Dynamic Feature Extraction and Variable Time-Delay Estimation for Industrial Quality Prediction

3. Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

4. Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application

5. Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data

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