Improved Virtual Sample Generation Method Using Enhanced Conditional Generative Adversarial Networks with Cycle Structures for Soft Sensors with Limited Data-Reference-Cited by-同舟云学术

Improved Virtual Sample Generation Method Using Enhanced Conditional Generative Adversarial Networks with Cycle Structures for Soft Sensors with Limited Data

Published:2021-12-30 Issue:1 Volume:61 Page:530-540
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhu Qun-Xiong¹²^ORCID,Xu Tian-xiang¹²,Xu Yuan¹²^ORCID,He Yan-Lin¹²^ORCID

Affiliation:

1. College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China

2. Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, P. R. China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c03197

Reference41 articles.

1. Novel Discriminant Locality Preserving Projection Integrated With Monte Carlo Sampling for Fault Diagnosis

2. Utilizing big data for batch process modeling and control

3. Development of Adversarial Transfer Learning Soft Sensor for Multigrade Processes

4. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

5. Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data

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