Feature Constrained Encoding Generative Adversarial Prediction Network for Quality Measurement Modeling Under Multitime Scales Data Imbalance In Process Industry-Reference-Cited by-同舟云学术

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Feature Constrained Encoding Generative Adversarial Prediction Network for Quality Measurement Modeling Under Multitime Scales Data Imbalance In Process Industry

Published:2024-07-01 Issue:13 Volume:24 Page:20928-20939
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Huang Gaolu¹^ORCID,Hao Xiaochen¹^ORCID,Zhang Yifu¹^ORCID,Liu Jinbo¹,Jiao Junze¹

Affiliation:

1. School of Electrical Engineering, Yanshan University, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Hebei Natural Science Foundation

Hebei Innovation Capability Improvement Plan Project

S&T Program of Hebei

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10577556/10535134.pdf?arnumber=10535134

Reference47 articles.

1. Effect of free lime content on properties of cement–fly ash mixtures

2. Multisource Data Ensemble Modeling for Clinker Free Lime Content Estimate in Rotary Kiln Sintering Processes

3. LS-SVR-Based Soft Sensor Model for Cement Clinker Calcination Process

4. Multiscale Dynamic Feature Learning for Quality Prediction Based on Hierarchical Sequential Generative Network

5. Neural networks with upper and lower bound constraints and its application on industrial soft sensing modeling with missing values

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