Global Dependency Graph Network for Soft Sensing in Process Industry-Reference-Cited by-同舟云学术

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Global Dependency Graph Network for Soft Sensing in Process Industry

Published:2024-08-15 Issue:16 Volume:24 Page:26290-26300
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Jia Mingwei¹^ORCID,Zhou Le²^ORCID,Liu Yi¹^ORCID,Gao Zengliang¹,Yao Yuan³^ORCID

Affiliation:

1. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, China

2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China

3. Department of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan

Funder

National Natural Science Foundation of China

National Science and Technology Council, ROC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10637303/10579729.pdf?arnumber=10579729

Reference40 articles.

1. Data-driven constrained reinforcement learning for optimal control of a multistage evaporation process

2. Multirate Nonlinear Process Fault Detection Based on Multiscale Hierarchical Variational Autoencoder

3. A two-phase soft sensor modeling framework for quality prediction in industrial processes with missing data

4. Rebooting data-driven soft-sensors in process industries: A review of kernel methods

5. TimeDDPM: Time Series Augmentation Strategy for Industrial Soft Sensing

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