A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities-Reference-Cited by-同舟云学术

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A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

Published:2024-06-06 Issue:24 Volume:9 Page:25539-25554
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Yan Feng¹,Kong Liyuan¹,Li Yanrui¹,Zhang Hanwen²,Yang Chunjie¹^ORCID,Chai Li¹

Affiliation:

1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China

2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.4c01254

Reference118 articles.

1. Data‐driven modelling methods in sintering process: Current research status and perspectives

3. Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge

4. Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder

5. Quality-Driven Regularization for Deep Learning Networks and Its Application to Industrial Soft Sensors

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