Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process-Reference-Cited by-同舟云学术

Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process

Published:2024-02 Issue:2 Volume:54 Page:974-987
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Wang Yalin¹^ORCID,Li Silong¹,Liu Chenliang¹^ORCID,Wang Kai¹^ORCID,Yuan Xiaofeng¹^ORCID,Yang Chunhua¹^ORCID,Gui Weihua¹^ORCID

Affiliation:

1. School of Automation, Central South University, Changsha, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Science and Technology Innovation Program of Hunan Province

Fundamental Research Funds for the Central Universities of Central South University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10402573/10207029.pdf?arnumber=10207029

Reference44 articles.

1. Projecting the Production Quantity and Per Capita Quantity of Six Major Food Crops Worldwide by 2030

2. Atomic insights into flotation separation of KCl and NaCl from a new viewpoint of hydration layer: A molecular dynamic study

3. Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint

4. Modelling for froth flotation control: A review

5. Toward Flotation Process Operation-State Identification via Statistical Modeling of Biologically Inspired Gabor Filtering Responses

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