Operating Condition Recognition of Industrial Flotation Processes Using Visual and Acoustic Bimodal Autoencoder With Manifold Learning
Author:
Affiliation:
1. School of Automation, Central South University, Changsha, China
Funder
National Natural Science Foundation of China
Science and Technology Innovation Program of Hunan Province in China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10521545/10436699.pdf?arnumber=10436699
Reference29 articles.
1. The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review
2. Revolutionizing Flotation Process Working Condition Identification Based on Froth Audio
3. Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process
4. Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint
5. A dynamic flotation model to infer process characteristics from online measurements
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