Deep correlation and precise prediction between static features of froth images and clean coal ash content in coal flotation: An investigation based on deep learning and maximum likelihood estimation

Lu, Fucheng; Liu, Haizeng; Lv, Wenbao

doi:10.1016/j.measurement.2023.113843

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Deep correlation and precise prediction between static features of froth images and clean coal ash content in coal flotation: An investigation based on deep learning and maximum likelihood estimation

Cited by 4 publications

References 50 publications

A multiscale network for deblurring coal slurry foam images

A multiscale network for deblurring coal slurry foam images

Optimizing flotation froth image segmentation via parallel branch network and hybrid loss supervision

A PSO-PLS Based Prediction Model for Reagent Dosage in Coal Slime Flotation

Contact Info

Product

Resources

About