Cross-Modal Interaction Fusion-Based Uncertainty-Aware Prediction Method for Industrial Froth Flotation Concentrate Grade by Using a Hybrid SKNet-ViT Framework
Fanlei Lu, Weihua Gui, Yulong Wang, Jiayi Zhou, Xiaoli Wang · 2025
Read Cross-Modal Interaction Fusion-Based Uncertainty-Aware Prediction Method for Industrial Froth Flotation Concentrate Grade by Using a Hybrid SKNet-ViT Framework by Fanlei Lu, Weihua Gui, Yulong Wang, Jiayi Zhou, Xiaoli Wang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12787880.txt.
Description
Fanlei Lu and co-authors present a 2025 paper on predicting ore quality in a foam-based mineral process. In flotation, mineral foam floats to the top, and the look of the foam hints at the grade of the final product. Air flow, slurry level, ore traits, and chemicals all shift the foam, so reading it is hard.
Ore traits cannot be measured directly, and sensor noise adds more doubt. Their method joins two network types: one reads local foam detail, and one reads the whole image at once. A fusion module blends the two views deeply. The design also states how unsure each prediction is, which helps plant staff trust the output.
Catalog record
| CREATOR | Fanlei Lu, Weihua Gui, Yulong Wang, Jiayi Zhou, Xiaoli Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |