Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition.

ABSTRACT: Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from thre

SUBMITTER: Fan Z 

PROVIDER: S-EPMC10969447 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature