Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature

Fan, Zipei; Wang, Kang; Li, Xiaoli

doi:10.1109/ddcls58216.2023.10166738

Cited by 1 publication

(1 citation statement)

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“…Consequently, there is an urgent need to enhance the accuracy of flotation working state recognition. Recently, hypergraph neural networks (HGNNs) have been widely used in tasks such as image classification [ 21 ] and retrieval [ 22 ]; leveraging its established capability in addressing intricate high-order correlations among data [ 23 ], the HGNN was introduced to identify flotation conditions through the utilization of manually extracted texture features, thereby achieving a high level of performance [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition

Fan,

Feng,

Wang

et al. 2024

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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 three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.

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