KEYWORDS: Data modeling, Aluminum, Video, Data processing, Education and training, Feature fusion, Feature extraction, Video processing, Performance modeling
Superheat is one of the key indicators for reflecting production efficiency in aluminum electrolysis industry. To address the problem of insufficient performance in superheat identification with single modal data, a multi-modal deep feature fusion (MMDFF) model is proposed for superheat identification. By considering the global and local features synchronously, a cross-modal information interaction block is developed to improve the accuracy of superheat identification, which enriches the information expression of each mode. Finally, the effectiveness of the proposed superheat identification is demonstrated by the comparison experiments conducted on multi-source datasets of aluminum electrolysis industrial process. Meanwhile, the ablation experiments are used to verify the superiority of the cross-modal information interaction block.
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