Research on intelligent recognition method of multi-modal information working condition in fused magnesium furnace
LI Wei-tao
GU Jia-qin
WANG Dian-hui
WU Gao-chang
Abstract：Aiming at the lack of modal completeness in the identification process of fused magnesium furnace,an intelligent recognition method of multi-modal information working condition of electric smelting magnesia furnace is described in this paper.Firstly,we use semantic neural network to extract image features and two-way coding language model to extract linguistic features to construct a complete joint feature vector of multimodal working conditions,and then we realize global interaction through the Transformer encoding layer to capture the fine-grained alignment of visual and linguistic information.The self-attention mechanism of the adaptive Transformer decoding layer is introduced to obtain the multimodal work condition recognition results using fully connected networks.Based on reinforcement learning,we define the evaluation strategy of gating unit,evaluate the recognition results of uncertain working conditions in real time,construct the dynamic adjustment mechanism of the decoding layer to obtain the fine-grained features of multimodal working conditions,and use the fuzzy integration to integrate the recognition results of the model library.Experiments demonstrate the effectiveness and robustness of this method.
Keywords：fused magnesium furnacemulti-modalreinforcement learningTransformerworking condition recogni-tion
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：16( 931-946 )