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The document presents a multi-source unsupervised domain adaptation model (MUDA_DF) for predicting mill load parameters in the grinding industry using deep learning and feature fusion. This model addresses the challenges of model mismatch and lack of labeled data under changing operating conditions, achieving high prediction accuracy for parameters like material-to-ball volume ratio, pulp density, and charge volume ratio. Experimental results demonstrate the effectiveness of the proposed method, significant

Author

adityaphd5508

Language

EN