A method of evaluating cell state based on data augmentation and ViT16

Abstract

Abstract In this paper, based on the model of data augmentation and Vision Transformer 16 (ViT16), a method of assessment for electrolysis cell state is presented to get the real-time information of the current cell state, so as to improve current efficiency of process. Firstly, in order to solve the issue of the small sample data and improve classification accuracy, the method of data augmentation is performed on the flame hole images by using convolutional block attention module to improve auxiliary classifier generativhyhee adversarial network. Secondly, the deep feature data of the flame hole images is extracted by the method of ViT16, and the genetic algorithm is applied to eliminate the redundant feature data to improve the accuracy. Thirdly, the support vector machines model is employed to classify the feature data, and the aluminum cells are classified into cold, hot, and normal. Finally, the actual data are applied to the experiments of the above method, the results of experiments show that this method is better than other methods, and the accuracy of classifying the cell state is as high as 98.677%. This is of great significance for the guidance of aluminum electrolysis production process.