In response to the thin nature of hot rolled steel plates and strips, the vast majority of which are surface defects that can easily lead to production accidents, and limited by the challenges of insufficient datasets and a large amount of unlabeled data, this paper proposes a comparative learning method to solve the above problems. In terms of methods, a dual data augmentation strategy is adopted. Firstly, the original image is data enhanced through manual processing, and CycleGAN is introduced for style transfer to enrich the dataset. Then, ResNet152 network is used for feature extraction, and several comparative learning methods are applied to observe the accuracy of hot rolled strip defect detection. In the end, the improved comparative learning method in this article successfully improved the accuracy of surface defect classification for hot rolled strip steel. Through this research, we are committed to providing more reliable quality control methods for industrial production and reducing the risk of production accidents.
The package recommendation has always been an important issue in the marketing of mobile operators, and machine learning provides a new solution for operators. Aiming at the problem that too many training times of dirty data in the existing methods lead to the reduction of prediction accuracy and the tedious manual setting of model parameters, this paper propose a model combining grid search and XGBoost, and use the exhaustive search method to find the parameter value with the highest accuracy in the validation set within the parameter range of the given XGBoost. Compared with Random Forest and XGBoost default parameter values, experiments show that the proposed model has higher prediction accuracy and can effectively avoids the error caused by manual adjustment of parameters.
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