Search

In order to predict the moisture of the mixture, a modeling method based on stacked generalization ensemble is introduced, and a Robust Scaler-Rank Gauss（RS-RG） hybrid algorithm is proposed to process the data input to the stacking model, and then the LGBM-RF-XGB hybrid model is established for sintered mixture moisture prediction based on stacked generalization ensemble, which can predict the moisture value before sinter mixture mixing. The internal mechanism of the LGBM-RF-XGB overlay model consists of generating metadata from the LGBM and RF models to calculate the final prediction using the XGB model. The proposed stacking model was simulated in comparison with several reference models by combining the sintering site data. The results show that the accuracy and error of the proposed stacking model are better than those of the reference models used for comparison, which meets the sintering process requirements. The proposed algorithm can be used for advance prediction of sinter mix moisture in actual production and provides a theoretical basis for automatic control of water addition. [ABSTRACT FROM AUTHOR]

Many existing arrhythmia researches focus on the separation of different frequency characteristic components in the ECG signal. However, the contribution of different subsequences to the final target decision-making is lack of research and analysis. In order to enhance the impact of high-contribution subsequences on the classifier, a recognition method combining variable weight singular spectrum analysis and deep learning is proposed. Multiple subsequences are obtained through singular spectrum analysis. The Gini coefficient under the random forest is calculated by the singular value of each sequence and used as the weight. The sequence samples with variable weights are used to train the neural network model, which can mine useful information more efficiently and further improve the recognition accuracy. The accuracy rate of final arrhythmia recognition is 98. 35%, and Macro-Fl is 97. 95%. Compared with the traditional fixed weight, the proposed recognition method of variable weight has a significant improvement in various performance indicators. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the predicted width of the first piece of steel after changing the steel type， the steel specification and the roll in the process of hot continuous rough rolling strip production， a new width prediction model based on the principal component analysis collaborated with random forest (PCA-RF) algorithm is proposed in this work. The PCA method is used to analyze the reasonability of data samples and the feature selection is carried out by calculating the eigenvalue， and principal component and cumulative contribution degrees. The best RF model is trained on variant dataset selected from the PCA. At the same time， support vector machine regression(SVR)and K-nearest neighbor(KNN)models are used for comparison and verification. The practical applications show that the R-squared value from the each pass width predicted by the PCA-RF model is controlled within the range of 0.999~1， and the prediction error of more than 96% samples is -5~5mm， which proves that the model can predict the steel width with a high precision. [ABSTRACT FROM AUTHOR]