Aiming at the problem that wind turbine blade icing seriously affects the generating efficiency, safety and economy of wind turbines, a Stacked AutoEncoder (SAE) network based prediction model was proposed based on SCADA (Supervisory Control And Data Acquisition) data. The unsupervised method of encoding-decoding was utilized to pre-train the unlabeled dataset, and then the back propagation algorithm was utilized to train and fine tune the labeled dataset to achieve adaptive fault feature extraction and fault state classification. The complexy of the traditional prediction models was simplified effectively, and the influence of artificial feature extraction was avoided on model performance. The historical data of wind turbine No.15 collected by SCADA system was used for training and testing. The accuracy of the test results was 97.28%. Compared with the models based on Support Vector Machine (SVM) and Principal Component Analysis-Support Vector Machine (PCA-SVM), which accuracies are 91% and 93% respectively, the result indicates that the proposed model is more accurate than the other two.

Building an interpretable and large-scale protein-compound interactions model is an very important subject. A new chemical interpretable model to cover the protein-compound interactions was proposed. The core idea of the model is based on the hypothesis that a protein-compound interaction can be decomposed as protein fragments and compound fragments interactions, so composing the fragments interactions brings about a protein-compound interaction. Firstly, amino acid oligomer clusters and compound substructures were applied to describe protein and compound respectively. And then the protein fragments and the compound fragments were viewed as the two parts of a bipartite graph, fragments interactions as the edges. Based on the hypothesis, the protein-compound interaction is determined by the summation of protein fragments and compound fragments interactions. The experiment demonstrates that the model prediction accuracy achieves 97% and has the very good explanatory.

According to the theory of sparse representation, images can be sparse-represented by using an appropriately redundant dictionary. The completeness can enable using very few big coefficients to capture the important information of images, and cause more robust to noise. Regarding image de-noising, considering the human visual characteristics, this paper studied the effective representation of characteristics and edge information of noisy image based on complete dictionary. For more effective feature retaining of images, a method of feature-retaining de-noising via sparse representation was proposed, which made the Structural SIMilarity (SSIM) as fidelity measure of the information. The experimental results indicate that the proposed algorithm has a better efficiency of de-noising, enhances the capacity of retaining feature, and gets a better visual effect of de-noised image.