China has launched national carbon trading marked in 2021, and up to now, Hubei has the largest proportion of carbon trading volume, it is totally important to research the carbon trading price in Hubei. In this paper, we propose a new model for carbon price in Hubei, which is combine complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) with convolutional neural network (CNN). Firstly, carbon price is decomposed by CEEMDAN into various intrinsic mode function (IMF) which are combined using sample entropy approach. Then, CNN is used to establish a point prediction model. Finally, we calculate the mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of the model are 0.4893, 0.6809, and 0.9754, respectively. Compare with other two models, the hybrid model proposed in this paper exhibits the best performance.
Due to lack of training samples, overfitting is a severe problem in fault diagnosis for mechanical devices, especially for rotating machinery. In this paper, a graph neural network (GNN) method with one-shot learning is proposed for fault diagnosis of rotating machinery. Convolutional Neural Network (CNN) is applied to extract the feature vectors and generate codes for one-shot learning. With adjacency matrix in GNN, the proposed method can achieve fault classification for rotating machinery with small dataset. Rotate vector (RV) reducer of the industrial robot and bearing of the rotating shaft were chosen as experimental subjects. Experimental results show the high accuracy of classification in both experiments with the proposed method. To further verify the efficiency of this method, Siamese Net, Matching Net and SAE+RF were chosen as the comparisons. The results indicate the proposed method outperforms all the selected methods for fault classification in both rotating machineries.
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