## 4 Classification Results and Discussion  
The unprocessed data and the data preprocessed using the improved CELMD method were separately applied to train CNN, ViT networks, and four subtypes of Swin Transformer networks for comparative analysis. The training results of the unprocessed data for each model are presented in Table 2, while the training results of the preprocessed data for each model are shown in Table 3.  
From Tables 2 and 3, it can be observed that the comprehensive accuracy of all six models is above 95%. Among them, the accuracy of all four subtypes of Swin Transformer networks is higher than that of convolutional neural networks and ViT networks. Although the accuracy of the models is already at a high level, preprocessing the data still improves the accuracy of various models. In terms of model size, convolutional neural networks and Swin T networks are relatively lightweight. In particular, the Swin T model not only has high accuracy on a lightweight basis but also exhibits excellent performance in inference  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Model** \\ **Size** \\ **(M)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **Speed** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Inference** \\ **Speed** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{**Top1**} & \multirow{2}{*}{**Top7**} \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline CNN & 96.62 & 9 & 0.013 & 0.012 & 99.04 & 89.08 \\ ViT & 95.92 & 34 & 0.005 & 0.006 & 98.07 & 90.05 \\ Swin T & 97.89 & 11 & 0.007 & 0.004 & 99.00 & 91.28 \\ Swin S & 97.93 & 19 & 0.049 & 0.016 & 99.61 & 92.50 \\ Swin B & 97.78 & 34 & 0.019 & 0.007 & 98.87 & 91.48 \\ Swin L & 97.45 & 76 & 0.083 & 0.019 & 99.52 & 90.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training effectiveness of neural networks with unprocessed data.  
Figure 10: Improved CELMD decomposition results of the RS signal.  
speed. While its training speed is only surpassed by the ViT network, the other subtypes of Swin Transformer networks, compared to the Swin T network, have deeper network structures, resulting in varying degrees of slowdown in training and inference speeds. However, there is no significant improvement in accuracy. This may be partly due to the fact that the accuracy is already at a high level, partly because the acoustic emission waveform data are relatively simple compared to other complex data types. The difficulty in extracting features from simple data is not significant, and compared to the convolutional operations of CNN and the W-MSA mechanism of VIT, the downsampling mechanism and SW-MSA mechanism of Swin Transformer are more adept at handling complex data and uncovering deep-level feature information. Therefore, only limited accuracy improvements have been achieved.  
In terms of best classification category performance (Top1), the four subtypes of Swin Transformer exhibit only a slight advantage. However, in terms of worst classification category performance (Top7), Swin Transformer networks outperform convolutional neural networks and ViT networks, especially when trained with data preprocessed using the improved CELMD method, the performance is significantly better than that of other networks.