## 4 Experimental Validation  
In this section, we demonstrate the recognition and classification ability of the above comprehensive methods for Lamb wave signals of different defects in composite materials. We classify different experimental test situations, and group the existing data into corresponding training sets to train the best model. Through the introduction and discussion in the previous chapter, it can be seen that DenseNet increases the transmission of gradients, reduces overfitting, and has low computational complexity and high accuracy. Therefore, DenseNet is used as the main model in the classification stage of the entire deep learning framework in this study. Moreover, this classifier has good perception effect on defect signals in this experiment and has strong feature extraction ability after verification. After research and optimization, the DenseNet model structure used in this experiment is shown in Table 2.  
In this study, a large number of simulation data about the defect are used to test the neural network model, and the recognition ability and feature extraction ability of different neural network models for this type of defect signal are compared and ana \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Output Size** & **DenseNet** \\ \hline convolution & 64 \(\times\) 64 & 7 \(\times\) 7 conv, stride2 \\ \hline pooling & 32 \(\times\) 32 & 3 \(\times\) 3 max pool, stride2 \\ \hline Dense block1 & 32 \(\times\) 32 & \(\begin{pmatrix}1\times 1\\ 3\times 3\end{pmatrix}\times\) 6 \\ \hline Transition layer1 & 32 \(\times\) 32 & 1 \(\times\) 1 conv \\ \hline  & 16 \(\times\) 16 & 2 \(\times\) 2 average pool, stride2 \\ \hline Dense block2 & 16 \(\times\) 16 & \(\begin{pmatrix}1\times 1\\ 3\times 3\end{pmatrix}\times\) 12 \\ \hline  & 16 \(\times\) 16 & 1 \(\times\) 1 conv \\ \hline  & 8 \(\times\) 8 & 2 \(\times\) 2 average pool, stride2 \\ \hline Dense block3 & 8 \(\times\) 8 & \(\begin{pmatrix}1\times 1\\ 3\times 3\end{pmatrix}\times\) 48 \\ \hline Transition layer3 & 8 \(\times\) 8 & 1 \(\times\) 1 conv \\ \hline  & 4 \(\times\) 4 & 2 \(\times\) 2 average pool, stride2 \\ \hline Dense block4 & 4 \(\times\) 4 & \(\begin{pmatrix}1\times 1\\ 3\times 3\end{pmatrix}\times\) 32 \\ \hline  & 1 \(\times\) 1 & 7 \(\times\) 7 global average pool \\ \cline{2-3} Classification layer & \multicolumn{3}{c}{Fully-connected softmax} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DenseNet network structure. Figure 11: The overall schematic diagram of DenseNet. lyzed, which is convenient for model selection. After converting one-dimensional data into two-dimensional images and putting them into corresponding neural networks for training, the network designed in this experiment is compared with common classification network models. This study combines the algorithms used for data expansion and classification recognition. In the data set and experimental setup, we used five combinations to test. The first is this proposed framework, which combines data augmentation and classification algorithms (including the improved DDPM). To validate the effectiveness of this method, we compared four different algorithm combinations: \"VAE + DenseNet\", \"DCGAN + DenseNet\", \"DDPM + ResNet\", and \"DDPM + DenseNet\" (including the original DDPM). In order to test the quality and effect of data generated by different models, several common comparison indicators are selected in combination with common methods. First, the accuracy rate, that is, the ratio of the number of correctly predicted results to the total number of samples. The higher the accuracy, the better the quality of the generated data. Then there is the recall rate, which is aimed at the original sample. It means to correctly predict the probability of positive samples in all positive samples, that is, to correctly predict the ratio of the number of positive samples to all positive samples. The higher the recall rate, the more accurate results will be retrieved. Finally, Frechet Inception Distance (FID) index is selected.