## 3 Results and Discussions
### Damage Classification Using the Deep Learning Approach
#### 3.3.3 Classification of AE Signals from Individual Specimens  
In this section, the group of AE signals originating from an individual specimen is classified in terms of their configuration (Tx, Ty, Tz, and T45). Based on the previous result (see Table 5), the ReLu activation function has been shown to be effective in classifying the AE signals. Based on this optimal configuration of CNNs, this section examines the amount of data needed to train a CNN to achieve 100% accuracy.  
The results are shown in Table 6. In the first training mode, TM 1, 400 input data were used, considering 100 per class (Tx, Ty, Tz, and T45) using the ReLu activation function. Figure 18 shows the results for alexNet (65.0%), squeezeNet (62.0%) and the new model (68.0% accuracy). The processing time behaves the same as the previous result. Finally, when the input images are increased from 400 to 2000 at a rate of 500 per class, an improvement is seen, which is 85.5% for alexNet, 81.1% for squeezeNet, and 90.1% for the new model (see Figure 19). Thus, for the best CNN configurations, the amount of input data affects the classification accuracy. A simplified CNN model can have better classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **N\({}^{\circ}\) Training** \\ **Mode [TM]** \\ \end{tabular} & **Model** & \begin{tabular}{c} **Number of** \\ **Inputs** \\ \end{tabular} & \begin{tabular}{c} **Type of** \\ **Activation** \\ **Function** \\ \end{tabular} & \
\begin{tabular}{c} **Pooling** \\ **Avg/Max** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **[\%]** \\ \end{tabular} & **Testing [\%]** & **Time** \\ \hline \multirow{3}{*}{TM 1} & AlexNet & 300 & Sigmoid & Avg/Max & 33.3 & 33.3 & 1 h 9 min \\  & SqueezeNet & 300 & Sigmoid & Avg/Max & 33.3 & 33.3 & 58 min 20 s \\  & New Model & 300 & Sigmoid & Avg/Max & 33.3 & 33.3 & 35 min 04 s \\ \hline \multirow{3}{*}{TM 2} & AlexNet & 300 & ReLu & Avg/Max & 100 & 100 & 49 min 06 s \\  & SqueezeNet & 300 & ReLu & Avg/Max & 100 & 100 & 38 min 07 s \\  & New Model & 300 & ReLu & Avg/Max & 100 & 100 & 24 min 00 s \\ \hline \multirow{3}{*}{TM 3} & AlexNet & 1500 & Sigmoid & Avg/Max & 33.3 & 33.3 & 4 h 39 min \\  & SqueezeNet & 1500 & Sigmoid & Avg/Max & 33.3 & 33.3 & 3 h 35 min \\  & New Model & 1500 & Sigmoid & Avg/Max & 33.3 & 33.3 & 2 h 01 min \\ \hline \multirow{3}{*}{TM 4} & AlexNet & 1500 & ReLu & Avg/Max & 100 & 100 & 3 h 10 min \\  & SqueezeNet & 1500 & ReLu & Avg/Max & 100 & 100 & 2 h 39 min \\ \cline{1-1}  & New Model & 1500 & ReLu & Avg/Max & 100 & 100 & 1 h 40 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of AE signals based on the configuration of the specimen.  
Figure 18: Classification based on the signals generated from individual specimens using TM 1: (a) AlexNet, (b) SqueezeNet, and (c) the Proposed model CNN.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} \begin{tabular} \end{tabular} \\ \end{tabular} & \begin{tabular}{c} **Model** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Inputs** \\ \end{tabular} & \begin{tabular}{c} **Type of** \\ **Activation** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **Pooling** \\ **Avg/Max** \\ \end{tabular} & \begin{tabular}{c} **Validation** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{c} **Testing [\%]** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ \end{tabular} \\ \hline \multirow{3}{*}{TM 1} & AlexNet & 400 & ReLu & Avg/Max & 68.3 & 65 & 1 h 16 min \\  & SqueezeNet & 400 & ReLu & Avg/Max & 61.7 & 62 & 55 min 01 s \\  & New Model & 400 & ReLu & Avg/Max & 69.7 & 68 & 40 min 05 s \\ \hline \multirow{3}{*}{TM 2} & AlexNet & 2000 & ReLu & Avg/Max & 97.2 & 85.5 & 4 h 37 min \\  & SqueezeNet & 2000 & ReLu & Avg/Max & 96.4 & 81 & 4 h 07 min \\ \cline{1-1}  & New Model & 2000 & ReLu & Avg/Max & 97.8 & 90.1 & 2 h 50 min \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification of AE signals from individual specimens based on the configuration.