## 4 Results and Discussion  
In this paper, we collected IE data from two different structures (Specimen\(-1\) and Specim\(-2\)) as described in Section 2. In Specim\(-1\) and Specim\(-2\), this data collection amounted to 8000 and 11,231, respectively. In addition, we analyzed all the amplitude values for the data which occurred and chose a range from 880 to 2640 us the training feature. Table 1 contains detailed information on the IE dataset that we used. In the experiment, we determined the optimal parameters of the proposed model using the grid search method. We examined the dimension of feature space (i.e., hidden node) and loss function in Deep SVDD, as well as output sizes of the first and second LSTM layer in the encoder structure of the autoencoder. The decoder structure of the autoencoder is the reverse of the encoder structure of the autoencoder. First, we determined optimal parameters about the encoder and decoder structure of the autoencoder. As a result, we chose the optimal parameters for the output sizes of the first and second LSTM layers in the encoder structure to be 8 and 4, respectively. So, output sizes of the first and second LSTM layers in the decoder structure are 4 and 8, respectively. Second, the dimension of the feature space was readjusted in Deep SVDD is 32 and the dimension of the feature space represented in the encoder of the autoencoder is 320. Finally, a hard margin was \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Feature Type** & **Number of Features** \\ \hline Raw IE & 880–2640 μs & 80 \\ \hline
**Specimen Type** & **Normal** & **Defect** \\ \hline Specim\(-1\) & 3200 & 4800 \\ Specim\(-2\) & 3456 & 77/75 \\ \hline Total & 14,390 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information of the IE data used as input features and the quantity collected in two specimens. Figure 9: Architecture of the autoencoder used in Deep SVDD. used in Deep SVDD's loss function. Table 2 shows them in detail. Finally, we used an early stopping method that stopped training when the loss of the validation data rose three times in a row to prevent overfitting. We carried out the following two evaluations in the experiment and compared the classification accuracy (Acc) for the normal and void data: (1) Examine the supervised learning model's performance with IE data collected in the same environment. It performs training and evaluation using just Specim\(-\)2 data divided in an 8:1:1 (train:validation:test) ratio. (2) With IE data collected in different environments, compare the performance of the supervised learning model [26; 27; 28] with our proposed models. It performs an evaluation using the IE data in Specim\(-\)2 and trains using the IE data in Specim\(-\)1. The two experiments support the issue we identified and demonstrate the superiority of the model we proposed for addressing it. In the first experiment (using the IEs collected in the same specimen), we trained and evaluated the performance of the supervised learning model developed in the previous studies [26; 27; 28]. The results are shown in Table 3. As shown in Table 3, the supervised learning model in this instance had an extremely high accuracy. The effectiveness of supervised learning models in detecting defects in IE data with the same distribution may therefore be proven. However, the issue we discovered makes the use of supervised learning models challenging. So, we compared the performance using the IE data collected in different specimens in order to demonstrate that our proposed model could address this issue. As shown in Table 4, supervised learning models did not correctly distinguish between the normal and defective IEs collected in different specimens. Some models also showed a tendency to overfit. On the other hand, our proposed model showed an accuracy of about 77.84%. As well, it showed an average accuracy improvement of about 25% (up to around 47%) compared to the supervised learning model.