## 3 Results
### Performance Analysis  
The experiments to validate the proposed hybrid model's superior performance in PTCs curve classification were conducted from three perspectives. Firstly, comparative experiments were performed with four groups of datasets of varying sizes to verify the classification performance of the proposed model under different data augmentation ratios. Secondly, comparative experiments were conducted with a set of classification models to demonstrate the superior performance of the T-CNN model proposed in this paper for PTCs curve classification under identical dataset conditions. Lastly, comparative experiments were performed with three groups of loss functions to examine the impact of the proposed asynchronous optimization strategy on enhancing the AoT-DCGAN's training process. The hyperparameters used in these experiments are listed in Table 2.  
In the four groups of comparative experiments on dataset sizes, the performance of the proposed hybrid model is demonstrated for data augmentation ratios of 600, 900, 1050, and 1200. Among these, the original dataset corresponds to 600, indicating the absence of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Epochs & 250 \\ Batch size & 16 \\ Learning rate & 0.002 \\ Optimizer type & Adam \\ Shuffle & Every-epoch \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyper-parameters used in the classification models. Figure 11: Examples of the real and fake data samples generated by AoT-DCGAN. data augmentation techniques, i.e., the AoT-DCGAN model. The datasets 900, 1050, and 1200 correspond to augmentation ratios of 1.5, 1.75, and 2, respectively. In each group, the training set accounts for 80% of the dataset, while the remaining 20% serves as the validation set. The classification performance of the models is evaluated using recall (Equation (4)), specificity (Equation (5)), F1-score (Equation (6)), and precision (Equation (7)). To mitigate the influence of randomness, the experimental results are averaged over five trials. The evaluation results of the models on different datasets can be seen in Tables 3-5. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{5}\]  
\[\text{F}_{1}\text{score}=2\times\frac{\text{precision}\times\text{recall}}{ \text{precision}+\text{recall}} \tag{6}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
As shown in Tables 3-5, for the AC curve, the classification performance with a dataset size of 1200 exhibits the best performance across all metrics, with 96%, 99.6%, 98%, and 99.3%, respectively. Similarly, for the ATI curve, the performance metrics outperform those of other groups when the dataset size is 1200, with 90.7%, 96.2%, 92.2%, and 93.7%, respectively. As for the NDT curve, the evaluation results indicate consistency with the previous results, all achieving the best performance when the dataset size is 1200, with percentages of 96.9%, 94.9%, 93.9%, and 91.2%. To further demonstrate the ability of the AoT-DCGAN model to enhance the classification performance of PTCs curves, we utilized the T-SNE visualization method to depict the inherent structure and features of PTCs curves acquired by the T-CNN model at various \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Size** & **Recall** & **Specificity** & **F1-Score** & **Precision** \\ \hline
600 (original) & 76.3 & 95.7 & 81.7 & 87.9 \\
900 & 85.5 & 95.3 & 86.8 & 88.1 \\
1050 & 92.5 & 93.8 & 91.1 & 89.8 \\
1200 & **95.5** & **96.4** & **94.8** & **94.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification evaluation metrics for abnormal interference (ATI) curves in different augmentation ratios.