## 3 Results
### Performance Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Size** & **Recall** & **Specificity** & **F1-Score** & **Precision** \\ \hline
600 (original) & 73.0 & 89.4 & 75.5 & 78.1 \\
900 & 84.1 & 92.3 & 84.5 & 85.0 \\
1050 & 90.6 & 96.5 & 92.0 & 93.5 \\
1200 & **96.8** & **97.4** & **96.1** & **95.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification evaluation metrics for none-defined torque (NDT) curves in different augmentation ratios. data augmentation ratios in the softmax layer. As shown in Figure 12, the different colors of the data points represent samples in the dataset, where similar samples are closer to each other in the plot, and dissimilar samples are relatively farther apart. In Figure 12a, for the original dataset, the artificial features were not able to effectively cluster different types of curves, resulting in overlapping between different curves and indistinct data boundaries. However, as the data augmentation strength increased, as shown in Figure 12b-d, the artificial features gradually clustered different types of curves together, reducing the occurrence of overlapping between different curves, and presenting more distinct data boundaries. Figure 13 shows the confusion matrices results of the proposed model under different data augmentation intensities. The columns in the figure correspond to the predicted labels, while the rows correspond to the true labels. Each cell represents the combination of true and predicted classes, where the color indicates the accuracy of the predictions, with darker colors indicating greater accuracy. The results reveal that when the dataset size is 600, the model achieves the lowest prediction accuracy, with accuracies of 92%, 76%, and 73% for the classes AC, ATI, and NDT, respectively. Specifically, the model exhibits the highest misclassification rate between ATI and NDT, with 22% of ATI curves incorrectly identified as NDT. This could be attributed to the model's inability to differentiate the oscillatory characteristics of the ATI curves from the smooth features of the NDT curves due to insufficient training data. Additionally, 25% of the NDT curves are erroneously classified as AC curves, which might be caused by the indistinctive smooth features of some NDT curves, resembling those of AC curves. In cases of limited data, the model struggles to accurately extract their distinguishing features. As the dataset size increases, the model's performance in recognizing ATI and NDT improves. When the dataset size reaches 1200, the model achieves the best prediction accuracy for classes AC, ATI, and NDT, with accuracies of 96%, 96%, and 97%, respectively. In one comparative experiment group, the performance of different classification models is compared when the dataset size is 1200. The models include a traditional machine learning model (logistic regression), a traditional deep learning model (deep neural network), and the T-CNN model designed in this paper. As shown in Figure 14, the results indicate that the proposed T-CNN outperforms the other models across all classes and in terms of overall accuracy, recording predictive accuracies of 95.9%, 95.5%, and 96.7%, with an overall accuracy of 96%. In contrast, although the DNN performs comparably to the other models in the AC curves, with a predictive accuracy of 95.2%, it reflects notably lower accuracies in the ATI and NDT curves, with values of 83.7% and 84.2%, respectively, leading to the lowest overall accuracy among the models at 87.7%. The performance of the LG model falls between that of the T-CNN and the DNN, with accuracies of 94.3%, 86.7%, and 87.1% in the AC, ATI, and NDT curves, respectively, and an overall accuracy of 89.3%. Figure 12: Feature visualization for different data augmentation ratios: (**a**) 600 dataset size; (**b**) 900 dataset size; (**c**) 1050 dataset size; (**d**) 1200 dataset size.