## 4 Discussion  
In this paper, we propose an improved AoT-DCGAN and T-CNN hybrid deep learning model for the automatic recognition and classification of PTCs curves, enabling the intelligent assessment of PTCs connection quality. The model addresses the following challenges: (1). achieving the automatic classification of PTCs curves; (2). overcoming the limited classification performance of deep learning models when dealing with small sample space; and (3). introducing a novel weight optimization strategy to mitigate the problem of gradient vanishing during the training process of the DCGAN model. To enable the automatic recognition of PTCs, we develop a novel T-CNN model that accurately classifies PTCs curves into different categories. To tackle the issue of small sample size, we design an AoT-DCGAN model to generate synthetic PTCs curves, thereby expanding the original  
Figure 16: The loss functions of the DCGAN model for generating ATI curves.  
Figure 17: The loss functions of the DCGAN model for generating NDT curves.  
Figure 15: The loss functions of the DCGAN model for generating AC curves.  
dataset. Additionally, we employ the asynchronous optimization strategy to stabilize the gradient descent process of the generator, resulting in the generation of realistic synthetic PTCs curves.  
Through a series of comparative experiments using different data augmentation ratios and traditional classification models, we found that the performance of the proposed method significantly improves as the dataset size increases, and the T-CNN demonstrates better performance for curve classification tasks. This conclusion is further supported by the T-SNE and confusion matrix visualization, which demonstrates a positive correlation between the model's clustering capability and the dataset size. Furthermore, we compared the variations in the loss function of the DCGAN model under different optimization strategies, and the results indicate that the proposed asynchronous optimization strategy greatly enhances the performance of the generator. In summary, the hybrid deep learning model proposed in this paper exhibits superior performance and significant potential in the evaluation of the connection quality in PTCs.  
In addition, during the data generation process, it was observed that the weight update frequency and learning rate of the generator and discriminator in DCGAN have a significant impact on the results. Among these, the choice of learning rate has a more pronounced effect on the results. When a higher learning rate is used, the loss function decreases rapidly, but this can lead to the model being underfitted, preventing further optimization. Therefore, in future research, the application of dynamic learning rates in GAN models will be considered to address this issue.  
Conceptualization, Z.M.; methodology, Z.M. and J.S.; software, Z.M.; validation, J.S. and W.L.; formal analysis, Y.F.; investigation, Y.C.; resources, Y.F.; data curation, Z.M.; writing--original draft preparation, Z.M.; writing--review and editing, Z.M. and X.H.; visualization, Y.F.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Oil & Gas and New Energy Company, PetroChina, grant number 20220302-09.  
Not applicable.  
The authors declare no conflict of interest.