# Research on State Diagnosis Methods of UHF Partial Discharge Sensors Based on Improved ViT
## 4 Experimental Validation and Analysis
### Analysis of the Results  
In order to better verify the validity of the model, the ViT model was introduced as a reference to see if the model would achieve better results as described in the above theory. Below, Table 1 compares and shows the classification accuracy of the two models on the validation set after the training of the same dataset is completed. It is evident that the improved model achieves significant improvements compared to the original model.  
In addition, this paper also compared the training processes of the two models. As it shows in the Figure 5, it can be seen that the improved model can achieve convergence through slightly shorter epoch training compared to the pre-improved model. This can effectively reduce costs and time in industrial scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy of Training** & **Accuracy of the Test** \\ \hline ViT & 94.8\% & 92.9\% \\ Improved ViT & 97.3\% & 95.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy of the model.  
Figure 4: Characteristics of data from UHF partial discharge sensors in a data-failure state due to external signal interference.  
The confusion matrix is a commonly used analysis table in machine learning for evaluating the predicted results of classification models. It summarizes the predictions of the model against the true labels in a matrix format. The rows of the confusion matrix represent the true values, while the columns represent the predicted values. The following Figures 6 and 7 are a comparison of the confusion matrix of ViT and the improved ViT. Compared with the previous table statistics and training curves, the superiority of the improved ViT over the original ViT can be perceived more intuitively.  
In order to better verify the universality of the model, the improved model will be compared with the classic ResNet50 and ResNet50V2 hereinafter. ResNet50 (a residual network with 50 layers) is a deep convolutional neural network architecture introduced by [PERSON] et al [33]. in their landmark paper \"Deep Residual Learning for Image Recognition\" (2016). This model was a breakthrough in the field of computer vision as it addressed the degradation problem faced by deeper networks, where performance plateaus or even de  
Figure 5: Accuracy of training.  
Figure 6: The confusion matrix of ViT on the test set.  
grades as layers increase. ResNet50 has become a standard architecture for many computer vision tasks, including image classification, object detection, and segmentation. It won first place in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2015, with top-five error rates of 3.6%. Its ability to generalize well, even at great depths, has made it a foundational model for further developments in deep learning. Figure 8 shows the core module of ResNet50. ResNet50V2 is an improved version of ResNet50, which retains the basic features of the ResNet architecture while improving model performance and training efficiency through pre-activation structures, improved residual blocks, and more BN layers.  
Here, to better highlight the advantages of the improved model, it was compared with the baseline model, ResNet50, and ResNet50V2. To ensure fairness in the experiment, the parameters of ResNet50, ResNet50V2, and the improved ViT model were set to approximately 25M. Figure 9 shows the training accuracies of the three models.  
It can be observed from Figure 9 that, compared to the ResNet50 and ResNet50V2 baseline models, the improved model has the fewest training epochs and the highest accuracy, which shows significant performance enhancements. Moreover, ResNet50V2 has slightly higher accuracy than ResNet50, with little difference in training time.  
Figure 8: Residual module.  
Figure 7: The confusion matrix of the improved ViT on the test set.  
In summary, the improved model demonstrates the highest accuracy and shortest training time, confirming the theoretical advancements proposed in this paper.