## 5 Case Study
### Comparison with Other Models  
#### 5.4.1 Comparison with the Model Constructed by 1D Convolution  
To validate the 2D convolution scheme's superior feasibility for the CVD-based metro-TGD identification problem, a 1D MTGDI-CNN (MTGDI-CNN-1D) was reconstructed and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **CAS Train** & **CAS Test** & **FMV Train** & **FMV Test** \\ \hline M-1 & ROS & ROS & ROS & ORI \\ M-2 & ROS & ORI & ROS & ORI \\ M-3 & ORI & ORI & ROS & ORI \\ BASE & ORI & ORI & ORI & ORI \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset-processing mode settings for different strategies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Avg. \(F_{2}^{\text{TGD}}\)** & **Avg. \(F_{1}^{\text{TGD}}\)** & **Avg. \(F_{0.5}^{\text{TGD}}\)** & **Avg. Training Time (GPU Days)** \\ \hline M-1 & 0.8561 & 0.8346 & 0.8141 & 0.0669 \\ M-2 & 0.8538 & 0.8367 & **0.8202** & 0.0755 \\ M-3 & **0.8684** & **0.8428** & 0.8187 & 0.0655 \\ BASE & 0.8043 & 0.8035 & 0.8029 & 0.0250 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance evaluation results.  
validated with the same division as that stated in the previous section. \(F_{1}^{\text{TGD}}\) and \(F_{0.5}^{\text{TGD}}\) were calculated simultaneously to avoid the bias of \(F_{2}^{\text{TGD}}\).  
(1) Setting of 1D model parameters  
The 2D computing operations in MTGDI-CNN were replaced with 1D operations to form the MTGDI-CNN-1D. The sample size was reshaped from \(1\times 80\times 7\) (one-channel 2D data) to \(7\times 80\) (seven-channel 1D data) to fit the model input. The kernel sizes were 3, 5, 7, 11, and 15 in 1D DSCB, 3, 5, 7, and 11 in 1D DiSCB, and 3 in 1D MaxPool and 1D AvgPool. With the void operation, 13 elements were present in the alternative computing operation set of the cell edges. The other settings were identical to those described in the previous section. The experimental modes of the ROS are listed in Table 4.  
(2) Model validation results of 1D model  
First, one experimental mode of CAS was performed (identical CAS settings were used for M-3-1D and BASE-1D), via three random seeds (similar to the methodology stated in the previous section), and three cell architectures were obtained. The average training time was 0.9584 GPU days. Subsequently, the fixed random seed, as stated in the previous section, was leveraged to retrain the model stacked by each cell architecture. Six combinations of the two modes and three random CAS seeds were trained. A comparison between the formulated performance metrics, based on the validating dataset and the values of M-3 stated in the previous section, is summarized in Table 5.  
Evidently, the training time with the same example number in the FMV remained unchanged (upon comparing M-3 with M-3-1D), indicating that 1D convolution and 2D convolution offered considerable computational efficiency. M-3 of the 2D convolution exhibited the highest performance metrics; compared with M-3-1D, the three performance metrics for M-3 were 33.3, 30.0, and 26.8% higher, indicating that 2D convolution is more suitable for CVD-based metro-TGD identification problems. The comparison of M-3-1D and BASE-1D verified the conclusion stated in the previous section, that is, the coping strategy that only uses ROS for the training dataset in FMV could obtain a superior-performing model.