## 4 Case Studies: Part I: Simulation Analysis
### Performance Comparison with Advanced PQD Classification Methods
#### 4.4.2 Performance Comparison with Advanced PQD Classification Algorithms
Meanwhile, in terms of efficiency performance, since the manual feature extraction methods generally need to perform complex time-frequency feature decoupling and high-dimensional component separation operations, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Hyperparameters**} \\ \cline{2-4}  & **Item** & **Range** & **Values** \\ \hline \multirow{8}{*}{DNN} & Batch size & 16, 32, 64, 128 & 32 \\  & Convolutional kernel size & (1,1), (3,3), (5,5) & (3,3) \\  & Activation function & ReLU, Tanh, Sigmoid & Tanh \\  & BN layer & With or without & With \\  & Fully connected layer number & 1, 2, 3, 4 & 3 \\  & Optimizer & SGD, Adam, Nadam & Nadam \\  & Dropout rate & 0, 0.2, 0.4, 0.6 & 0 \\ \hline \multirow{8}{*}{CNN} & Batch size & 16, 32, 64, 128 & 64 \\  & Convolutional kernel Size & (1,1), (3,3), (5,5) & (5,5) \\  & Activation function & ReLU, Tanh, Sigmoid & ReLU \\  & Fully connected layer number & 1, 2, 3, 4 & 1 \\  & Optimizer & SGD, Adam, Nadam & Adam \\ \hline \multirow{2}{*}{ResNet18} & Batch size & 16, 32, 64, 128 & 64 \\  & Activation function & ReLU, Tanh, Sigmoid & ReLU \\  & The optimizer & SGD, Adam, Nadam & SGD \\ \hline \multirow{2}{*}{ResNet50} & Batch size & 16, 32, 64, 128 & 64 \\  & Activation function & ReLU, Tanh, Sigmoid & ReLU \\  & Activation function & ReLU, Tanh, Sigmoid & ReLU \\  & The optimizer & SGD, Adam, Nadam & SGD \\ \hline \multirow{2}{*}{Proposal} & Batch size & 16, 32, 64, 128 & 64 \\  & Activation function & ReLU, Tanh, Sigmoid & ReLU \\  & Optimizer & SGD, Adam, Nadam & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**Size (MB)**} & \multirow{2}{*}{**FLOPs (G)**} & \multicolumn{2}{c}{**Time (ms)**} & \multirow{2}{*}{**Accuracy**} \\ \cline{4-5}  & & & & **Signal** & & **Model** \\  & & & & **Processing** & & **Classification** \\  & & & & **(ms)** & & **(ms)** \\ \hline DWT\&SVM [6] & 10.92 & 85.65 & 32.72 & 16.83 & 13.82 & 88.75 \\ ST\&RF [7] & / & 1014.17 & / & 59.44 & 29.38 & 84.91 \\ VMD\&LGBM [8] & / & 7.29 & / & 24.26 & 11.94 & 89.28 \\ FCN-LSTM [9] & 1.72 & 6.58 & 0.54 & / & 18.37 & 77.51 \\ DNN [10] & 0.17 & 0.64 & 0.11 & / & 16.43 & 90.23 \\ GAF\&CNN [11] & 1.53 & 5.83 & 0.13 & 109.93 & 9.64 & 91.56 \\ TC\&ResNet18 [12] & 11.19 & 42.73 & 1.82 & 34.75 & 41.90 & 95.63 \\ ITC\&ResNet50 [13] & 23.57 & 90.11 & 4.13 & 57.71 & 74.33 & 96.78 \\ Proposed & 1.88 & 6.94 & 0.22 & 12.26 & 11.06 & 98.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comprehensive performance comparison between the proposed algorithm and advanced classification algorithms in terms of accuracy and efficiency. their signal processing efficiency is lower than that of the proposed LT because the latter only needs to construct an ideal signal for simple curve synthesis. Furthermore, due to the lightweight architecture design of L-DenseNet-CBAM, its classification efficiency is also higher than that of the three machine learning classifiers. One-dimensional deep learning algorithms directly take sequence data as model input without any data processing, thus outperforming other algorithms in terms of efficiency performance. However, their limitations in information mining of complex temporal sequence data still constrain their recognition accuracy for complex PQDs. Based on the deep feature mining capabilities and strong generalization of image recognition networks, the accuracy performance metrics of two-dimensional deep learning algorithms are superior to those of machine learning-based algorithms and one-dimensional deep learning algorithms. The proposed algorithm can achieve a classification accuracy of 98.05%, significantly outperforming GAF&CNN, TC&ResNet18, and ITC&ResNet50.