## 5 Classification of Refrigerant Flow Noise Using Support Vector Machine  
To improve the performance of the SVM classification, kernel functions, which can classify refrigerant flow noise without too much calculation, despite high-dimensional mapping, can be a good option. Therefore, this study used the most widely used RBF kernel. The training data targeted to apply the classification of refrigerant noise to the SVM are shown in Table 3. Sound level in frequency refers to feature vectors of input data. The sounds can be classified into slug, churn and annular flow noise depending on SPL (dB) in each frequency. The results of the SVM-based classification are presented in Table 4. The feature vectors are the column of Table 3. The FT overlap value is 85%, FT block size is 1024 and maximum frequency is about 6100 Hz (sampling frequency is 12,200 Hz) with ArtemiSUITE software. The SVM analysis was performed after selecting parameters within a limited range, and those with the maximum classification performance were searched. For the SVM-based classification, \(k\)-fold cross validation was used. The classification accuracy of slug, churn and annular flow noise were 91%, 93% and 90%, respectively. Here, classification accuracy was high in churn because the CWT coefficient at the start and end points of this time intervals was high, and refrigerant noise was clear after the significant fluctuation of signals (Figure 3). Likewise, the CWT coefficient at the start and end of annular noise was the lowest because refrigerant noise was lower than other noises in terms of classification accuracy. Further, the SVM analysis was performed after 4-, 6-, 7- and 8-fold cross-validation in addition to 5-fold cross-validation (Table 5). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Predicted Noise**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{2-2} \cline{4-5}  & & **Slug** & **Churn** & **Annular** & \\ \hline \multirow{3}{*}{**Actual Noise**} & Slug & 456 & 29 & 15 & 91 \\  & Churn & 12 & 660 & 35 & 93 \\ \cline{1-1}  & Annular & 11 & 13 & 225 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from classification of refrigerant flow noise by kernel. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time (s)**} & \multicolumn{4}{c}{**Sound Level in Frequency**} & \multirow{2}{*}{**Class**} \\ \cline{2-2} \cline{4-5}  & **800 Hz** & & **802 Hz** & & **2385 Hz** & \\ \hline
144 & 24.52 & & 26.00 & & 20.30 & Slug \\ \(\vdots\) & \(\vdots\) & & \(\vdots\) & & \(\vdots\) & \(\vdots\) \\
170 & 24.02 & & 25.80 & & 20.20 & Slug \\
198 & 20.34 & & 20.33 & & 20.10 & Churn \\ \(\vdots\) & \(\vdots\) & & \(\vdots\) & & \(\vdots\) & \(\vdots\) \\
232 & 20.30 & & 20.43 & & 20.12 & Churn \\
313 & 18.34 & & 18.40 & & 25.60 & Annular \\ \(\vdots\) & \(\vdots\) & & \(\vdots\) & & \(\vdots\) & \(\vdots\) \\
317 & 18.40 & & 18.30 & & 25.67 & Annular \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training data for support vector machine. When \(k\) was 4, 5, 6, 7 or 8, the SVM classification accuracy was almost the same. In particular, it was all the same when \(k\) was \(\geq 5\). Therefore, this study used 5-fold cross-validation at the classification of refrigerant flow noise with the SVM. To increase the classification accuracy of refrigerant flow noise, it was estimated by increasing the number of SVM feature vectors. The block size of the Fourier transform was increased to increase the number of feature vectors. Then, the estimation was compared to the results of the classification using the SVM without applying input data to the kernel (Table 6). In terms of classification accuracy, the kernel method is more accurate. The classification accuracy increases as the number of feature vectors rises. In addition, if the number of feature vectors increases, frequency resolution declines so that each refrigerant flow noise's frequency characteristics are well detected.