## 3 Parameter Selection Experiment
### Selection of Feature Parameters
#### 3.3.2 Second Step Selection  
(1) Selection of clustering algorithm  
Energy and duration parameters were taken as instances to choose the optimal algorithm among K-means, linkage, and gmdistribution, using the algorithm switching rule that was mentioned above. In order to determine the optimum number of clusters, the enumeration method was used to calculate the silhouette value when the cluster was 2, 3, 4, and 5, respectively, and the results are shown in Figure 6a,b.  
The average silhouette value using those three algorithms under different \(K\) values to intuitively get the optimum number of clusters was calculated, and the results are shown in Figure 6c. For the Energy parameter, the maximum silhouette value can be obtained when the K value is 5, and when the K value is 4 for the duration parameter. The silhouette value performance and main parameters used in the algorithm switching of those three clustering algorithms are shown in Figure 6d and Table 2, respectively (with five clusters for the Energy parameter and four clusters for the Duration parameter).  
Figure 6: (**a,b**) show the silhouette values using different algorithms in different clusters, where (**a**) is the result of the energy parameter, and (**b**) is the result of the duration parameter. (**c**) shows the mean value of silhouette value with different algorithms. (**d**) shows the performance of different algorithms in terms of silhouette value.  
2. Clustering analysis  
Set the number of clusters to 5 due to there being five moisture contents. The clustering results are shown in Figure 7, where the abscissa is the sample number and the ordinate is the parameter value after standardized operation.  
The label correlations \(E\) of the above five parameters are calculated, and the results are shown in Figure 8.  
RiseTime is discarded, because its label correlation is bigger than the threshold \(E_{\textit{filter}}\), which is set to 300. The Arrival Time and the Rise Time were discarded among the six original parameters after two-step selection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature** & **Parameter** & **Parameter** & **Feature** & **Parameter** & **Parameter** & **Parameter** & **Parameter** \\
**Name** & **List** & **Value** & **Name** & **List** & **Value** & **Name** & **Value** \\ \hline \multirow{6}{*}{\(X_{\textit{at}}\)} & \(B_{\textit{i}\Delta}\) & 0.565 & & \(B_{\textit{u}\Delta}\) & 0.561 & \(\frac{1}{2}(M_{\textit{u}1}+M_{\textit{u}1})\) & 0.8425 \\  & \(B_{\textit{i}\Delta}\) & 0.857 & & \(B_{\textit{u}\Delta}\) & 0.573 & \(\frac{1}{2}(M_{\textit{u}1}+M_{\textit{u}1})\) & 0.8425 \\  & \(B_{\textit{i}\Delta}\) & 0.86 & & \(B_{\textit{u}\Delta}\) & 0.525 & \\  & \(B_{\textit{i}\Delta}\) & 0.857 & & \(B_{\textit{u}\Delta}\) & 0.807 & \(\frac{1}{2}(M_{\textit{u}2}+M_{\textit{u}2})\) & 0.83 \\  & \(K_{\textit{u}}\) & 4 & \(K_{\textit{u}}\) & 5 & \\  & \(M_{\textit{u}1}\) & 0.805 & & \(M_{\textit{u}21}\) & 0.88 & \(\frac{1}{2}(M_{\textit{u}3}+M_{\textit{u}3})\) & 0.48 \\  & \(M_{\textit{u}2}\) & 0.79 & & \(M_{\textit{u}2}\) & 0.87 & \\  & \(M_{\textit{u}3}\) & 0.1 & & \(M_{\textit{u}3}\) & 0.86 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of parameters in algorithm switching.  
Figure 8: Label correlations of different parameters.  
Figure 7: Output of clustering. (**a**) is Energy parameter; (**b**) is Duration parameter; (**c**) is Counts Number parameter; (**d**) is Rise Time parameter; (**e**) is Peak Amplitude parameter.