## 4 Experiment
### RNN vs. BO Experiment  
Comparing the BO technique with the previously applied RNN method, the BO showed equal or better performance in terms of the entry time and maximum vibration. The RNN method required more than 5000 tests to train in advance for predicting the vibration performance, making it difficult to apply in practice. The RNN model built a predictive model based on the initial UB values of dehydration and generated the optimal UBT through it. In contrast, the BO method could derive control specifications at a level equivalent to the RNN with a maximum of 500 tests. In terms of the number of tests, the BO method is considered more suitable for automating the control specifications of washing machines compared to the RNN method. For this experiment, the vibration threshold, \(T_{v}\) was reduced to 70% of its original value to meet the stricter vibration requirements under conditions requiring minimal vibration. Figure 11 shows the entry time verification results for the RNN and BO, comparing the entry times in the Light and Medium Load conditions. In the Light Load condition, the RNN method resulted in entry times that were, on average, higher than those achieved by the BO method, and exhibited a wider distribution. In contrast, the BO method recorded relatively shorter entry times, with the data distribution concentrated at lower values. For the Medium Load condition, the BO method demonstrated faster and more stable entry times compared to the RNN. This result indicates that the BO method had faster and more stable entry times in both the Light and Medium Load conditions when compared to the RNN. This result demonstrates that the BO had faster and more stable entry times compared to the RNN in the Light and Medium loads. Figure 12 shows the Max Vibration verification results for the RNN and BO, comparing the maximum vibration values for the Light and Medium Load conditions. Under the \begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Engineer** & **CALS** \\ \hline Automation & Manual & BO-based Timing Automation \\ \hline Development & 3 months & 1 month \\ Period & (Miner development demand) & (Miner development demand) \\ \hline Development Quality & - & Equivalent to Engineer level \\ \hline Development Validation & 1 percent required S per day for testing and analysis & Unmanned possible, \\ \hline Testing & Easy time, vibration level perspective, & Easy time, vibration level perspective, \\ \hline Scope & selective training & full-energy US flight Timing \\ \hline \end{tabular}
\end{table} 
Table 2: The effectiveness of the BO-based automation system (Engineer vs. GAUS). Figure 11: Entry time verification results (RNN vs. BO). Light Load condition, the RNN method showed higher maximum vibration values and a wider distribution compared to the BO method. The BO method had relatively lower vibration values and a narrower distribution, indicating greater stability. In the Medium Load condition, the BO method exhibited lower maximum vibration values and a narrower distribution compared to the RNN, with less variation in vibration values. This result indicates that BO exhibited a more stable vibration control performance compared to the RNN. Table 3 compares the RNN and BO in the Light and Medium Load conditions. The main comparison items are the entry time and vibration amount, consisting of the average, maximum values, whether the criteria were exceeded, Z-values, and the number of tests. For the Light Load condition, the BO method achieved an average entry time of 77 s with a maximum of 245 s, and vibration amounts averaging 6.7 mm with a maximum of 9.1 mm. In comparison, the RNN method had an average entry time of 174 s with a maximum of 501 s, and vibration amounts averaging 10.8 mm with a maximum of 17.7 mm. The T-Test results (Two-Sided) showed statistically significant differences in both entry time (T-Statistic: 6.488, \(p\)-value: 4.1 \(\times\) 10\({}^{-9}\)) and vibration (T-Statistic: 9.191, \(p\)-value: 8.3 \(\times\) 10\({}^{-16}\)).