# A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion
## 4 Experimental Setup
### Recognition Accuracy with Different Expanded Sample Sizes  
The generated samples and the real samples are further combined into a new training sample set and then applied to acoustic fault identification. A Multi-layer Perceptron (MLP) artificial neural network [22] is employed as the classifier. The classifier performance of the proposed algorithm is tested on the optimum sample size and also compared with different expanded sample sizes. The total number of generated samples is 1000 for each class. The expanded training dataset consists of two parts, 800 real samples and some expanded samples, which have been rearranged according to the descending order of their confidences. Based on Equation (7), the optimum sample size for three classes are \(n_{1}\) = 831, \(n_{2}\) = 538, \(n_{3}\) = 282 respectively. Design contrast experiments as: \(E_{1}\): the optimum size; \(E_{2}\): half the optimum size; \(E_{3}\): double the optimum size, if the size is larger than 1000, then 1000 is chosen; \(E_{4}\): all 1000 samples; \(E_{5}\): none. The classifiers' accuracies of these different expanded sample sizes are compared and listed in Table 1.  
Figure 2: Frequency domain plots of the measured signals with main vibration frequencies (**a**) 90 Hz; (**b**) 296 Hz; (**c**) 360 Hz and the generated signals with main vibration frequencies; (**d**) 90 Hz; (**e**) 296 Hz; (**f**) 360 Hz.  
In Table 1, the accuracies (83.00%, 82.67%, 81.67%, 83.00%) for the first four experiments are significantly higher than the accuracy with no expanded samples addition (61.76%). This shows that the AFGAN for sample expansion is effective. At the optimum sample size, the accuracy reaches the highest 83.00%, half or double the optimum sample size leads to the decrease of the accuracy. When the sample size is chosen 1000 per class, the model's accuracy also reaches 83.00%, which is the same as that of the optimum size. This shows that all those generated quality samples are capable of providing a high identification performance, but there is a trade-off between the number of samples and the time for classification. Thus, sample size control is necessary.  
Furthermore, the relationships among the classier performance, the expanded sample arrangement and the sample size are investigated. Three expanded sample arrangement schemes, random, descending and ascending order are carried out. Figure 3 illustrates the classifier accuracies as the sample size variation on these three schemes. It is clear that the accuracy of the descending order outperforms the ascending one. To reach the accuracy 83.00%, which is obtained based on the optimum size, the n1, n2 and n3 need to be 500 in the descending order scheme, while the ascending one needs all the 3000 samples. This behaviour demonstrates that the descending order rearrangement can ensure the high confidence samples be selected preferentially so that the high accuracy can be achieved through small sample size. It is also worth pointing out that, for the descending order scheme in sample size range [850, 900], the accuracy reaches the 83.33%, which is a little higher than the accuracy when the sample size is optimal, in the sense that an additional increase in sample size does not lead to further sensible improvements in classifier performance. Thus, the results demonstrate that the performance of the size-controlled AFGAN can be considered satisfactory with the optimum sample size.