## 3 Results  
Figure 3 presents the load-deflection curve with c1 and c3 AE signal features for CFE and GFE samples. CFE samples achieved, on average, a 15% higher maximum flexural load with significantly higher sample deflection than GFE samples. The load dropped sharply after the CFE specimen broke, while in the case of glass fibers that are 5 times smaller in diameter than carbon fibers, the load after sample breakage gradually decreased with accompanying fiber breakage. A clear trend of increasing amplitude and energy of AE signals with increasing load for both types of samples is observed. When the CFE sample breaks, the highest amplitudes and energies of AE signals are measured, while in the case of GFE samples, after sample breaking the values of the amplitude and energy remain high. In the tests, AE signals up to a 10% drop in load after fracture were analyzed. Deep features were extracted from continuous wavelet transform (complex Morlet wavelets) scalograms using a convolutional autoencoder (CAE). Different CAE configurations influence deep feature extraction and consequently classification accuracy for DT and DA classifiers. The results of the investigation of various CAE configurations as feature extractors and evaluation of the performance of classification for CFE and GFE composites are summarized in Tables 1-3. The tables are structured as follows:  
* The first column presents the architecture of the deep autoencoder that was applied to generate the corresponding deep features. * The second and the third column present the selected features, listed in order of importance, as chosen by DT and DA classifiers. * The last two columns denote the classification accuracy obtained by DT and DA methods. Figure 3: Load-deflection curve with a peak amplitude and burst signal energy values for CFE sample (**a**) and GFE sample (**b**). In the upper left part of the diagrams are optical microscope photos of the sample cross-sections. Table 1 shows the results of applying only standard features. In this case, the accuracy measures around 80% were obtained by both classifiers. Table 1 reveals differences in the selection of relevant features, which are due to different partitioning strategies of DT and DA classifiers, and it also indicates the absence of highly relevant features that would be simultaneously selected by both classifiers. Table 2 shows results obtained using only deep features. These results are significantly better compared to those shown in Table 1. Comparative analysis also indicates good consistency of feature selection by both classifiers, which denotes good predictive power of selected deep features. In this case, the best accuracy of 91.3% was obtained by both classifiers. Table 3 presents results where both feature sets (standard and deep features) were available as classifier inputs. This case reveals only slight improvement compared to Table 2, which means that deep features already accurately represent the information relevant for the defined classification task. Figure 4 shows the classification accuracies for all three feature sets and both classifiers. Deep and combined features provide a similar result that significantly outperforms the result obtained using only standard features.