# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 4 Deep-Learning (DL) Methods for AE Testing
### Convolutional Neural Network (CNN) Solutions  
The architecture of CNNs differs from the common model of neural networks since the intermediate layers are not completely connected (Figure 5). The input level receives the data that are supplied to the network and is sized according to the specific characteristics of the input data [68]. The convolutional level follows, from which the network takes its name, which precisely carries out a convolution operation to recognize specific characteristics in the data. There may be more convolutional levels depending on the complexity of the characteristics to be recognized. Subsequently, the pooling level can reduce the dimensionality by eliminating what is superfluous. Finally, the output level necessary for the classification consists of a completely connected level that connects all neurons to classify the characteristics identified by the previous levels [69]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Input** & **ANN Architecture** & **Results** \\ \hline [PERSON] et al. [56] & AE from & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [56] \\ laminate \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} AE from reinforced \\ polymer \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Back-propagation-- \\ hyperbolic tangent \\ activation function \\ \end{tabular} } & \multirow{2}{*}{96\%} \\  & & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [58] \\ discharge \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} AE from partial \\ resilient--sigmoid \\ activation function \\ \end{tabular} } & \multirow{2}{*}{51.1–98.6\%} \\  & AE from & & \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [59] \\ plastic \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} AE from aluminium \\ oxide grinding wheel \\ in dressing operation \\ AE from deep piles \\ AE from from \\ astenempering of \\ steels \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Back-propagation \\ AE from \\ astenempering of \\ steels \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 100\% \\ Back-propagation \\ \end{tabular} } \\  & AE from fiber–cement \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [63] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} AE from thermally \\ modified wood \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Back-propagation \\ method of data \\ handling, linear vector \\ quantization \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 89.1–91.1\% \\ quantization \\ \end{tabular} } \\  & AE from bearing & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ANN-based methods for AET. The convolutional level carries out a main operation, namely convolution, which consists of the product of two functions, one delayed compared with the other. This operation can be considered as the application of a filter consisting of a matrix (kernel) of smaller dimensions than the input data on which it is applied. The application of the matrix is a scalar product between the kernel weights and so-called receptive field, a subset of data having the same dimensions as the kernel. To perform the convolution on all data, the filter is shifted by an amount equal to the pitch (stride) until it reaches the edge [70]. At the end of the scan, another matrix, called a feature map, is obtained that highlights a particular characteristic of the data. Therefore, to carry out the recognition, multiple filters are used at the same time; this will produce a tensor at the output whose depth is equal to the number of filters used. Each filter involves a few synaptic values (weights) equal to the size of the kernel; the number of parameters does not depend on the size of the image but can be calculated [71]. The output generated by the convolutional levels allows for more detailed and consistent information than the starting image.