# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 7 Conclusions  
The application of classification based on the SOM has potential for classifying straight propagation waves. Thus, conventional AE source localizations are expected to perform in heterogeneous elastic wave velocity distributions if classified waves are applied. Although the TDOA method is expected to have reduced visualized sources because the classified waves have the potential to compute the determinant of 0, the application of ray tracing localizes sources with lower accuracy in comparison with sources localized by the TDOA method if the interval of the candidates is too low. In order to validate the performance of the source localizations with the classified waves, the classified waves were applied to the TDOA method and the ray tracing in this study. Moreover, the model tests based on PLB were conducted to validate the performance of the source localizations. Furthermore, the AEs used in the SOM were validated to evaluate the localization accuracy since the results of the SOM depend on the selected AEs. The conclusions obtained from the model test are listed as follows:  
* Selected waves measured at the vertex of the specimen were applied to the SOM. According to the results of the AE source localizations assuming homogeneous velocity distributions, the accuracy of localized sources in heterogeneous velocity distributions was improved by the use of the waves classified on the basis of the map. Therefore, it is confirmed that this selection of waves for the SOM is appropriate in the classification of elastic waves. It should be noted that this selection of the measured waves assumes that the source localizations are conducted in materials that were locally damaged.
* In the particular location of sensors, AE source localization based on ray tracing localizes a larger number of sources in comparison with the computation of the TDOA method because of the divergence in the least squares method used in the TDOA method. Moreover, the TDOA method generates maximum errors in the comparison of the source localizations. Therefore, it is confirmed that the source localization based on ray tracing is appropriate in the application of classified waves if the interval of the candidates is 20 mm.  
* The computational times of ray tracing are huge, and the optimization of the computation time is required. In the application of classified waves, approximated ray paths are not required in the source localization because straight propagation waves are classified. Thus, the computation time is expected to be optimized.  
In the use of machine learning for source localization, unsupervised learning methods should be applied for versatility because the characteristics of AE signals depend on the sources, the propagated materials, and the measurement system. However, supervised learning methods have been applied to the localizations for NDT [33; 34; 35]. In future works, the classification of elastic waves based on the SOM are going to be validated in comparison with supervised learning methods to show its superiority.  
Conceptualization, K.N.; methodology, K.N.; software, K.N.; validation, K.N.; formal analysis, K.N.; investigation, K.N., Y.K., K.O. and S.S.; resources, Y.K. and K.O.; data curation, K.N.; writing--original draft preparation, K.N.; writing--review and editing, Y.K., K.O. and S.S.; visualization, K.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.