## 7 Conclusions  
This study presents a comprehensive approach to acoustic emission (AE) source localization, leveraging a novel single fiber-optic coil-based sensing system, advanced signal processing techniques, and data augmentation with GAN. Our hybrid methodology, combining GANs for data augmentation with an adapted Inception network for localization, demonstrated substantial increases in AE source localization accuracy in bounded isotropic plate-like structures. The t-SNE visualizations and Wasserstein distance analysis confirm the high fidelity of the synthetic data generated by the GAN, leading to improved model accuracy and robustness. The comparison of error distributions highlights the superiority of GAN-based augmentation over noise-based augmentation and the original dataset, illustrating an evident reduction in localization errors and improved consistency across various source locations, crucial for detecting fatigue cracks and corrosion in metallic plate structures. The implications of these findings are extensive, potentially transforming struc  
Figure 9: The comparison of errors for the different methods.  
tural health monitoring practices across various industries. By addressing the challenges of limited and imbalanced datasets, our approach paves the way for more reliable and efficient damage detection and localization in complex infrastructures. While our study focused on a specific, thin aluminum plate-like structure, future research should extend this methodology to more diverse real-world scenarios, including larger structures of different materials and additive manufacturing. Further exploration of advanced signal processing techniques and alternative deep learning architectures could potentially enhance model performance, particularly in applications such as ship hulls, bridge girders, aircraft wings, and fuselages. In conclusion, the hybrid deep learning architectures we developed here are for more accurate, reliable, and efficient damage detection and localization in plate-like structures, thus contributing to safer and more sustainable infrastructure management. This single-sensor configuration enables precise localization without the need for a multiple sensor array, offering a promising solution for enhancing AE-based SHM in complex infrastructures and improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.  
Conceptualization, X.H. and Y.D.; methodology, X.H.; software, X.H.; validation, X.H.; formal analysis, X.H.; investigation, X.H.; resources, X.H.; data curation, X.H.; writing--original draft preparation, X.H.; writing--review and editing, X.H. and M.H.; visualization, X.H.; supervision, M.H. and Y.D.; project administration, M.H. and Y.D.; funding acquisition, M.H. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the US Office of Naval Research under Award Nos. N00014-20-1-2649, N00014-21-1-2273 and N00014-22-1-2321.  
Not applicable.  
The code used in this study is publicly available at [[https://github.com/Xuhui94/Hybrid-DL-for-AE-source-localization](https://github.com/Xuhui94/Hybrid-DL-for-AE-source-localization)]([https://github.com/Xuhui94/Hybrid-DL-for-AE-source-localization](https://github.com/Xuhui94/Hybrid-DL-for-AE-source-localization)) (accessed on 10 August 2024). The data used in this study has limited access due to privacy/ethical restrictions. Requests for data access can be directed to the corresponding author.  
The authors would like to express their gratitude for the research support provided by the US Office of Naval Research under Award Nos. N00014-20-1-2649, N00014-21-1-2273, and N00014-22-1-2321, and technical guidance from the Program Manager, [PERSON]. The authors are also grateful to [PERSON] for her assistance in collecting the data which used fiber-optic AE sensors.  
The authors declare no conflicts of interest.