Deep-learning-based acoustic pipeline defect detection and characterization in dynamic environments involving varying temperature and fluid flow
- DOI
- 10.1016/j.ultras.2026.108324
- Published
- 2027-02
- Container
- Ultrasonics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ultras.2026.108324,
title = {Deep-learning-based acoustic pipeline defect detection and characterization in dynamic environments involving varying temperature and fluid flow},
author = {Sangmin Lee and Rajendra P. Palanisamy and Do-Kyung Pyun and Alp T. Findikoglu},
year = {2027},
journal = {Ultrasonics},
doi = {10.1016/j.ultras.2026.108324},
url = {https://doi.org/10.1016/j.ultras.2026.108324}
}RIS
TY - JOUR TI - Deep-learning-based acoustic pipeline defect detection and characterization in dynamic environments involving varying temperature and fluid flow AU - Sangmin Lee AU - Rajendra P. Palanisamy AU - Do-Kyung Pyun AU - Alp T. Findikoglu PY - 2027 JO - Ultrasonics DO - 10.1016/j.ultras.2026.108324 UR - https://doi.org/10.1016/j.ultras.2026.108324 ER -
APA
Lee, S., Palanisamy, R. P., Pyun, D., & Findikoglu, A. T. (2027). Deep-learning-based acoustic pipeline defect detection and characterization in dynamic environments involving varying temperature and fluid flow. Ultrasonics. https://doi.org/10.1016/j.ultras.2026.108324
Source records
- crossref · retrieved 2026-11-09T00:46:08.603Z