Robust statistical feature extraction and deep learning surrogates for highly accurate guided wave-based determination of global corrosion parameters in metallic plates
- DOI
- 10.1016/j.ultras.2026.108272
- Published
- 2027-01
- Container
- Ultrasonics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ultras.2026.108272,
title = {Robust statistical feature extraction and deep learning surrogates for highly accurate guided wave-based determination of global corrosion parameters in metallic plates},
author = {Slawomir Koziel and Beata Zima and Anna Pietrenko-Dabrowska},
year = {2027},
journal = {Ultrasonics},
doi = {10.1016/j.ultras.2026.108272},
url = {https://doi.org/10.1016/j.ultras.2026.108272}
}RIS
TY - JOUR TI - Robust statistical feature extraction and deep learning surrogates for highly accurate guided wave-based determination of global corrosion parameters in metallic plates AU - Slawomir Koziel AU - Beata Zima AU - Anna Pietrenko-Dabrowska PY - 2027 JO - Ultrasonics DO - 10.1016/j.ultras.2026.108272 UR - https://doi.org/10.1016/j.ultras.2026.108272 ER -
APA
Koziel, S., Zima, B., & Pietrenko-Dabrowska, A. (2027). Robust statistical feature extraction and deep learning surrogates for highly accurate guided wave-based determination of global corrosion parameters in metallic plates. Ultrasonics. https://doi.org/10.1016/j.ultras.2026.108272
Source records
- crossref · retrieved 2026-09-26T15:26:15.006Z