Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects
Yazarlar (5)
Dr. Öğr. Üyesi Reyhan YURT Kırşehir Ahi Evran Üniversitesi, Türkiye
Doç. Dr. Hamid Torpi Yıldız Teknik Üniversitesi, Türkiye
Prof. Dr. Ahmet Kızılay Yıldız Teknik Üniversitesi, Türkiye
Slawomir Koziel Reykjavík University, İzlanda
Prof. Dr. Peyman Mahouti Yıldız Teknik Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Scientific Reports (Q1) | ||
| Dergi ISSN | 2045-2322 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2024 |
| Cilt / Sayı / Sayfa | 14 / 1 / 14898– | DOI | 10.1038/s41598-024-65996-0 |
| Makale Linki | http://dx.doi.org/10.1038/s41598-024-65996-0 | ||
| UAK Araştırma Alanları |
Elektromanyetik, Mikrodalga ve Anten Teknolojileri
Haberleşme
Yapay Zeka
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| Özet |
| In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the … |
| Anahtar Kelimeler |
| Artificial intelligence | Buried object characterization | Deep regression network | Ground penetrating radar (GPR) | Surrogate modeling | Time frequency spectrogram |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 3 |