Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
Yazarlar (5)
Dr. Öğr. Üyesi Reyhan YURT Yalova Üniversitesi, Türkiye
Doç. Dr. Hamid Torpi Yıldız Teknik Üniversitesi, Türkiye
Prof. Dr. Peyman Mahouti Yıldız Teknik Üniversitesi, Türkiye
Prof. Dr. Ahmet Kızılay Yıldız Teknik Üniversitesi, Türkiye
Slawomir Koziel Reykjavík University, İzlanda
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q1) | ||
| Dergi ISSN | 2169-3536 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2023 |
| Cilt / Sayı / Sayfa | 11 / 1 / 13309–13323 | DOI | 10.1109/ACCESS.2023.3243132 |
| Makale Linki | http://dx.doi.org/10.1109/access.2023.3243132 | ||
| UAK Araştırma Alanları |
Elektromanyetik, Mikrodalga ve Anten Teknolojileri
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| Özet |
| This work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling approach for buried objects characterization, (ii) construction of the surrogate model in a computationally efficient manner using small training datasets, (iii) development of a novel deep learning method, time-frequency regression model (TFRM), that employes raw signal (with no pre-processing) to achieve competitive estimation performance. The presented approach is favourably benchmarked against the state-of-the-art regression techniques, including multilayer perceptron (MLP), Gaussian … |
| Anahtar Kelimeler |
| A-scan data analysis | artificial intelligence | Buried object characterization | ground penetrating radar (GPR) | microwave modeling | surrogate modeling |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 10 |
| Scopus | 12 |
| Google Scholar | 13 |