Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models-Reference-Cited by-同舟云学术

Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

Published:2023 Issue: Volume:11 Page:13309-13323
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yurt Reyhan¹^ORCID,Torpi Hamid²,Mahouti Peyman³^ORCID,Kizilay Ahmet²,Koziel Slawomir⁴^ORCID

Affiliation:

1. Department of Electrical-Electronics Engineering, Yalova University, Yalova, Turkey

2. Department of Electronic and Communication Engineering, Yıldız Technical University, Istanbul, Turkey

3. Department of Avionics, Yıldız Technical University, Istanbul, Turkey

4. Department of Technology, Engineering Optimization & Modeling Center, Reykjavik University, Reykjavik, Iceland

Funder

Türkiye Bilimsel Ve Teknolojik Araştirma Kurumu (TÜBİTAK) through the TÜBİTAK-BİDEB (Bilim İnsanı Destek Programları Başkanlığı) 2011—A International Ph.D. Fellowship Program

Icelandic Centre for Research

Narodowe Centrum Nauki

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10038643.pdf?arnumber=10038643

Reference47 articles.

1. Application of Convolutional and Recurrent Neural Networks for Buried Threat Detection Using Ground Penetrating Radar Data

2. Multi-kernel Gaussian processes;melkumyan;Proc 22nd Int Joint Conf Artif Intell,2011

3. Recognizing subsurface target responses in ground penetrating radar data using convolutional neural networks;sakaguchi;Proc SPIE,2015

4. Two-Stage Framework for Efficient Gaussian Process Modeling of Antenna Input Characteristics

5. GPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Study on the temperature field and infrared characteristics of unsaturated soils with shallow buried objects at different depths;International Journal of Thermal Sciences;2024-11

2. Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects;Scientific Reports;2024-06-28

3. A Deep Learning Strategy for Multipath Ghosts Filtering via Microwave Tomography;IEEE Transactions on Geoscience and Remote Sensing;2024

4. Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna;Scientific Reports;2023-08-03

5. A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data;Applied Sciences;2023-07-07