Numerical and experimental generated data during project https://doi.org/10.1038/s41598-024-65996-0 - Open Research Data - Bridge of Knowledge

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Numerical and experimental generated data during project https://doi.org/10.1038/s41598-024-65996-0

Description

The dataset was generated using a deep-learning-based surrogate modeling technique for characterizing buried objects using 3-D full-wave electromagnetic simulations of a GPR model. The task was 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. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for the time-frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm, and 4.7%, 11.6%, respectively.

Dataset file

metadata_p13_complete.pdf
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License:
Creative Commons: by 4.0 open in new tab
CC BY
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Details

Year of publication:
2025
Verification date:
2025-03-17
Dataset language:
English
Fields of science:
  • automation, electronics, electrical engineering and space technologies (Engineering and Technology)
DOI:
DOI ID 10.34808/b14p-4585 open in new tab
Funding:
Verified by:
Gdańsk University of Technology

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