• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.113-130
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• 2021

Recently, ground-penetrating radar (GPR) surveys have been actively employed to obtain a large amount of data on occurrences such as ground subsidence and road safety. However, considering the cost and time efficiency, more intuitive and accurate interpretation methods are required, as interpreting a whole survey data set is a cost-intensive process. For this purpose, GPR data can be subjected to attribute analysis, which allows quantitative interpretation. Among the seismic attributes that have been widely used in the field of exploration, complex trace analysis and similarity are the most suitable methods for analyzing GPR data. Further, recently proposed attributes such as edge detecting and texture attributes are also effective for GPR data analysis because of the advances in image processing. In this paper, as a reference for research on the attribute analysis of GPR data, we introduce the useful attributes for GPR data and describe their concepts. Further, we present an analysis of the interpretation methods based on the attribute analysis and past cases.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.103-105
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• 2002

제 3세대 이동통신 시스템인 UMTS/GPRS는 현재 IPv4를 기반으로 MS(Mobile Station)에게 패킷 서비스를 제공하고 있다. 그러나 3GPP의 Release 2000 IM CN(IP Multimedia Core Network) 서브 시스템에서는 멀티미디어 서비스 지원을 위해 인터넷에 연결되어 있는 모든 MS에게 IP를 할당할 수 있도록 IPv6 지원을 필수로 정의하고 있다. 따라서 본 논문에서는 UMTS/GPRS에서 IPv4 패킷 서비브 뿐만 아니라 IPv6 패킷 서비스를 지원할 수 있도록 UMTS/GPRS에 다양한 IPv6 변환 메커니즘(dual-stack, funneling, transition)을 적용하여 비교한 후 적합한 UMTS/GPRS 네트워크 모델을 제안한다.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.51-60
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• 2010

To determine subglacial topography and internal features of the Fourcade Glacier on King George Island in Antarctica, helicopter-borne and ground-towed ground-penetrating radar (GPR) data were recorded along four profiles in November 2006. Signature deconvolution, f-k migration velocity analysis, and finite-difference depth migration applied to the mixed-phase, single-channel, ground-towed data, were effective in increasing vertical resolution, obtaining the velocity function, and yielding clear depth images, respectively. For the helicopter-borne GPR, migration velocities were obtained as root-mean-squared velocities in a two-layer model of air and ice. The radar sections show rugged subglacial topography, englacial sliding surfaces, and localised scattering noise. The maximum depth to the basement is over 79m in the subglacial valley adjacent to the south-eastern slope of the divide ridge between Fourcade and Moczydlowski Glaciers. In the ground-towed profile, we interpret a complicated conduit above possible basal water and other isolated cavities, which are a few metres wide. Near the terminus, the GPR profiles image sliding surfaces, fractures, and faults that will contribute to the tidewater calving mechanism forming icebergs in Potter Cove.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.171-181
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• 2013

Recently, ground penetrating radar(GPR) has been widely used in detecting metallic and nonmetallic buried landmines and a number of related researches have been reported. A novel preprocessing method is proposed in this paper to flag potential locations of buried mine-like objects from GPR array measurements. GPR operates by measuring the reflection of an electromagnetic pulse from discontinuities in subsurface dielectric properties. As the GPR pulse propagates in the geologic medium, it suffers nonlinear attenuation as the result of absorption and dispersion, besides spherical divergence. In the proposed algorithm, a logarithmic transformed regression model which successfully represents the time-varying signal amplitude of the GPR data is estimated at first. Then, background signals may be densely distributed near the regression model and candidate signals of targets may be far away from the regression model in the time-amplitude space. Based on the observation, GPR signals are decomposed into candidate signals of targets and background signals using residuals computed from the estimated value by regression and the measurement of GPR. Candidate signals which may contain target signals and noise signals need to be refined. Finally, targets are detected through the refinement of candidate signals based on geometric signatures of mine-like objects. Our algorithm is evaluated using real GPR data obtained from indoor controlled environment and the experimental results demonstrate remarkable performance of our mine-like object detection method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.66-71
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• 2019

In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.

• BMB Reports
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• v.51 no.5
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• pp.255-260
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• 2018

Wntless/GPR177 functions as WNT ligand carrier protein and activator of $WNT/{\beta}$-catenin signaling, however, its molecular role in gastric cancer (GC) has remained elusive. We investigated the role of GPR177 in gastric tumorigenesis and provided the therapeutic potential of a clinical development of anti-GPR177 monoclonal antibodies. GPR177 mRNA expression was assessed in GC transcriptome data sets (GSE15459, n = 184; GSE66229, n = 300); protein expression was assessed in independent patient tumor tissues (Yonsei TMA, n = 909). GPR177 expression were associated with unfavorable prognosis [log-rank test, GSE15459 (P = 0.00736), GSE66229 (P = 0.0142), and Yonsei TMA (P = 0.0334)] and identified as an independent risk predictor of clinical outcomes: GSE15459 [hazard ratio (HR) 1.731 (95% confidence interval; CI; 1.103-2.715), P = 0.017], GSE66229 [HR 1.54 (95% CI, 1.10-2.151), P = 0.011], and Yonsei TMA [HR 1.254 (95% CI, 1.049-1.500), P = 0.013]. Either antibody treatment or GPR177 knockdown suppressed proliferation of GC cells and sensitized cells to apoptosis. And also inhibition of GPR177 suppresses in vitro and in vivo tumorogenesis in GC cells and inhibits $WNT/{\beta}$-catenin signaling. Finally, targeting and inhibition of GPR177 with antibody suppressed tumorigenesis in PDX model. Together, these results suggest GPR177 as a novel candidate for prognostic marker as well as a promising target for treatment of GC patients.

• Water for future
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• v.30 no.1
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• pp.143-143
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• 1997

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7473-7482
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• 2013

The G-protein coupled receptor 87 (GPR87) is a recently discovered orphan GPCR which means that the search of their endogenous ligands has been a novel challenge. GPR87 has been shown to be overexpressed in squamous cell carcinomas (SCCs) or adenocarcinomas in lungs and bladder. The 3D structure of GPR87 was here modeled using two templates (2VT4 and 2ZIY) by a threading method. Functional assignment of GPR87 by SVM revealed that along with transporter activity, various novel functions were predicted. The 3D structure was further validated by comparison with structural features of the templates through Verify-3D, ProSA and ERRAT for determining correct stereochemical parameters. The resulting model was evaluated by Ramachandran plot and good 3D structure compatibility was evidenced by DOPE score. Molecular dynamics simulation and solvation of protein were studied through explicit spherical boundaries with a harmonic restraint membrane water system. A DRY-motif (Asp-Arg-Tyr sequence) was found at the end of transmembrane helix3, where GPCR binds and thus activation of signals is transduced. In a search for better inhibitors of GPR87, in silico modification of some substrate ligands was carried out to form polar interactions with Arg115 and Lys296. Thus, this study provides early insights into the structure of a major drug target for SCCs.