• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1790-1795
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• 2009

For GPR(Ground Penetrating Radar) applications, an accurate analysis of the scattered field is necessary to identify the unknown target. Dyadic Green's function of the multilayered medium is developed and applied to analysis of the underground conducting object. We used method of moment(MOM) with dyadic Green's function, and Discrete Complex Image Method(DCIM). To reduce the computational complexity, fast multipole method is introduced and we showed the accuracy of the method comparing with the conventional method of moment. For investigating the underground conducting target, several numerical experiments were accomplished using this method.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1984-1986
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• 2003

본 논문은 UWB(Ultra WideBand) 기술에 기반한 지반탐사레이더(Ground Penetrating Radar, GPR)용으로 개발된 광대역 안테나의 특성에 관해 나타낸다. UWB 기술의 핵심은 주기가 수 나노초 이하의 짧은 임펄스를 이용하는 것으로 지반탐사레이더는 이 임펄스 기술을 이용함으로서 탐사 능력과 해상도를 높일 수 있다. 임펄스 전송을 위해 안테나는 넓은 대역폭, 크기, 무게, 가격 등 지반탐사레이더에 적용될 수 있는 조건을 만족해야 함으로 안테나의 설계는 이에 부합할 수 있는 다이폴 안테나를 선택하여 개발하였다. 개발된 안테나의 성능을 검증하기 위해 시뮬레이션과 실험을 실시하였고 그 결과 설계된 안테나의 대역폭이 0.5 GHz에서 2.7 GHz로 2.2 GHz(138%)의 사용대역을 나타냄으로서 UWB 기술에 기반한 지반탐사레이더용으로 유용함을 확인하였다.

• Journal of The Geomorphological Association of Korea
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• v.17 no.2
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• pp.87-98
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• 2010

There have been growing concerns for the sea level rise due to global warming in recent years. Sea level rise is a serious problem to densely populated coastal areas, because it may affect the coastal landforms to be damaged. Especially coastal sand deposits like coastal dunes are more sensitive than the other coastal landforms. In this paper, Ground Penetrating Radar (GPR) and Optically Stimulated Luminescence (OSL) dating method were used to identify the Holocene geomorphic changes of coastal dune field in Shinduri located at the western coast. The main results in this study that are the dunefield in the study area may have begun to form at around 6.8 ka and it has grown seaward thereafter. Then, dunefield appears to have extensively developed since 3.7 ka. This result, together with previous works on the sea level and climatic changes in the western coast of Korea suggest that the dunefield has been affected by the sea level regression since the Holocene high stand in the Holocene at around 6 ka and climatic change from warm and humid to cold and dry conditions occurred at 4.5 ka.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.494-499
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• 2007

The ballast, one of a track components, plays an essential role as intermedium in transmitting train traffic-load to subgrade safely, and deterioration of ballast caused by cumulative load effects growth of track irregularity. Especially in the case of Gyeongbu high-speed railway, the deteriorating speed of ballast by dynamic vibration is faster than conventional line because KTX is longer than normal trains in length and it's velocity is very fast with high speed of 300km/h as well. In addition, ballast is a nonlinear material contrary to ordinary metal which has homogeneous property and this property of ballast may cause transformation of ballast. Therefore the theoretical modeling of ballast is quite complicated and it is hard to ensure the reliability of the result. The objective of this paper is to examine the availability of GPR(Ground Penetrating Radar) in estimating the thickness and the degree of deterioration of ballast. First, We figured out the principle of GPR which is the technique of evaluating the condition of ballast and then analyzed data which were measured at Gyeongbu high-speed railway where KTX is running now.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.2
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• pp.19-27
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• 2001

All accurate and efficient antenna feed model is very important for computing GPR response using the FDTD method In literature, there are several feed models such as the equivalent network in angular-frequency domain, 1-D transmission-line cell， voltage boundary condition in time domain, etc. In this paper, theoretical relationship among the models is investigated. It is found that the above three models become equivalent when a short and lossless feed line can match with its connected transmitter receiver). In view of accuracy and efficiency of the simulation, the FDTD results according to the feed models arc compared with the measured data of the receiving responses for an actual GPR system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.385-396
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• 2024

To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3 are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract sinkhole feature in real time.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.56-68
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• 2009

Ongoing and extensive urbanisation, which is frequently accompanied with careless construction works, may threaten important archaeological structures that are still buried in the urban areas. Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) methods are most promising alternatives for resolving buried archaeological structures in urban territories. In this work, three case studies are presented, each of which involves an integrated geophysical survey employing the surface three-dimensional (3D) ERT and GPR techniques, in order to archaeologically characterise the investigated areas. The test field sites are located at the historical centres of two of the most populated cities of the island of Crete, in Greece. The ERT and GPR data were collected along a dense network of parallel profiles. The subsurface resistivity structure was reconstructed by processing the apparent resistivity data with a 3D inversion algorithm. The GPR sections were processed with a systematic way, applying specific filters to the data in order to enhance their information content. Finally, horizontal depth slices representing the 3D variation of the physical properties were created. The GPR and ERT images significantly contributed in reconstructing the complex subsurface properties in these urban areas. Strong GPR reflections and highresistivity anomalies were correlated with possible archaeological structures. Subsequent excavations in specific places at both sites verified the geophysical results. The specific case studies demonstrated the applicability of ERT and GPR techniques during the design and construction stages of urban infrastructure works, indicating areas of archaeological significance and guiding archaeological excavations before construction work.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.74-84
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• 2017

Since a soldier manages a hand-held landmine detector by hands, it is necessary to develop a system that can detect the target quickly and accurately. However, the hand-held landmine detector used in Korea has a problem that it can only detect the metal mines. Therefore, it is important to solve the problem and to develop a hand-held landmine detection system suitable for the Korean environment. In this paper, we propose a hand-held landmine detection system suitable for the Korean environment using ground penetrating radar. The proposed system uses depth compensation, matched filtering, and frequency shifting filtering for preprocessing. Then, in the detection step, the system detects the target using the edge ratio. In order to evaluate the proposed system, we buried landmines in sandy loam which is most of the soil in Korea and obtained a set of ground penetrating radar data by using a hand-held landmine detector. By using the obtained data, we carried out some experiments on the size and position of the patch and the shifting frequency to find the optimal parameter values and measured the detection performance using the optimized values. Experimental results show that the proposed preprocessing algorithms are suitable for detecting all landmines at low false alarm rate and the performance of the proposed system is superior to that of previous works.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.191-194
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• 2008

The cavity behind the tunnel lining, caused by overbrake, might be cause a severe instability during tunnel construction. So backfill grouting is essentially required. GPR(Ground penetrating Radar) is widely used to identify the position and size of the cavity and to verify the effect of the backfill grouting. In this study, GPR survey with 450 MHz antenna was implied to access the effect of the backfill grouting before and after the work to the crown part of ○○ tunnel in Seoul respectively. The result of GPR survey conducted before the backfill, was revealed that cavities behind the lining were existed in the areas of 8 spans. Finally, from the GPR survey implied after backfilling, it was turned out that backfill grouting was successfully carried out. Also, GPR survey was ascertained the better contact between lining and rock base at arrangement of bar span.

• The Journal of Bigdata
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• v.6 no.1
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• pp.197-206
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• 2021

GPR data is used for underground exploration. The data gathered are interpreted by experts based on experience as the underground facilities often reflect GPR. In addition, GPR data are different in the noise and characteristics of the data depending on the equipment, environment, etc. This often results in insufficient data with accurate labels. Generally, a large amount of training data have to be obtained to apply CNN models that exhibit high performance in image classification problems. However, due to the characteristics of GPR data, it makes difficult to obtain sufficient data. Finally, this makes neural networks unable to learn based on general supervised learning methods. This paper proposes an image classification method considering data characteristics to ensure that the accuracy of each label is similar. The proposed method is based on semi-supervised learning, and the image is classified using clustering techniques after extracting the feature values of the image from the neural network. This method can be utilized not only when the amount of the labeled data is insufficient, but also when labels that depend on the data are not highly reliable.