• Geophysics and Geophysical Exploration
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• v.14 no.2
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• pp.140-145
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• 2011

In order to analyze the influence of the noise on a cross-well traveltime tomography to detect a small scale low velocity body in a homogeneous medium, the first arrival travel times were computed one a tunnel model by a finite-difference ray tracing scheme. Three different types and four different intensity levels of white noises were added to the computed first arrival travel times, and velocity tomograms were constructed using an iterative inversion method (SIRT). Tomograms with the noise intensity up to 10% of the maximum traveltime delay in the tunnel model, showed the exact location of the tunnel. However, the velocity shown at the tunnel location was not close to air velocity but only slightly less than the velocity of the background medium. The additive random noise showed significantly less degree of influence on the resulting tomogram than the source- and receiver consistent noise.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.477-480
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• 2006

IPv6는 IPv4의 주소 부족을 해결하기 위해 1998년 IETF에서 표준화된 프로토콜이다. 현재 IPv4가 수축으로 되어 있는 인터넷을 동시에 IPv6로 전환하는 것은 불가능하므로 IPv4/IPv6 혼재네트워크를 거쳐 IPv6 순수 망으로 전환될 것이다. 본 논문에서는 혼재네트워크에서 IPv4 망과 IPv6 망간의 통신을 가능하게 해주는 IPv6 전환 메커니즘 중 터널링 방식에 대해 기술하고, 보안 취약성을 테스트하기 위해 동일한 보안 취약성에 대해 각각 IPv4 패킷, IPv6 패킷, 터널링된 패킷을 캡쳐할 수 있는 구축방안을 제안한다. 제안된 방식은 IPv4, IPv6, 터널링 패킷에 대한 분석이 가능하므로 IPv6 지원을 계획하는 침입탐지, 침입차단 시스템에 활용이 가능하다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.253-263
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• 2021

Concrete structures are damaged by aging and external environmental factors. This type of damage is to appear in the form of cracks, to proceed in the form of spalling. Such concrete damage can act as the main cause of reducing the original design bearing capacity of the structure, and negatively affect the stability of the structure. If such damage continues, it may lead to a safety accident in the future, thus proper repair and reinforcement are required. To this end, an accurate and objective condition inspection of the structure must be performed, and for this inspection, a sensor technology capable of detecting damage area is required. For this reason, we propose a deep learning-based image processing algorithm that can detect spalling. To develop this, 298 spalling images were obtained, of which 253 images were used for training, and the remaining 45 images were used for testing. In addition, an improved loss function and data augmentation technique were applied to improve the detection performance. As a result, the detection performance of concrete spalling showed a mean intersection over union of 80.19%. In conclusion, we developed an algorithm to detect concrete spalling through a deep learning-based image processing technique, with an improved loss function and data augmentation technique. This technology is expected to be utilized for accurate inspection and diagnosis of structures in the future.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.549-553
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• 2009

인터넷의 빠른 발전과 확산에 따라, 통신은 방송, 통신, 인터넷 등 개별 미디어 융합을 기반으로 IP 기반 융합네트워크로 발전해 나가고 있다. 네트워크의 발전과 함께 보안문제는 네트워크 관리에 있어서 매우 중요시 되고 있는데, 특히 트래픽 폭주를 일으키는 분산공격트래픽 공격에 대한 방어는 필수적이라 할 수 있으며, 분산공격트래픽 공격에서는 알려진 패턴에 대한 유해트래픽의 방어뿐만 아니라 알려지지 않은 새로운 패턴의 유해트래픽에 대한 보안관리가 모두 필요하다. 현재 인터넷에 대한 공격을 차단할 수 있는 방어 기술은 싱크홀(sinkhole) 터널링(tunneling) 기술 등이 제공되고 있으나, 싱크홀 라우터를 따로 만들지 않고 알려지지 않은 패턴의 유해트래픽에 대한 실시간 탐지 및 대응 방법은 정의되고 있지 않다. 본 논문에서는 분산공격트래픽, 바이러스 등을 모두 포함하는 유해트래픽의 실시간 탐지 및 대응 방법에 대하여 제안한다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.181-186
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• 2005

Gravity method produces subsurface density distribution, which is direct information of soundness of basement. Therefore, microgravity is one of the most effective method for detections of limestone cavities, abandoned mine-shafts and other tunnels, The paper show the effectiveness of microgravity by three different field cases.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.515-528
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• 2020

Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.

• Convergence Security Journal
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• v.8 no.1
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• pp.19-26
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• 2008

In wireless sensor networks, an adversary can launch the wormhole attacks, where a malicious node captures packets at one location and tunnels them to a colluding node, which retransmits them locally. The wormhole attacks are very dangerous against routing protocols since she might launch these attacks during neighbor discovery phase. A strategic placement of a wormhole can result in a significant breakdown in communication across the network. This paper presents a compromise-resilient tunneled packet filtering method for sensor networks. The proposed method can detect a tunneled message with hop count alteration by a comparison between the hop count of the message and one of the encrypted hop counts attached in the message. Since the proposed method limits the amount of security information assigned to each node, the impact of wormhole attacks using compromised nodes can be reduced.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.561-573
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• 2023

Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.