• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.936-942
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• 2022

While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably degrade the accuracy of existing localization techniques. This paper proposes a proximity detection scheme using low-cost beacons where a handheld mobile device within a moving vehicle autonomously detects its approximate position and moving direction by only observing Received Signal Strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique of multi-directional beams to reduce the impact of the environment, vehicle, and mobile device. A low-cost multi-directional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of 394.8m×304.3m. Experimental results show that the 90th percentile of the average proximity detection error is 0.8m. Furthermore, our proposed scheme provides robust proximity detection performance with various vehicles and mobile devices.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.679-690
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• 2023

Leakage is one of the representative abnormal conditions in Water distribution systems (WDSs). Leakage can potentially occur and cause immediate economic and hydraulic damage upon occurrence. Therefore, leakage detection is essential, but WDSs are located underground, it is difficult. Moreover, when multiple leakage occurs, it is required to prioritize restoration according to the scale and location of the leakage, applying for an optimal restoration framework can be advantageous in terms of system resilience. In this study, various leakage scenarios were generated based on the WDSs hydraulic model, and leakage detection was carried out containing location and scale using a Deep learning-based model. Finally, the leakage location and scale obtained from the detection results were used as a factor for the priority of leakage restoration, and the results of the priority of leakage restoration were derived. The priority of leakage restoration considered not only hydraulic factors but also socio-economic factors (e.g., leakage scale, important facilities).

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.199-210
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• 2008

Acoustic emission(AE)/Microseimsic(MS) activities are low-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is not easy to determine the precursor and initiation stress level of failure in displacement detection method. To overcome this problem, AE/MS techniques for detection of structure failure and damage have recently adopt in civil engineering. In this study, AE/MS monitoring system, which consist of sensor, data acquisition and operation program, is constructed with domestic technology. To verify and optimize the developed system, we are now carrying out the field application at an underground research laboratory and the developed AE/MS monitoring will be used in detecting of seismic events with various scales.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.1
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• pp.18-29
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• 2015

This paper presents a feasibility study of a fresh air load reduction system by using an underground double floor space. The system was introduced into a real building and was examined by the field measurement. Judging from the measurements during three years(1999~2001), the state of the system operation was very stable through this period and it was clear that the system contributes to reduction of energy consumption for air-conditioning. Futhermore, a simulation model used the simple heat diffusion equation was developed to simulate its thermal characteristics and performances. The simulations resulted in air temperature in good agreement with the measurements. Also, from the result of numerical analysis, it is clear that the amount of heat supply by using this system is more than the amount of energy loss to the room above it. Therefore, it is concluded that this systems is very useful and the proposed numerical model can be used for the prediction of system thermal performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.780-785
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• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• The Journal of Engineering Geology
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• v.29 no.4
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• pp.553-563
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• 2019

In order to survey archaeological sites, drilling and excavation are carried out at the final stage. However, at the preliminary stage, non-excavation geophysical prospection is used for assessing underground archaeological ruins. Among the geophysical prospecting techniques, Ground Penetration Radar (GPR) prospection has effectively been applied to historical sites due to its high resolution at shallow depths. In this study, the GPR prospection was conducted to find underground ruins near Suseong-Dang, the place of ancient rituals in Buan area, Korea. First, the GPR prospection was conducted at three sites (Site-1, 2, and 3), and subsequently, the GPR prospection was carried out at Site-3 in more detail. As a result of the prospection, the underground layered structure of the survey area consists of three layers, which are soil layer, weathered rock, and sound rock from the surface. And the GPR anomaly to the archaeological structure was clearly identified at around 100-cm depth showing est-west direction that is parallel to the long-axis array. This GPR anomaly of irregular geomorphological features and intermittent distribution may be related to the ritual remains found in Suseong Dang. The GPR prospection could be effectively used to detect archaeological sites or remains buried in the ground.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.716-722
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• 2017

The present study numerically investigates the effect of obstacles located in the trajectory of fire plume flow on heat flow characteristics by using Fire Dynamics Simulation (FDS) software in an underground combined cycle power plant (CCPP). Fire size is taken as 10 MW and two different locations of fire source are selected depending on the presence of an obstacle. As the results, when the obstacle is in the trajectory of fire plume, hot plume arrives at the ceiling about 5 times slower in the upper of the fire in comparison to the results without obstacle. In addition, the average propagation time of ceiling jet increases by about 70 % with the distance from the ceiling in the upper of the fire, and it increases mainly about 4 times at the distance of 10 m. Consequently, it is noted that the analysis of heat flow characteristics in the underground CCPP considering fire scenarios is essential to develop the fire detection system for initial response on evacuation and disaster management.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.3
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• pp.275-283
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• 2002

To detect the position and depth of buried underground utilities, method of Ground Penetrating Radar(GPR) survey is the most commonly used. However, the skin-depth of GPR is very shallow, and in the places where subsurface materials are not homogeneous and are compose of clays and/or salts and gravels, GPR method has limitations in application and interpretation. The aim of this study is to overcome these limitations of GPR survey. For this purpose the site where the GPR survey is unsuccessful to detect the underground big pipes is selected, and soil tests were conducted to confirm the reason why GPR method was not applicable. Non-destructive high-frequency electromagnetic (HFEM) survey was newly developed and was applied in the study area to prove the effectiveness of this new technique. The frequency ranges $2kHz{\sim}4MHz$ and the skin depth is about 30m. The HFEM measures the electric field and magnetic field perpendicular to each other to get the impedance from which vertical electric resistivity distribution at the measured point can be deduced. By adopting the capacitive coupled electrodes, it can make the measuring time shorter, and can be applied to the places covered by asphalt an and/or concrete. In addition to the above mentioned advantages, noise due to high-voltage power line is much reduced by stacking the signals. As a result, the HFEM was successful in detecting the buried underground objects. Therefore this method is a promising new technique that can be applied in the lots of fields, such as geotechnical and archaeological surveys.