• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2113-2118
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• 2016

The precise detection of electric leakage point of underground power cable is very important to reduce cost and time of maintenance and prevent electric shock accident through expedite repair of electric leakage point. This paper proposes a electric leakage point detection system underground power cable using of half-period modulated transmission waveform and earth electric potential measurement. The developed system is composed of transmitter to generate the wanted pulse waveform, receiver to measure and display earth electric potential by the transmitted pulse in electric leakage point and PC Software program to display of GPS coordinate on detection cable line. The performance of the electric leakage point detection system was tested in the constructed underground cable leakage detection test bed. The test results on signal generation voltage precision of signal transmitter, mean detection earth voltage, mean detection leakage current and electric leakage point detection error showed the developed system can be used in electric leakage point detection underground power cable.

• Spatial Information Research
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• v.18 no.4
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• pp.23-32
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• 2010

Water mains underground facilities are essential components to make up urban infrastructure. In order to manage these water mains underground facilities systematically and scientifically, GIS (Geographic Information System) had been constructed. For the sake of construction of GIS for water mains underground facilities, an exact underground detection and the construction of DB (Data Base) for buried water mains underground facilities should be preceded. In this study, in order to find out the ways to improve exact detection rate of data, the statistical analysis for the causes of detection raw degradation was done, and standardization methods of detection through a case study were suggested, When water mains underground facilities were measured, the detection of non-metallic water pipes was not carried out. The reason was that the results of detection was uncertain and detection was difficult because the assessment of public measurements was vulnerable. Moreover, due to the absence of standardized operating regulations for detection, systematic surveys weren't conducted. In this study, methods to standardize works over the detection of water mains underground facilities were presented so that we can improve the detection rate when we are doing that. As the proposals to improve detection rate, effective performance assessment over non-metallic pipes were presented, and related issues to supplement work regulations of public survey were described systematically.

• International Journal of Railway
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• v.7 no.4
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• pp.94-99
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• 2014

Urban railway systems are located under populated areas and are mostly constructed for underground structures which demand high standards of structural safety. However, the damage progression of underground structures is hard to evaluate and damaged underground structures may not effectively stand against successive earthquakes. This study attempts to examine initial damage-stage and to access structural damage condition of the ground structures using Earthquake Damage Monitoring (EDM) system. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members is obtained from measured acceleration data introduced unsupervised learning recognition. The result showed damage index obtained by damage scenario establishment using acceleration response of selected vulnerable members is useful. Initial damage state is detected for selected vulnerable member according to established damage scenario. Stiffness degrading ratio is increasing whereas the value of reliability interval is decreasing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1819-1823
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• 2005

Incorrect information on public sites can cause serious problem. One of relevant countermeasures against this problem is to detect of buried underground utilities in real time. Although there have been several method to detect of buried underground utilities, such as investigating of gravity and elastic wave and electric field, they have not been so efficient tools. Because it is too expensive and difficult to use. In this paper, magnetic sensors which could provide an easier and more efficient method are used to detect of buried underground utilities. Also fluxgate method of self detection are used. Input signal is used $1\~10kHz$ frequency. Filtering and signal processing of output signal are used labview software. After experiment, detection system of buried underground utilities which used magnetic shows possibility of precise detecting of laying object based on theorectical analysis for electromagnetic field.

• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.35-43
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• 2001

Imposing electromagnetic field using transmitter of buried metal object in skill that detect underground object sensing person atonement in being widowed on the land being magnetized upside numerical value of buried metal object searching way used most widely current by skill be. This paper proposed about mode and detection system of underground object that sense the changed magnetic and judge real radish buried metal object sign of the cook because this treatise forms magnetic in land and design and composition of display device. Also, through simulation of detection system of underground object, showed that can measure radish judgment sign of the cock of underground object

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.27-37
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• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Industrial Technology
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• v.18
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• pp.125-130
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• 1998

The undergound space is widely developed because of dometic industry and protection of enviornment. The existence and exact location of tunnel is very important for stability of the enormous underground storage house or building. Various types of prospecting methods have been applied to detection of underground tunnel. In this study, electrical prospecting method is applied to detect tunnel because the development of underground space is very connected with groundwater. Sensitivity analysis is introduced for the calculation of elctrical inversion data. The governing equation is Fourier transformed into the 2-dimensional wave number space and solved by using the finite element method.