• Journal of Information Processing Systems
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• v.19 no.6
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• pp.745-755
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• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1734-1737
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• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of somke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility form the fire. We need to develope a new high precision smoke detection system to keep expensive industrial facilities most reliably form fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke particles in the air. It is operated continously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities, such as power plants, underground common tunnel, main control rooms, computer rooms etc.

• Journal of Korean Society of Disaster and Security
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• v.7 no.2
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• pp.35-41
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• 2014

If a disaster occurs in the underground like subway, disaster response system should minimize the casualties. It must quickly guide passengers to a safe evacuation route. But sometimes the system does not work properly. And then they need distributed disaster response system which make decision autonomously. We perform conceptual research about distributed autonomous decision-making disaster detection and response system and disaster detection method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.128-132
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• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of smoke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility from the fire. We need to develope a new high precision smoke detection system to keep expensive industial facilities most reliably from fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke psrticles in the air. It is operated continuously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities,such as power plants, underground common tunnel,main control rooms,computer rooms etc.

• Smart Structures and Systems
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• v.6 no.3
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• pp.239-262
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• 2010

Tube Lines has carried out a "knowledge and investigation programme" on the deep tube tunnels comprising the Jubilee, Northern and Piccadilly lines, as required by the PPP contract with London Underground. Many of the tunnels have been in use for over 100 years, so this assessment was considered essential to the future safe functioning of the system. This programme has involved a number of generic investigations which guide the assessment methodology and the analysis of some 5,000 individual structures. A significant amount of investigation has been carried out, including ultrasonic thickness measurement, detection of brickwork laminations using radar, stress measurement using magnetic techniques, determination of soil parameters using CPT, pressuremeter and laboratory testing, installation of piezometers, material and tunnel segment testing, and trialling of remote photographic techniques for inspection of large tunnels and shafts. Vibrating wire, potentiometer, electro level, optical and fibre-optic monitoring has been used, and laser measurement and laser scanning has been employed to measure tunnel circularity. It is considered that there is scope for considerable improvements in non-destructive testing technology for structural assessment in particular, and some ideas are offered as a "wish-list". Assessment reports have now been produced for all assets forming Tube Lines' deep tube tunnel network. For assets which are non-compliant with London Underground standards, the risk to the operating railway has to be maintained as low as reasonably practicable (ALARP) using enhanced inspection and monitoring, or repair where required. Monitoring techniques have developed greatly during recent years and further advances will continue to support the economic whole life asset management of infrastructure networks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.516-522
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• 2020

Water trees can cause considerable damage to the performance of underground cables. Theymay formwithin the dielectric used in buried or water-immersed high voltage cables. They grow in a bush-like or tree-like form, often taking decades before causing damage to a cable's performance. They are usually found on very old underground cables, often in an inaccessible place. It is costly and time-consuming to detect watertrees in underground cables. Tree detection technology, including mathematical modeling,can reduce the maintenance cost and time necessary for detecting these trees.To simulate detection of water trees in this study, a mathematical model ofan XLPE cable and a water tree were developed. The complex water tree structure was simplified, based on two identified patterns of aventedtree. A Matlab simulation was performed to calculate and analyze the capacitance and resistance of a cable insulation layer,based on growth of a watertree. Capacitance size increased about 0.025×10^-13[Farads/mm] compared to normal when the tree area of the cable was advanced to 95% of the insulation layer. The resistance value decreased by about 0.5×10¹⁶[ohm/m]. These changesand changesshowninaBurkes paper physical modeling simulation are similar.The value of mathematical modeling for detecting water trees and damage to underground cables has been demonstrated.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• Journal of Soil and Groundwater Environment
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• v.7 no.4
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• pp.40-47
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• 2002

The practices of the construction and management of the petroleum and hazardous chemical compound storage facilities in Korea were investigated extensively, and the problems were identified. The advanced technologies in the U.S.A were comparatively studied. Considering the effectiveness of leak prevention and applicability, the following measures were suggested. To prevent corrosion of a tank, a clad tank, an interior lining tank, or a double-wall tank were thought to be the most cost effective. For piping. use of non-metalic materials was suggested. A catchment basin seemed to be effective for preventing spills. For monitoring of leaks, constructions of more than one of detection systems, such as an automatic leak detection device. a vapor detection system, a groundwater monitoring system, or a double-wall monitoring system, were recommended.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.503-516
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• 2021

Autonomous fire detection and suppression system requires advanced technology for complex detection technology and injection/control technology for accurate hitting by fire location. Also, foam spraying should be included to respond to oil fires. However, when a single spray monitor is used in common, water and foam spray properties appear different, so for accurate fire suppression, research on the spray trajectory and distance will be required. In this study, experimental studies and numerical analysis studies were combined to analyze the foam spray characteristics through the spray monitor developed for the establishment of an autonomous fire extinguishing system. For flow analysis of foam injection, modeling was performed using OpenFOAM analysis software, and the commonly used foaming agent (Aqueous Film-Forming Foam) was applied for foam properties. The injection distance analysis was performed according to the injection pressure and the injection angle according to the form of the foam, and at the same time, the results were verified and presented through the injection experiment.

• 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.