• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.99-107
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• 2020

An underground integrated map is being built for underground safety management and is being updated periodically. The map update proceeds with the procedure of deleting all previously stored objects and saving newly entered objects. However, even unchanged objects are repeatedly stored, deleted, and stored. That causes the delay of the update time. In this study, in order to shorten the update time of the integrated map, an updated object and an unupdated object are separated, and only updated objects are reflected in the underground integrated map, and a system implementing this technology is described. For the updated object, an object comparison method using the center point of the object is used, and a quad tree is used to improve the search speed. The types of updated objects are classified into addition and deletion using the shape of the object, and change using its attributes. The proposed system consists of update object detection, extraction, conversion, storage, and history management modules. This system has the advantage of being able to update the integrated map about four times faster than the existing method based on the data used in the experiment, and has the advantage that it can be applied to both ground and underground facilities.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.171-184
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• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.302-314
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• 2024

Purpose: This study proposes a fall detection model based on a top-down deep learning pose estimation model to automatically determine falls of multiple workers in an underground utility tunnel, and evaluates the performance of the proposed model. Method: A model is presented that combines fall discrimination rules with the results inferred from YOLOv8-pose, one of the top-down pose estimation models, and metrics of the model are evaluated for images of standing and falling two or fewer workers in the tunnel. The same process is also conducted for a bottom-up type of pose estimation model (OpenPose). In addition, due to dependency of the falling interference of the models on worker detection by YOLOv8-pose and OpenPose, metrics of the models for fall was not only investigated, but also for person. Result: For worker detection, both YOLOv8-pose and OpenPose models have F1-score of 0.88 and 0.71, respectively. However, for fall detection, the metrics were deteriorated to 0.71 and 0.23. The results of the OpenPose based model were due to partially detected worker body, and detected workers but fail to part them correctly. Conclusion: Use of top-down type of pose estimation models would be more effective way to detect fall of workers in the underground utility tunnel, with respect to joint recognition and partition between workers.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.285-292
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• 2016

Reactor neutrinos have been detected in the past 50 years by various detectors for different purposes. Beginning in the 1980s, neutrino physicists have tried to use neutrinos to monitor reactors and develop an optimized detector for nuclear safeguards. Recently, motivated by neutrino oscillation physics, the technology and scale of reactor neutrino detection have progressed considerably. In this review, I will give an overview of the detection technology for reactor neutrinos, and describe the issues related to further improvements in optimized detectors for reactor monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.104-110
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• 2016

To get the empirical data of GPR detection and to develop the image prosessing program of GPR detection data, GPR detection were proceed by the underground pipes and cavities buried in the Chamber. In the case of non pavement and asphalt pavement, water filled cavity that was buried in 0.7m depth was able to detection. But in the case of 1.0 m and 1.3 m buring depth, water filled cavity was not able to detection. In the case of non-reinforced and reinforced concrete pavement, it was difficult to detect the cavity caused by signal interference. GPRiPP programs was developed for image processing of the GPR detection data. The major processing algorithm were background removal, stacking and gain function. With proper image processing of gain function and background removal in GPRiPP program, it was showed that similar results can be obtained with conventional image processing program.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.2
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• pp.251-256
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• 2017

This study developed an image processing system for detecting damages on underground spiral PVC pipes. The detection method is simple-identifying damaged areas by measuring circularity along the pipeline. This uses the assumption that damage parts will not make a circular shape. Conventional devices check the circular shape of the pipe along the pipeline by measuring the angles between 6 spring-connected legs on the device. The conventional device, however, requires the insertion of 3 different wires (electrical, communication, and camera lines) along with a guide wire for pulling the device. The developed system presented here has simplified this system, requiring only a camera line while maintaining reasonable accuracy in damage detection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.821-826
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• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of a good contermeasures of leaks Is to find and repair of leak points of pipes. Leak noise is a good source to identify the location of leak points of pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they were not so efficient tools beca. In this paper, two accelermeters are used to detect leak locations which could provide an easier and efficient method. The filtering, signal processing and algorithm is described for the detection of leak location. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the two accelerometers gives very accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed in underground pipes is also described.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.4
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• pp.253-257
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• 2017

Nowadays, it is increasingly important to detect whether cables are live for the operator's safety if there is a sudden power failure. It is especially hard to detect the electrical field of an underground line because of shielding. This paper on detection of live-line states in cables studied the detection characteristics of the change in the magnetic field and axis as the frequency, voltage, and distance at the same load are changed using 3 axes. A search coil type was used as a magnetic field sensor with non-contact. We found that magnetic fields decrease proportionally to the square of the distance and the decrease of rated voltage with load effected to magnetic field. The magnetic field was detected by 3-axis sensors given correct proximity, but appeared as noise components beyond a distance of 2 cm.