• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.327-328
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• 2015

본 논문은 지하공동구에서의 출입자의 통제를 위해 스마트폰을 이용한 출입관리시스템을 제안한다. 제안하는 시스템은 지하공동구 자동개폐기의 MCU를 통해 온습도, 개폐기의 상태, 출입자의 기록 등 다양한 정보를 취득하여 관리한다. 이때 취득한 정보는 블루투스4.0 모듈을 통해 ECB 암호화 방식을 사용하여 스마트폰에 전송한다. 본 논문에서 제안하는 출입관리시스템은 기존 물리적인 키에 의존하던 출입 방식을 대체하여 신뢰성 있는 보안 및 체계적인 관리가 가능할 것으로 기대한다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2013.04a
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• pp.44-45
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• 2013

본 연구는 선착장이 있는 부두의 변전소와 지하로 연결된 지하공동구에 대하여 위험성을 평가하기 위하여 현장조사를 하여 위험성을 발췌하고 실측을 통해 공동구의 재원을 확보하고 화재시뮬레이션을 수행하여 화재위험성 정도를 평가하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1597-1603
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• 2017

The incidence of fire in underground utility tunnel is lower than other fires, but the damage caused by fire can cause social loss due to social management paralysis as well as economic loss. Hereupon, this paper presents the results of an empirical test on the construction of the underground utility tunnel condition monitoring system using the leakage coaxial cable installed in the underground utility tunnel. For this reason, a verification test was conducted by connecting a inductive coupler 200 Mbps power line communication modem with insertion loss characteristics of $-6{\pm}2dB$ to the installed the leakage coaxial cable installed in the underground utility tunnel. As a result, We confirmed sending/receiving of IP cameras up to 500 m. Therefore, it is judged that it is possible to construct a condition monitoring system for underground utility tunnel by using the leakage coaxial cables installed in the underground utility tunnels without installing additional communication lines for data transmission.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.248-258
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• 2021

24-hour monitoring is required to maintain the city's lifeline function in the underground facility for public utilities. And it is necessary to develop technology to exchange the shortage of human resources. It is difficult to reflect the specificity of underground space management in general management methods. This study proposes underground facility for public utilities digital twin system requirements. The concept of space is divided into physical space and virtual space, and the physical space constitutes the type and layout of the sensor that is the basis for the construction of the multimodal image sensor system, and the virtual space constitutes the system architecture. It also suggested system functions according to the task. It will be effective in preventing disasters and maintaining the lifeline function of the city through the digital twins.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.873-875
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• 2015

출입보안을 목적으로 하는 대다수의 출입관리 시스템은 중앙관제시스템과 자동 개폐기 간의 네트워크가 구축되어 있으며 RFID/지문인식과 같은 형태의 인증시스템을 통해 시스템을 구축한다. 그러나 지하공동구의 출입구와 같은 빈번한 출입이 필요 없는 환경에서의 출입관리를 위한 시스템이 없는 실정이며, 지하공동구의 환경 여건상 외부통신의 연결이 어려운 곳에 위치하고 있어 기존의 유선중심의 중앙관제시스템을 구성하기 어려운 상황이다. 따라서 본 논문에서는 지하공동구 또는 공공시설물 출입관리의 효율성과 보안성을 높이기 위해 스마트폰을 키로 사용할 수 있는 출입관리시스템을 제안한다.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.665-672
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• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• 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 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 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.

• Water for future
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• v.55 no.12
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• pp.49-55
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• 2022