• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.378-382
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• 2006

현재 주로 사용되고 있는 화재 감지 장치들은 경고 기반으로 되어 있어 정확한 화재 발생의 위치 검색이 어려우며 장치의 고장이 발생한 경우 고장 유/무의 확인이 어려워 보다 큰 인명 피해 및 재산 피해를 가져올 수 있다. 센서 네트워크 시스템은 통신매체 없이 데이터 송/수신이 가능하며 이를 통한 모니터링 환경의 구축이 쉬워 위치검색 및 고장 검출 알고리즘의 적용이 간단하다. 본 논문에서는 WSN 기반의 화재 감지 시스템을 제안하며 오류검출 알고리즘인 Consensus 알고리즘을 적용하여 그 유용성을 확인하고자 한다.

• Fire Protection Technology
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• s.17
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• pp.10-14
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• 1994

Since it is extremely of importance that the automatic fire detection systems are reliable and able to operate properly in case of fire, they are subjucted to test on the variety of combustibles.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.597-598
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• 2013

A lot of fire and crime accidents are caused to a significant national loss. For example, the network and power facilities in national industry facilities, the fire risk region in large scale factories such as nuclear and thermal power plants, large-sized buildings, cultural properties, metal and steel mills, chemical plants, oil refineries. The development of a fire detection system that can detects the temperature and movement of objects as high-level quality is essential to prevent these incidents and accidents fundamentally. In this paper, the fire detection system based on infrared thermal imaging & CCD camera id designed to solve these problems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.1-10
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• 2014

This paper proposes a parallel implementation of the video based 4-stage fire detection algorithm using a general-purpose graphics processing unit (GPGPU) to support real-time processing of the high computational algorithm. In addition, this paper compares the performance of the GPGPU based fire detection implementation with that of the CPU implementation to show the effectiveness of the proposed method. Experimental results using five fire included videos with an SXGA ($1400{\times}1050$) resolution, the proposed GPGPU implementation achieves 6.6x better performance that the CPU implementation, showing 30.53ms per frame which satisfies real-time processing (30 frames per second, 30fps) of the fire detection algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.50-53
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• 2022

In order to detect an object in yolv5, a process of annotating location information on an existing image is required when learning an image. The most representative method is to draw a bounding box on an image to store location information as meta information. However, if the boundary of the object is ambiguous, it will be difficult to make a bounding box. A representative example would be to classify parts that are not fire and parts that are fire. Therefore, in this paper, images of 100 samples judged to have caught fire were learned by varying the number of boxes. The results showed better fire detection performance in the model where the bounding box was trained by annotating it with three boxes by segmenting it slightly more than annotating it with one box by holding the edge as large as possible during annotating it with one box.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.265-270
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• 2003

Though the probability of tunnel fire accident is very low, but critical fatalities are expected when it occurred. In this study the effect of critical safety parameters on tunnel fire accident are examined using probabilistic technique. Fire detection time, smoke spread velocity, passenger escape velocity, flash-over time, and emergency service arrival time are considered. In order to estimate the uncertainties of input parameters Monte Carlo simulation are used, and fatalities for each assumed accident scenarios are obtained as results. For the efficiency of iterative calculation PRA(Probabilistic Risk Analysis) code is developed in this study. As a result fire detection have large effect.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.382-386
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• 2005

In this paper we introduce a fire-detection system which is robust to light sources and environment changing. We can decide the threshold values that classify the regions between a fire flame and light sources by analyzing them in RGB color space. The mean histogram difference technique make it possible to extract flame region more efficient because fire flame is continuously changing after it occurs. In order to detect flame region, this paper proposes to count fire pixels.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.590-597
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• 1997

In this work, a new type of UV flame detection system was developed. In order to measure the performance of UV flame detector, various kinds of experiments was performed. The results show that the maximum response time of the UV flame detector is 0.2 seconds when the detection distance is one meter The advantages of this system include wide area, high speed response and high sensitivity. After testing the W flame detector in engine compartment it detected fire within 0.09 seconds and extinguished within 5 seconds. Hence, the UV flame detector can be applied in automatic fire suppression system for automobiles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7964-7970
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• 2015

In enclosed areas such as railway tunnels, the heat and smoke generated by a fire can pose a tremendous risk to the life of passengers. To prevent or mitigate such scenarios, fire detectors are installed for early fire detection. This numerical study is preformed for establishing the method of detecting performance of fire detectors installed on railway tunnels. Numerical analysis are conducted using the fire dynamics simulator, developed by the NIST. The temperature of the tunnel walls is determined using the assumed exterior structure of the tunnel. In addition, the detection times of detectors installed at different locations in the tunnel are obtained for different sizes of the fire source, and the results are compared and analyzed.

• Journal of Korean Institute of Fire Science & Engineering
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• v.4 no.1
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• pp.1-5
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• 2008