• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.389-392
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• 2012

연기감지기는 이온전류의 변화량을 감지하는 이온화식 연기감지기와 발광부와 수광부로 이루어진 챔버내에 연기에 의한 광량의 변화를 감지하는 광전식 연기감지기로 구분된다. 국내에서는 광전식 연기감지기가 더 많이 사용되고 있는데 이러한 광전식 연기감지기의 챔버내에 이물질이 침입하게 되면 비화재보를 발생시키게 된다. 본 논문은 연기감지기의 연기감지 챔버에 대한 비화재보를 감소시키기 위한 신뢰성 시험에 대하여 논한 연구이다. 광전식 연기감지기의 신뢰성을 검증하는 방법으로는 먼지에 대한 신뢰성을 검증하는 가연물 종류에 따른 연기감지 신뢰성 시험이나 분진시험이 적합한 것으로 판단되며, 이러한 시험의 반복을 통하여 광전식 연기감지기의 신뢰성을 검증하는 것이 필요한 것으로 사료된다.

• 방재와보험
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• s.107
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• pp.38-44
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• 2005

기존 화재감지 기술이 점차적으로 발전되어 가고 있지만, 이러한 기존 기술과 다른 자동식 연기감지 방식이 혁신적으로 변화하고 있다. 이런한 혁신기술로 인하여 성장기의 화재를 감지하고 감지기에서 데이터를 분석하고, 화재와 관련된 데이터를 소방관계자에게 전송하는 방법이 계속 변화하고 있다. 기존 화재감지 방식의 예는 여러 가지 감열소자(열감지기), 광전식, 이온화식, 공기흡입형(연기감지기), 자외선.적외선(불꽃감지기)감지 방식이다. 감지기에서 데이터 분석의 형태로 연기농도를 표시하는 방법 등이 있다. 화재조건을 전송하는 방법은 특정한 구성요소(발신기, 감지기 등)의 위치(어드레스), LCD, CRT 그래픽 표시장치에서 많은 발전이 있었다. 이러한 혁신 기술은 화재경보 설비 판매시장, 설치방법, 기술기준의 변화에 많은 영향을 미치고 있다. 최근 개발된 디지털 비디오 연기감지 방식은 자동화재 감지 방식에 있어서 새롭고 급진적인 변화를 요구하고 있다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2013.04a
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• pp.150-151
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• 2013

화재감지기는 연기감지기는 이온전류의 변화량을 감지하는 이온화식 연기감지기와 발광부와 수광부로 이루어진 챔버내에 연기에 의한 광량의 변화를 감지하는 광전식 연기감지기로 구분된다. 국내에서는 광전식 연기감지기가 더 많이 사용되고 있는데 이러한 광전식 연기감지기의 챔버내에 이물질이 침입하게 되면 비화재보를 발생시키게 된다. 본 논문은 연기감지기의 연기감지 챔버에 대한 비화재보를 감소시키기 위한 신뢰성 시험에 대하여 논한 연구이다. 본 연구에서는 광전식 연기감지기의 신뢰성을 검증하는 방법으로 UL 268 등에 규정되어 있는 먼지, 즉, 분진에 대한 신뢰성을 검증방법을 제시하였다. 먼지에 대한 신뢰성 검증을 보다 적절하게 하기 위하여 분진챔버로 분진을 반복적으로 폭로됨에 따른 감도성능 변화를 분석하는 방법이 필요할 것으로 사료된다.

• Fire Science and Engineering
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• v.17 no.4
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• pp.86-91
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• 2003

Since the air stream in the room controlled by HVAC system affects on he expected response of conventional detectors which are designed in accordance with normal characteristics of air stream in the fire incident, unexpected operation time delay may occur in fire. In order to solve this problem and to improve sensitivity so that to initiate fire in its early stages for minimizing damage and protecting people, we studied and developed Air Sampling Smoke Detector. The Air Sampling Smoke Detector is a kind of active-type fire detection system. it draws air continuously from the protected area through an air sampling pipe network to the smoke density analyzer. This study presents smoke density analysing technique and air intake balancing technique through an air sampling pipe network. As a result of evaluating, Air Sampling Smoke Detector was much more sensitive than conventional smoke detectors that passively wait for smoke to reach them and was not affected by ambient airflow in the room by means of balanced air intake through the sampling holes.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.236-244
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• 2021

Purpose: The purpose of this study is to develop IoT multi-fire detectors combined smoke/carbon monoxide/heat and wireless IoT communication and to confirm the detect performance by smoke generator fire test and cotton wicks fire test. Method: The IoT multi-fire detector combined smoke and CO and combined smoke and heat were experimented the detect performance by smoke generator test and fire test of cotton wicks. And the case of fire alarm was checked. Result: The IoT multi-fire detector combined smoke and CO rung the alarm at the fire test of cotton wicks, did not ring the alarm at the smoke generator test. In comparison, the IoT multi-fire detector combined smoke and heat did not ring the alarm both at the smoke generator test and the fire test of cotton wicks. Conclusion: The IoT multi-fire detector combined smoke and CO detected the only smoke including the carbon monoxide and the IoT multi-fire detector combined smoke and heat did not ring the alarm for lack of heat. As a result, when the developed IoT multi-fire detector was detected the signal more than the set point, the fire alarm was sounded through cotton wicks fire test and smoke generator.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.287-293
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• 2014

Early fire detection is very important to avoid loss of lives and material damage. The conventional smoke detector sensors have difficulties in detecting smoke in large outdoor areas. The video-based smoke detection can overcome these drawbacks. This paper proposes a new smoke detection method in video sequences. It uses the ratio of variation rate of subband energy in the wavelet transform domain. In order to reduce the false alarm, candidate smoke blocks are detected by using motion, decrease of chromaticity and the average intensity of block in the YUV color space. Finally, it decides whether the candidate smoke blocks are smokes or not by using their temporal changes of subband energies in the wavelet transform domain. Experimental results show that the proposed method noticeably increases the accuracy of smoke detection and reduces false alarm compared with the conventional smoke detection methods using wavelets.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.43-52
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• 2011

With the rapid development of technology, skyscrapers are widely spread and they are tightly coupled. If fire occurs in a building, it is easily spread to neighboring buildings, resulting in the large number of victims and property damages. To remove fire disasters, the need for early fire detection techniques is increasing. To detect fire, detecting devices for heat, smoke, and flame have been used widely. However, this paper surveys and presents the latest research which focuses on early smoke and flame detection algorithms and systems with camera's input images. In addition, this paper implements and evaluates the performance of these flame and smoke detection algorithms with several types of movies.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.213-220
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• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• Fire Science and Engineering
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• v.16 no.4
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• pp.28-32
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• 2002

The performance based design in fire detection system, the effect of high airflow and dilution of smoke produced in any fire situation serve to increase the response time of point-type smoke detectors. This study investigated the smoke density of ceiling, under the air stream and in normal status when fire type is smoldering fires. The result of study, smoke generated in the fire was swept away from nearby spot type smoke detector which failed to actuate because dispersed in diluted form around the room. The concept of performance based design in fire detection system of protected area influenced by high airflow provided the need of active fire detection system such as air sampling smoke detection system.

• 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.