• Title/Summary/Keyword: 화재 감지 방법

Search Result 100, Processing Time 0.02 seconds

A Fire Detection System Using Fuzzy Logic with Input Variables of Temperature and Smoke Density (열과 연기농도를 입력변수로 갖는 퍼지로직을 이용한 화재감지시스템)

  • Hong Sung-Ho;Kim Doo-Hyun;Kim Sang-Chul
    • Fire Science and Engineering
    • /
    • v.18 no.4
    • /
    • pp.42-51
    • /
    • 2004
  • This paper presents a study on the analysis of fire detection system using fuzzy logic with input variables of temperature and smoke density. The input variables for the fuzzy logic algorithm are measured by fire experiment of small scale with temperature detector and smoke detector. The antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire possibility. Also the triangular fuzzy membership function is chosen for input variables and fuzzy rules to simplify computation. In order to calculate fuzzy values of such fuzzy system, a computer program is developed with Matlab based on graphics user interface. The experiment was conducted with paper and ethanol to simulate flaming fire and with plastic and sawdust to model smoldering fire. The results showed that the fire detection system presented here was able to diagnose fire very precisely. With the help of algorithms using fuzzy logic we could distinguish whether fire or not.

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire (강인한 움직임 영역 검출과 화재의 효과적인 텍스처 특징을 이용한 화재 감지 방법)

  • Nguyen, Truc Kim Thi;Kang, Myeongsu;Kim, Cheol-Hong;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.18 no.6
    • /
    • pp.21-28
    • /
    • 2013
  • This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

Fire Detection using Deep Convolutional Neural Networks for Assisting People with Visual Impairments in an Emergency Situation (시각 장애인을 위한 영상 기반 심층 합성곱 신경망을 이용한 화재 감지기)

  • Kong, Borasy;Won, Insu;Kwon, Jangwoo
    • 재활복지
    • /
    • v.21 no.3
    • /
    • pp.129-146
    • /
    • 2017
  • In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

Fuzzy Measure를 이용한 화재감지기의 기본설계

  • 백동현;김기화
    • Fire Science and Engineering
    • /
    • v.10 no.3
    • /
    • pp.19-28
    • /
    • 1996
  • This paper present the way the fire detector determines whether a fire has broken out or not using the fuzzy measure. This method is based on Dempster's combination rule using the belief measure. The detector indicate a 'Fire'(F) or 'Nonfire'(N) when it determines whether a fire has broken out or not. To determine this, the fuzzy rule is applied in the setting value for the heat and smoke detector which is used. As a result, It is proved that the final decision can be determined more exactly whether a fire has broken out or not in proportion to the frequency of the fuzzy measure and the value of Bel (F).

  • PDF

A Study on the Reliability Test for Smoke Detection Chamber of Smoke Detector (연기감지기의 연기감지 챔버와 신뢰성 시험에 관한 연구)

  • Hong, Sung-Ho;Choi, Moon-Soo;Park, Sang-Tae;Baek, Dong-Hyun
    • Proceedings of the Korea Institute of Fire Science and Engineering Conference
    • /
    • 2012.04a
    • /
    • pp.389-392
    • /
    • 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.

  • PDF

Test Method Using Shield-cup for Evaluating Response Characteristics of Fire Detectors (화재감지기의 응답특성 평가를 위한 Shield-cup이 적용된 시험방법)

  • Jang, Hyo-Yeon;Hwang, Cheol-Hong
    • Fire Science and Engineering
    • /
    • v.34 no.4
    • /
    • pp.36-44
    • /
    • 2020
  • It is necessary to predict the activation time of fire detectors accurately to improve the reliability for evaluating the required safe egress time (RSET) in performance-based fire safety design. In this study, problems of the plunge test, which is widely applied in assessing fire detectors, were examined through experiments and numerical simulations. In addition, a new shield-cup test method was proposed to address these problems. A fire detector evaluator (FDE) developed in a previous study was applied to ensure measurement accuracy and reproducibility. During the plunge tests, a significant measurement error was observed in the activation time of the smoke detector because of the rapid flow change when the detector was input. However, during the shield-cup tests, slight changes occurred in the flow inside the FDE when the detector as exposed to smoke. In conclusion, the proposed shield-cup test method is expected to be useful for evaluating the response characteristics of fire detectors more accurately in simulated fire environments.

Fire Rescue System based on Location-aware (위치 인식 기반 화재 구조 시스템)

  • Yoo, Jae-Bong;Yoo, Beum-Jung;Kim, Sang-Youn;Park, Chan-Young
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2007.11a
    • /
    • pp.741-744
    • /
    • 2007
  • 홈 네트워크 분야가 발전하면서 건물이나 집안 내부 정보를 감지하는 센서들도 발전하고 있다. 화재 역시 다양한 원인에 대해 여러 가지 방법으로 감지하는 센서들이 개발되고, 화재 진압 시스템도 발전되고 있다. 본 연구에서는 화재 감지 시스템에 Cricket 센서 네트워크 이용한 위치 추적 기법을 추가하여 화재 발생 시 건물 내 사람의 위치를 추적하는 시스템을 구현한다. 이는 신속한 인명 구조에 도움을 주고, 홈 네트워크의 새로운 한 분야를 제시한다.

Development of Fire Detection Algorithm for Video Incident Detection System of Double Deck Tunnel (복층터널 영상유고감지시스템의 화재 감지 알고리즘 개발)

  • Kim, Tae-Bok
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.23 no.9
    • /
    • pp.1082-1087
    • /
    • 2019
  • Video Incident Detection System is a detection system for the purpose of detection of an emergency in an unexpected situation such as a pedestrian in a tunnel, a falling object, a stationary vehicle, a reverse run, and a fire(smoke and flame). In recent years, the importance of the city center has been emphasized by the construction of underpasses in great depth underground space. Therefore, in order to apply Video Incident Detection System to a Double Deck Tunnel, it was developed to reflect the design characteristics of the Double Deck Tunnel. and In this paper especially, the fire detection technology, which is not it is difficult to apply to the Double Deck Tunnel environment because it is not supported on existing Video Incident Detection System or has a fail detect, we propose fire detection using color image analysis, silhouette spread, and statistical properties, It is verified through a real fire test in a double deck tunnel test bed environment.

A Study on the Problem and Improvement of Fire Detector Test in the Field Inspection (화재감지기 현장점검의 문제점 및 개선방안에 관한 연구)

  • Cha, Ha-Na;Ok, Kyung-Jae;Kim, Shi-Kuk;Lee, Chun-Ha;Jee, Seung-Wook
    • Fire Science and Engineering
    • /
    • v.22 no.4
    • /
    • pp.50-53
    • /
    • 2008
  • This study has been conducted for the purpose of considering the problems expected when checking the smoke detector at site and how to improve the smoke detector. We have conducted this study focused on checking the problems expected upon site inspection and finding out the way of improving the site inspection by analyzing the reliability and performance of instrument used upon site inspection. The test results show that some problems exist in the reliability and performance of instrument used for the site inspection of smoke detector way of inspecting the inspection instrument.