• 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

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.7-8
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• 2014

The smart fire detection and automatic extinguish device interface platform based on thermal imaging camera that early monitors fire is designed and implemented in this paper. If a fire occurs in some area, the developed system can detect and automatically extinguish the fire. The major functions for developing the system are: Image system and Viewer for fire detection based on Thermal imaging camera and Megapixel camera; Automatic extinuish device for early fir detection; Interface platform between monitoring systems and automatic extinguish device.

• Fire Science and Engineering
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• v.34 no.1
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• pp.26-36
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• 2020

It is very difficult to suppress fire by rapid flame spread through flue space between flammable commodities on the rack when a fire occurs in the rack storage. At present, the fire protection system for rack storage in Korea has many issues, and the new fire protection system was designed and developed by it. A smart system using the sensor network and artificial intelligence was designed to detect fire very rapidly and track the location of a fire. In the very early stages, the system was constructed using vertical open sprinkler pipes, wet pipes, and solenoid valves to allow water to spray near fire locations. Based on the design results, the system was installed and tested, and the full-scale test was successfully completed.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1649-1656
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• 2016

Performance of the existing sensor-based fire detection system is limited according to factors in the environment surrounding the sensor. A number of image-based fire detection systems were introduced in order to solve these problem. But such a system can generate a false alarm for objects similar in appearance to fire due to algorithm that directly defines the characteristics of a flame. Also fir detection systems using movement between video flames cannot operate correctly as intended in an environment in which the network is unstable. In this paper, we propose an image-based fire detection method using CNN (Convolutional Neural Network). In this method, firstly we extract fire candidate region using color information from video frame input and then detect fire using trained CNN. Also, we show that the performance is significantly improved compared to the detection rate and missing rate found in previous studies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.193-194
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• 2024

스마트홈과 연동하는 안전한 방범창은 외부의 침입자를 차단하고 주거 내의 물품 도난 및 개인의 신변을 보호하기 위한 안전설비로 다양한 형태의 방범창이 존재한다. 스마트홈은 건물 내의 센서가 화재를 감지하면 방범창 내부의 원격시스템이 자동으로 방범창을 매우 짧은 시간 안에 개방할 수 있도록 설계한다. 화재 등 재난시 상하부 방법창이 자동으로 개폐되도록 랙피니언을 이용하여 방범창의 위쪽 창틀을 상승시켜 창살이 움직일 수 있도록 개방하고 동시에 아래쪽 창틀이 회전하도록 구성하여 화재 및 폭우 등에 인명피해를 줄일 수 있도록 설계한다. 스마트홈 시스템과의 연계로 재난을 미리 감지하고 탈출구를 개방한다면 신속하고 안전한 대피가 가능하고, 기존의 프레임 부분 변경에 간단한 시스템만 변경하여 적용할 수 있어, 간단한 구조로 설치할 수 있고 낮은 비용으로 스마트홈을 구성할 수 있다. 특히 스마트홈과 연동하여 재난을 미리 감지하고 탈출구를 개방함으로써 신속하고 안전한 대피가 가능하여 다양한 재난을 대비할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.251-254
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• 2018

Research about IoT system that utilizes variation of wireless received signal according to the changing of the surrounding environment are actively being studied. In this paper, firstly we proved that the received signal strength changes according to the ambient temperature variation. Then, we proposed the fire detection scheme by using received signal strength variation when the signal exchange between fixed transmitter and receiver periodically. The proposed scheme consists of the received signal strength change detection unit and the internal receiver temperature detection unit which prevents misunderstanding the received signal strength variation by the changing of wireless channel environment as outbreak of fire. The proposed scheme has the advantage of being able to support the existing receiver through software upgrade without additional device.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.391-394
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• 2023

일반적으로 화재 감지 시스템은 정확하고 빠르게 화재를 감지하는 것은 어려운 문제 중 하나이다. 본 논문에서는 U-net을 활용하여 기존의 화재(불) 영역 추출 기법으로 Segmentation으로 보다 정밀하게 탐지하는 기법을 제안한다. 이 기법은 화재 이미지에서 연기제거 및 색상보정을 통해 이미지를 전처리하여 화염 영역을 추출한 뒤 U-Net으로 학습시켜 이미지를 입력하면 불 영역의 Segmentation을 추출하도록 한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.9-16
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• 2020

In this paper, we propose a prototype design and implementation of a fire detection algorithm using multiple sensors. The proposed topic detection system determines fire by applying rules based on data from multiple sensors. The fire takes about 3 to 5 minutes, which is the optimal time for fire detection. This means that timely identification of potential fires is important for fire management. However, current fire detection devices are very vulnerable to false alarms because they rely on a single sensor to detect smoke or heat. Recently, with the development of IoT technology, it is possible to integrate multiple sensors into a fire detector. In addition, the fire detector has been developed with a smart technology that can communicate with other objects and perform programmed tasks. The prototype was produced with a success rate of 90% and a false alarm rate of 10% based on 10 actual experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.904-905
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• 2014

일반 가정이나 산업 현장에서 화재가 발생할 경우 인명 피해와 재산 피해가 막대하다고 할 수 있다. 이러한 막대한 피해를 막기 위하여 국가에서는 소화방재를 위한 법적 검토와 소방 안전교육 및 피난훈련을 실시하고 있다. 그러나 대부분의 화제 현장에서의 초기 대응은 대부분 분말소화기에 의존하고 있는 실정이다. 현재 새롭게 개발된 청정 소화약제를 활용한 고체에어로졸(fixed aerosol) 소화장치를 개발하여 화재 방지시스템을 개발하고 있다. 본 논문에서는 고체에어로졸 소화장치의 활용에 있어서 불꽃감지센서 및 열 감지센서의 데이터를 활용할 수 있고, 화재 발생 현장에 대한 현장 데이터를 원격지에서 모니터링 할 수 있는 유무선통신기반의 제어기 모듈을 개발하여 원격지 화재 현장을 보다 효율적으로 대응할 수 있는 시스템을 개발하고자 한다.