• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.70.2-70.2
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• 2010

본 연구는 최근 발생한 숭례문 화재와 같은 목조문화재의 화재를 초기에 인지하고 확인하는 차원에서 고안된 시스템으로써 불꽃감지기와 같은 초기 화재 감지시스템의 오작동 여부를 확인하여 화재감지기의 오작동으로 인한 경제적 시간적인 손실을 예방하고 목조문화재를 화재로부터 보호하기 위한 시스템이다. 초기에 화재를 감지하는 불꽃감지기는 현재 목조문화재뿐만 아니라 다양한 곳에서 활용되고 있지만 감지기의 오작동 및 오류를 확인하는 시스템은 실제로 실효성 등의 문제로 인해 활용이 미비한 실정이다. 본 연구에서는 유비쿼터스 센서 네트워크(USN) 기술, 불꽃감지기, 이미지 센서, USN 기반 문화재 방재 응용사례, 오작동 확인시스템 구현 등에 대해서 살펴보고 유비쿼터스형 문화재 방재시스템을 제시해 본다.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.293-294
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• 2022

본 프로젝트에서는 화재 발생 시 발견하기 어려운 곳이나 화재가 빈번한 곳에 구축하는 것을 목적으로 하고 있다. 가스와 불꽃을 감지하는 센서로 화재를 감지하고, 디스플레이와 LED 그리고 소리를 통해 화재발생을 알려준다. 그 후, 스프링클러가 작동하여 초기화재에 대응에 도움을 주고 119에 자동으로 신고가 된다. 일정 수치의 센서에 대한 감지 값을 인식하고 인식한 감지 값에 반응하여 화재 대처를 가능하게 구현하는 시스템을 제안한다. 감지센서를 통한 화재장소에서의 불꽃과 가스를 감지하게 되어 스프링클러가 1차적으로 화재의 번짐을 지연해 주고 은근 소방서에 자동적으로 신고를 하게 되는 자동화 프로그램 이행을 목표로 하고 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.331-336
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• 2017

The need for early fire detection technology is increasing in order to prevent fire disasters. Sensor device detection for heat, smoke and fire is widely used to detect flame and smoke, but this system is limited by the factors of the sensor environment. To solve these problems, many image-based fire detection systems are being developed. In this paper, we implemented a system to detect fire and smoke from camera input images using a convolution neural network. Through the implemented system using the convolution neural network, a feature map is generated for the smoke image and the fire image, and learning for classifying the smoke and fire is performed on the generated feature map. Experimental results on various images show excellent effects for classifying smoke and fire.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.953-958
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• 2015

Raspberry Pi is a credit card-sized computer with support for a large number of input and output peripherals. This makes it the perfect platform for interaction with many different devices and for usage in a wide range of applications. When combined with Wi-Fi, it can communicate remotely, therefore increasing its suitability for the construction of wireless sensor nodes. In addition, data processing and decision-making can be based on artificial intelligence, what is performed in developed testbed on the example of monitoring and determining the confidence of fire. In this paper, we demonstrated the usage of Raspberry Pi as a sensor web node for fire-safety monitoring in a building. When the UV-flame sensors detect a flame as thin as that of a candle, the Raspberry Pi sends a push-message to notify the assigned smartphone of the on-site situation through the GCM server. A mobile app was developed to provide a real-time video streaming service in order to determine a false alarm. If an emergency occurs, one can immediately call for help.

• Fire Science and Engineering
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• v.33 no.1
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• pp.85-91
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• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.231-234
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• 2005

In this paper, propose device that all of AC Power could do switching because uses fire flame sensor(UV TRON R2868) and sensor driving circuit. Used OrCAD program for analysis of consisted Power On/Off Set, and this manufactures and experimented driving circuit actually. The experimental result is considered that can refer to data of circuit design in case of apply to actual field of practical use that need alarm after sense flame, and special quality that give an alarm sensing flame in experiment wave and given environment relatively stable. With this result Power On/off Set and that action of embodied serrounding system can utilize actually verify.

• Electrical & Electronic Materials
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• v.19 no.3
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• pp.20-26
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• 2006

• Electrical & Electronic Materials
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• v.19 no.3
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• pp.10-19
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• 2006

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.463-470
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• 2009

In this paper, we propose the advanced fire-flame detection algorithm using camera image for better performance than previous sensors-based systems which is limited on small area. Also, previous works using camera image were depend on a lot of heuristic thresholds or required an additional computation time. To solve these problems, we use statistical values and divide image into blocks to reduce the processing time. First, from the captured image, candidate flame regions are detected by a background model and fire colored models of the fire-flame. After the probability models are formed using the change of luminance, wavelet transform and the change of motion on time axis, they are used for membership function of fuzzy logic. Finally, the result function is made by the defuzzification, and the probability value of fire-flame is estimated. The proposed system has shown better performance when it compared to Toreyin's method which perform well among existing algorithms.