• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.642-645
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• 2012

본 논문에서는 자외선 및 적외선 필터를 기반으로 불이 연소하면서 에너지를 빛과 열의 형태로 방출하는 산화 과정으로 여러 가지의 연소반응에 의해 표출 형태가 열, 전기, 연소가스, 복사 등으로 나타날 때, 이중 열복사로 빛이 방출되는 화염의 온도에 따라 각각 특성이 다른 파장을 활용한 적외선과 자외선(IR/UV) 필터를 활용한 불꽃영상 감지시스템 설계방안을 제안한다.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.516-521
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• 2010

In this paper, we propose a video-based intelligent unmanned fire surveillance system using fuzzy color models. In general, to detect heat or smoke, a separate device is required for a fire surveillance system, this system, however, can be implemented by using widely used CCTV, which does not need separate devices and extra cost. The systems called video-based fire surveillance systems use mainly a method extracting smoke or flame from an input image only. The smoke is difficult to extract at night because of its gray-scale color, and the flame color depends on the temperature, the inflammable, the size of flame, etc, which makes it hard to extract the flame region from the input image. This paper deals with a intelligent fire surveillance system which is robust against the variation of the flame color, especially at night. The proposed system extracts the moving object from the input image, makes a decision whether the object is the flame or not by means of the color obtained by fuzzy color model and the shape obtained by histogram, and issues a fire alarm when the flame is spread. Finally, we verify the efficiency of the proposed system through the experiment of the controlled real fire.

• 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.

• 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.27 no.5
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• pp.15-18
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• 2013

This paper is concerned with the Performance and Reliability Certification for fire detection system in outdoor area such small and middle sized cultural assets, natural monument and outdoor facilities. Especially, if a fire were to occur in vulnerable area, it is difficulty to detect a fire. therefore we propose a high efficiency and low cost unmanned fire detection system in capable of an early detection regardless spontaneously fire or firebug. for Adoption of Intelligent Fire Detection System with movable and unmanned function breaking from the existing Conventional Fire Detection System, this Range of R&D includes the Performance test, Function test, Field test, Flame Detection test and EMI/EMS Compliance test. the Result data of Performance test, Function test and Field test is generally good during 3 months. also we checked that thermal variation test and EMI/EMS compliance test are good result data within allowable range. As a result of general test, we verified improvement results that the measure distance of fire detection extend 75 m, the Power of waiting time increase 4 hours, the Power of operation time increase 3 days and the context awareness with video as well as sensors.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.245-247
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• 2022

컨테이너선에서 스위치보드와 리퍼컨테이너의화재 발생 전과 후를 CCTV 및 열화상카메라, 추가센서류로 상시 모니터링하여, 화재 전 이상감지와 화재 후 불꽃, 연기 등의 화재 종류를 선내 알림하여 즉시 조치할 수 있도록 효율적인 화물관리시스템을 설계하고, 정확도 높은 화재 감지를 목적으로 데이터 학습을 위한 영상 및 전류 데이터 수집과 실제 컨테이너 스위치보드 및 리퍼컨테이너 레이아웃을 모사한 Testbed를 구축하였습니다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.93-96
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• 2015

In this paper, we introduce a fire detection system using context-aware sensor. In existing weather and based on vision sensor of fire detection system case, acquired image through sensor of camera is extracting features about fire range as processing to convert HSI(Hue, Saturation, Intensity) model HSI which is color space can have durability in illumination changes. However, in this case, until a fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. Additionally, the fire detection in complex situations as well as difficult to separate continuous boundary is set for the required area is difficult. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire it. In addition, it is possible to differential management to intensive fire detection is required zone dividing the state of fire.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.463-466
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• 2010

In this paper, CCTV based fire surveillance system should aim to development. Advantages and Disadvantages analyzed of Existing sensor-based fire surveillance system and video-based fire surveillance system. To national support U-City, U-Home, U-Campus, etc, spread the ubiquitous environment appropriate to fire surveillance system model and a fire judgement technology. For this study, Microsoft LifeCam VX-1000 using through the capturing images and analyzed for apple and tomato, Finally we used H.264. The client uses the Linux OS with ARM9 S3C2440 board was manufactured, the client's role is passed to the server to processed capturing image. Client and the server is basically a 1:1 video communications. So to multiple receive to video multicast support will be a specification. Is fire surveillance system designed for multiple video communication. Video data from the RGB format to YUV format and transfer and fire detection for Y value. Y value is know movement data. The red color of the fire is determined to detect and calculate the value of Y at the fire continues to detect the movement of flame.