• 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.

• Fire Science and Engineering
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• v.18 no.4
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• pp.42-51
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• 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.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.85-90
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• 2013

Currently, the electric fire prediction system uses PIC(Peripheral Interface Controller) for controller microprocessor. PIC has a slower computing speed than DSP does, so its real-time computing ability is inadequate. So with the basic characteristics waveform during arc generation as the standard reference, the comparison to this reference is used to predict and alarm electric fire from arc. While such alarm can be detected and taken care of from a remote central server, that prediction error rate is high and remote control in mobile environment is not available. In this article, the arc detection of time domain and frequency domain and wavelet-based adaptation algorithm executing the adaptation algorithm in conversion domain were applied to develop an electric fire prediction system loaded with new real-time arc detection algorithm using DSP. Also, remote control was made available through iPhone environment-based app development which enabled remote monitoring for arc's electric signal and power quality, and its utility was verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.331-334
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• 2010

This paper proposes a new design of a video-base fire detection system using texture and color spatial distribution information. The video sequences used are taken in different days with different lighting conditions having different backgrounds. The time complexity of most previous vision-based fire detection techniques are very high due to lengthy programing. To overcome the problems of lengthy codes and time complexity, in this algorithm, at first we normalize the video image frames by size and color information. Then the spatial distribution of the color information is used to extract the candidate regions, later using visual texture of the fire, we detect the fire regions. The experimental results show an real-time fire detection over thousands of image frames, and have higher detection rate when compared to the conventional fire detection techniques.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.148-151
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• 2007

The CF(Counter-fire) is neutralizing enemy's all command control systems and fire support elements. It will weaken a battle continuous ability and an intension to fight. At the beginning of the CF is obtaining locations of targets using various detection assets. CF command center processes acquired target information and send it to attacking equipments. The targets are classified into two classes, preplanned target and target of opportunity The target of opportunity is potential threaten, so it needs to take a immediate and exact process for determining location of target of opportunity. This paper proposes the real-time processing algorithm for offensive weapons to strike target of opportunity, and presents the result of its performance.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.11a
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• pp.123-134
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• 2000

In this study, adapted Turbo Coding Algorithm for reducing communication error of fire detection system for marine vessels, especially image transmission. and proposed decoding speed increasing method of Turbo Coding Algorithm. The results are as follows : 1) Confirmed that a Turbo Code is so useful methods for reducing communication error in lots of noise environments. 2) Proposed technology in this study of speed increasing method of Turbo Coding Algorithm proved 2 times speed up effect than normal Turbo Code and communication error reducing as well in the board made by VHDL software & chips of ALTER Company.

• Fire Science and Engineering
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• v.22 no.4
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• pp.42-49
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• 2008

This research is to provide a scheme for an automatic fire alarm system with higher reliability through solving problems of malfunctioning (false or missing fire alarm) and power interruption (result from frequently unwanted activation, etc) of an automatic fire alarm system. A digital control system with microprocessor-based is proposed to reduce the possibility of malfunctioning through a combinational use of heat, smoke and CO sensors. Higher reliability could be achieved by these multiple sensors based fire detection system and fire distinction algorithm. In this research, we implemented actual fire detection system and conducted fire test to verify improvement on reliability.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.20-27
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• 2022

This article aims to develop an algorithm that detects fire pump defects by analyzing the current signals of an induction motor, which are triggered by changes in the flow rate and pressure of multistage volute pumps that are used for fire services. The operational status of the pumps was categorized into three: first, normal operation; second, a defect that is caused by a change in the current value; and third, a defect occasioned by a change in current, pressure, and flow rate. When a fire pump was in normal operation, the motor's operating current was measured between 5.06 A and 6.9 A, the flow rate was estimated at 0-0.27 m³/min, and the pressure ranged from 0 to 0.47 MPa. In the event that a defect was caused by an abnormal current value in the motor, it was attributed to the pump's adherence. Furthermore, if there was no source of water, the defect was considered to have been induced by phase-loss operation, no-load operation, or run-stop operation, with the current value of each scenario being measured at > 52.8 A, < 4.13 A, > 45.15 A, and < 3.8 A, respectively, placing its overall range between 0 and 50 A. The sources of defects were detected based on an analysis of the flow rate, pressure, and current, which represent the following causes: air inflow into the casing, inadequate suction of water, and reverse-phase operation, respectively. Each cause entailed the following values: when air seeped into the casing, the pressure was measured at 0.24 MPa irrespective of changes in the flow rate; when there was inadequate suction of water, the pressure was recorded between 0 and 0.05 MPa despite changes in the flow rate; and when the power line's reverse-phase loss was the cause of the defect, the pressure was measured at 0.33 MPa for a flow rate of 0 L/min, and a higher flow rate decreased the pressure to nearly 0 MPa. The results of this study will enable engineers to develop a pump defect detection algorithm that is based on an analysis of current, and this algorithm will facilitate the execution of a program that will control a fire pump defect detection system.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.149-154
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• 2014

Detection performance of fire detection in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes. In this paper, a smoke detection in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation, and also, statistical characteristics of smoke is applied to detect the smoke for separated candidate region. Through the experiments with input videos obtained from a various weather conditions, the proposed algorithms were useful to detect smoke in the outdoor.