• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.235-238
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• 2020

In the smart home era, fire safety is very important for human life and facility safety. Casualties and property damage from the fire would be a huge national loss. In this paper, we propose to predict the risk by determining the operating time of the fire detector according to the fire in the smart home. Among IoT fire detectors, heat detectors and smoke detectors, the risk can be predicted due to the difference in the operating time depending on the fire. Based on the results of this experiment, the ion-type smoke detector shows very fast characteristics, so it would be good to use the results in future fire prevention facility.

• Journal of the Society of Naval Architects of Korea
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• v.41 no.5
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• pp.63-69
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• 2004

This study contains the CFD analysis to predict the flow in engine room and utilize the results as a reference for arranging smoke detectors. FLUENT, a commercial CFD code is adopted because of its good application experience in DSME(Daewoo Shipbuilding & Marine Engineering Co.. Ltd.). The target is the engine room of VLCC. which was delivered in 2002. The model for analysis includes main structure elements, ventilation ducts, main engine and other big size equipment. From the analysis results, the internal flow pattern can be observed and some guidelines for the position of smoke detectors cane be presented.

• Fire Science and Engineering
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• v.24 no.1
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• pp.111-115
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• 2010

For the development of fire detector equipments that was fused independent type of fire detector and a wireless communication function, this paper adopted a signal processing method of an independent formation fire sensor by a ZigBee communication method, and it was designed that the combinations of fire detector and wireless communications network were enabled. When a fire occurred, output voltage of a fire detector was 0.4 V, the movement pulse width was $600{\mu}s$, and that was confirmed by an experiment. When it has lookout state, LED worked by 30 s cycle and working current is 0.12 mA. When a fire occurred, LED worked by 0.5 s cycle and working current was 7 mA, and through this experiment of power source, Chamber tester, smoke tester,IRED, LED lighting, the propriety of the method that we showed in this paper confirmed.

• Fire Science and Engineering
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• v.30 no.4
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• pp.1-5
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• 2016

This research performed tests of Video Fire Detector and criteria of installation to make suggestions regarding the criteria that must be reflected in NFSC 203 by comparing the standards of FM Approvals, UL, ISO7240 and NFPA 72. FM Standard related to Video Fire Detector test has been classified as Smoke, Flame type, but the UL Standard has classified only as a Smoke type. This research examined 6 cases of fire phenomenon detection case in ISO 7240 and 3 cases in NFPA 72, respectively. There are 15 items required for the installation standard of a Video Fire Detector and each field standard is presented as a per installation method. To apply a Video Fire Detector, the pertinent items (the definition of term, detector's classification, structure and function among its test item) must be inserted. In addition, 7 items of the fire test, i.e., the sensitivity adjustment, prevent false alarm, ambient temperature test, the effective sensitivity and detection distance and viewing angle, aging test, flood test, must be applied to the actual test. For installation in the field, the operation environment and levels of illumination, and NFSC 203 must be set, and standards relevant to the sound system, indicators' installation distance, etc. need to be inserted.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2002.11a
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• pp.152-157
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• 2002

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Fire Science and Engineering
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• v.29 no.4
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• pp.81-88
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• 2015

A low-cost device using the light-extinction method was developed to measure the optical smoke density in various fire experiments in the present study. The relative measurement accuracy of low-cost device was evaluated through the comparison of optical density measured by a high-cost standard device consisting of He-Ne laser, photo detector and various optical components. The low-cost device was composed of laser module, photocell and acrylic board. From the experiments using a smoke generator can be easily adjusted the smoke concentration, it was found that the low-cost device could measure the smoke density within the range of ${\pm}10%$, compared to the standard device. In addition, the reliability of low-cost device was also confirmed in the experiment using a polyethylene flame. Finally, it is expected that the low-cost device developed with real-time measurement and simple installation for measuring the smoke density will be used instead of the high-cost standard device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1301-1314
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• 2018

In this paper, we present a low-cost fire detection system using a thermal camera and a smartphone. The developed system collects thermal and RGB videos from the developed camera. To detect fire, candidate fire regions are extracted from videos obtained using a thermal camera. The block mean of variation of adjacent frames is measured to analyze the dynamic characteristics of the candidate fire regions. After analyzing the dynamic characteristics of regions of interest, a fire is determined by the candidate fire regions. In order to evaluate the performance of our system, we compared with a smoke detector, a heat detector, and a flame detector. In the experiments, our fire detection system showed the excellent performance in detecting fire with an overall accuracy rate of 97.8 %.

• Fire Science and Engineering
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• v.10 no.3
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• pp.19-28
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• 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).

• Fire Science and Engineering
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• v.28 no.6
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• pp.58-61
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• 2014

Flame detectors has been used more than heat & smoke detector, because this is more effective to catch fire and ceiling height in spite of high price. For upgrade to flame detector, Compare to national or abroad standard about performance test, sensitivity test, sectioning indoor or outdoor, after service and management. As a result, Europe and America have only sensitivity test, no have not operation test and sectioning indoor & outdoor. In performance test, action is put to focus to credit and durability, Not operation test is to be able to tune the sensitivity to match the environment of the site. Also, we need code about aftercare, quality assurance responsibility and implement special detector institution by Certificate Authority implement.