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http://dx.doi.org/10.5573/ieie.2017.54.4.50

S-FDS : a Smart Fire Detection System based on the Integration of Fuzzy Logic and Deep Learning  

Jang, Jun-Yeong (School of Computer Science and Engineering Koreatech University)
Lee, Kang-Woon (School of Computer Science and Engineering Koreatech University)
Kim, Young-Jin (School of Computer Science and Engineering Koreatech University)
Kim, Won-Tae (School of Computer Science and Engineering Koreatech University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.4, 2017 , pp. 50-58 More about this Journal
Abstract
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.
Keywords
fire detection; multi-sensors; fuzzy logic; deep learning; IoT;
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Times Cited By KSCI : 2  (Citation Analysis)
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