000			02099nab a2200193 4500
005			20260520001112.0
008			260224s2015 xxu ing
041			_aInglés
245	0	0	_aDistinction of Fire Source from Smoke Using Discrete Probability Distribution and Neural Networks
260			_a _b _cjul. 2015
270			_a14/09/2015 ; 14/09/2015
300			_a17 p. ; 887-904
520			_aTranscripción del resúmen realizada por el autor: Fire source distinction is useful in reducing the occurrence of false alarms and in choosing an effective fire extinguishing medium. This study was aimed at distinguishing fire sources through a smoke analysis. For this purpose, a smoke detection chamber was created, which was equipped with one light source and several light sensors for enabling simultaneous detection of light extinction and scattering, respectively. The test fires considered in this study had two kinds of sources: single fire source (paper, wood, and flammable liquid) and mixed fire source (paper-wood, paper-flammable liquid, and wood-flammable liquid mixtures). The amounts of extinction and scattering for each fire were measured experimentally, and the discrete probability distributions were calculated from the measured scattering amount. The optical characteristics of each fire were obtained using extinction data and the calculated probability distributions. These optical characteristics were then used for learning of neural networks, and the learned neural networks were used to distinguish fire sources from smoke generated in the case of both single fires and mixed fires. Results revealed that the neural networks could precisely distinguish fire sources on the basis of the smoke particles in the case of both single fires and mixed fires. The results of this study are expected to be useful in developing an advanced smoke detector that can distinguish fire sources in addition to detecting smoke.
581			_a4
773	0		_tFire Technology _g51
942			_cARTICULO
100	1		_aJee, Seung-Wook _954833
999			_c189188 _d189188