Application of Neural Networks for Discriminating Fire Detectors.
J A. Milke
Research is being conducted to describe the characteristics of an improved fire detector which promptly reacts to smoke while discriminating between smoke and odors from fire and non-fire sources. This study is investigating signature patterns associated with fire and environmental sources via small- and large-scale tests toward the development of an improved fire detector. On the tests, smoke and odors are produced from a variety of conditions: flaming, pyrolyzing and heated samples, and nuisance sources, such as aerosols, household products and cooked food. Measurements include light obscuration, temperature, mass loss, CO, CO2, O2 and oxidizable gas concentrations. The feasibility of an elementary expert system to classify the source of the signatures from small-scale experiments was demonstrated in the first phase. In the recently completed second phase, a similar expert system correctly classified the source of the signatures in large-scale experiments in 85% of the cases. Neural networks have been applied to both sets of data from the small- and large-scale tests providing an even greater successful classification rate.
University of Duisburg. International Conference on Automatic Fire Detection "AUBE '95", 10th
fire detection, fire detectors, experiments, small scale fire tests, large scale fire tests, smoke, odors, expert systems, smoldering, neural networks, light obscuration
Application of Neural Networks for Discriminating Fire Detectors., University of Duisburg. International Conference on Automatic Fire Detection "AUBE '95", 10th, Duisburg, , [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=916699
(Accessed December 6, 2023)