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Proceedings Paper

Optical infrared flame detection with neural networks
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Paper Abstract

A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.

Paper Details

Date Published: 25 September 2007
PDF: 10 pages
Proc. SPIE 6697, Advanced Signal Processing Algorithms, Architectures, and Implementations XVII, 66970L (25 September 2007); doi: 10.1117/12.731164
Show Author Affiliations
Javid J. Huseynov, Univ. of California, Irvine (United States)
General Monitors, Inc. (United States)
Shankar B. Baliga, General Monitors, Inc. (United States)


Published in SPIE Proceedings Vol. 6697:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVII
Franklin T. Luk, Editor(s)

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