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Optical Flame Detection Using Large-Scale Artificial Neural Networks

By Javid Huseynov, Zvi Boger, Gary Shubinsky and Shankar Baliga


Abstract – A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a large number of inputs is presented. Joint time-frequency analysis in the form of Short-Time Fourier Transform was used for extracting the relevant features from infrared sensor signals. After appropriate scaling, this information was provided as an input for the ANN training algorithm based on conjugate-gradient (CG) descent method. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for an industrial hydrocarbon flame detector. I

Year: 2005
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