Identification of fire signatures for shipboard multi-criteria fire detection systems
First Claim
1. A multi-criteria fire detection system, comprising:
- a plurality of sensors, wherein each said sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating same;
a processor for receiving each of said outputs of said plurality of sensors, said processor including a probabilistic neural network for processing said outputs, and wherein said probabilistic neural network comprises a nonlinear, non-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter, and wherein said plurality of data sets includes;
a baseline, non-fire, first data set;
a second, fire data set; and
a third, nuisance data set;
whereby said algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discriminating between said data sets and whether said outputs suffice to substantially indicate the presence of a fire as opposed to a non-fire or nuisance situation.
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Abstract
A multi-criteria fire detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-fire, fist data set; a second, fire data set, and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of a fire, as opposed to a non-fire or nuisance situation.
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Citations
4 Claims
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1. A multi-criteria fire detection system, comprising:
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a plurality of sensors, wherein each said sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating same; a processor for receiving each of said outputs of said plurality of sensors, said processor including a probabilistic neural network for processing said outputs, and wherein said probabilistic neural network comprises a nonlinear, non-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter, and wherein said plurality of data sets includes; a baseline, non-fire, first data set; a second, fire data set; and a third, nuisance data set; whereby said algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discriminating between said data sets and whether said outputs suffice to substantially indicate the presence of a fire as opposed to a non-fire or nuisance situation. - View Dependent Claims (2, 3)
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4. A method for detecting the presence of a fire, comprising the steps of:
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establishing a plurality of data sets, said data sets including; a baseline, non-fire, first data set; a second, fire data set; and a third, nuisance data set; training each of said data sets to respond to an input and provide a representative output; sensing a plurality of signatures of a fire; encoding each of said plurality of signatures in a numerical output representative of a point or location in a multidimensional space; inputting each said numerical output to a probabilistic neural network that operates by defining a probability density function for each said data set based on said training set data and an optimized kernel width parameter; and correlating said numerical outputs to a location in said multidimensional space to determine the presence or absence of a fire at said location.
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Specification