USA As Represented by Secretary of the Navy

Mark H Hammond

Susan L Rose-Pehrsson

Sean J Hart

Kevin J Johnson

Daniel T Gottuk

Michael Paul Malito

Ronald Eugene Shaffer

Robert E Morris

Braden C Giordano

Greg E Collins

Cy R Tamanaha

Adam L Lubrano

Jeffrey A Cramer

Ronald E Schaffer

Apparatuses and methods for generating trace vapors are provided. The apparatus includes a controller and an oven. The controller includes: a processor, a memory storing at least one control program, a clean solution supply port constructed to output a clean solution, an analyte solution supply port constructed to output an analyte solution, a carrier gas inlet port constructed to receive a carrier gas, and a plurality of carrier gas supply controllers constructed to output the carrier gas. The oven includes a clean manifold, an analyte manifold, a clean solution nebulizer constructed to: receive the clean solution from the clean solution supply port, and the carrier gas from one of the plurality of carrier gas supply controllers, and output a clean solution vapor stream comprising the clean solution and the carrier gas to the clean manifold, an analyte solution nebulizer constructed to: receive the analyte solution from the analyte solution supply port and the carrier gas from another one of the plurality of carrier gas supply controllers, and output an analyte solution vapor stream comprising the analyte solution and the carrier gas to the analyte manifold, a pneumatic valve controllably connected to the processor and communicatively connected to the clean manifold and the analyte manifold, and an output supply port communicatively connected to the pneumatic valve. The controller is configured to operate the pneumatic valve to allow the clean vapor solution or the analyte vapor solution to enter the chamber and be provided to the output supply port.

Methods and apparatuses for generating trace vapors

A system is described that includes a known fuels database of data from gas chromatography-mass spectrometry analyses of a library of fuels with known fuel properties for a multiple known fuel samples. Gas chromatography-mass spectrometry equipment can acquire gas chromatography-mass spectrometry data for an unknown fuel sample. A metaspectrum module can accept and transform the gas chromatography-mass spectrometry data collected by the gas chromatography-mass spectrometry equipment for the unknown fuel sample into a single metaspectrum for the unknown fuel sample, wherein the metaspectrum is a quantitative representation of every compound detected in the unknown fuel sample. A correlation module can correlate the metaspectrum for the unknown fuel sample to a plurality of fuel properties of known fuel samples using a regression model to predict fuel properties for the unknown fuel sample. A reporting module can report the fuel properties for the unknown fuel sample to a user.

Prediction of fuel properties

A method and system of analyzing signal-vector data from first order sensors including providing a training data set, adjusting the training data set using a background adjustment technique, normalizing and transforming the training data set into wavelet coefficients, using an automated analysis of variance feature selection technique and a pattern recognition technique to classify the training data set. The method and system may also include performing these operations on an unknown sample data set collected under unknown conditions and comparing the unknown sample data set to the classification model to provide an identity of the unknown conditions associated with the unknown sample data set. The present invention is also directed to a computer system for analyzing signal-vector data according to this method and a sensing system that includes a sensor and a microprocessor on which is stored a classification model for real-time sensing of unknown sample data sets.

Method and system for analyzing signal-vector data for pattern recognition from first order sensors

A multi-criteria event detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of an event 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-event, first data set; a second, event 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 an event, as opposed to a non-event or nuisance situation.

Probabilistic neural network for multi-criteria event detector

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.

Identification of fire signatures for shipboard multi-criteria fire detection systems

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Alexandria, VA, United States of America

Mark H Hammond