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Title: Smoke detection

Abstract

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Inventors:
; ;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1224429
Patent Number(s):
9,171,453
Application Number:
14/162,547
Assignee:
UT-Battelle, LLC (Oak Ridge, TN) ORNL
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Patent
Resource Relation:
Patent File Date: 2014 Jan 23
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Warmack, Robert J. Bruce, Wolf, Dennis A., and Frank, Steven Shane. Smoke detection. United States: N. p., 2015. Web.
Warmack, Robert J. Bruce, Wolf, Dennis A., & Frank, Steven Shane. Smoke detection. United States.
Warmack, Robert J. Bruce, Wolf, Dennis A., and Frank, Steven Shane. Tue . "Smoke detection". United States. doi:. https://www.osti.gov/servlets/purl/1224429.
@article{osti_1224429,
title = {Smoke detection},
author = {Warmack, Robert J. Bruce and Wolf, Dennis A. and Frank, Steven Shane},
abstractNote = {Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 27 00:00:00 EDT 2015},
month = {Tue Oct 27 00:00:00 EDT 2015}
}

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Works referenced in this record:

Advanced fire detection algorithms using data from the home smoke detector project
journal, February 2005


Development Of A Fire Detection System Using Ft-ir Spectroscopy And Artificial Neural Networks
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Advanced fire detection using multi-signature alarm algorithms
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Multi-criteria fire detection systems using a probabilistic neural network
journal, October 2000

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