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:
- Issue Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1320884
- Patent Number(s):
- 9437092
- Application Number:
- 14/859,631
- Assignee:
- UT-Battelle, LLC (Oak Ridge, TN)
- Patent Classifications (CPCs):
-
G - PHYSICS G08 - SIGNALLING G08B - SIGNALLING OR CALLING SYSTEMS
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2015 Sep 21
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; 99 GENERAL AND MISCELLANEOUS
Citation Formats
Warmack, Robert J. Bruce, Wolf, Dennis A., and Frank, Steven Shane. Smoke detection. United States: N. p., 2016.
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. https://www.osti.gov/servlets/purl/1320884.
@article{osti_1320884,
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 Sep 06 00:00:00 EDT 2016},
month = {Tue Sep 06 00:00:00 EDT 2016}
}
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