Smoke detection
First Claim
1. A method of training a classifier for a smoke detector, comprising:
- inputting sensor data from a plurality of tests into a processor, the sensor data indicative of environmental conditions during the tests;
using the processor to process the sensor data from the tests to generate derived signal data corresponding to the test data for respective tests;
assigning the derived signal data into categories comprising at least one fire group and at least one non-fire group;
performing linear discriminant analysis (LDA) training using the processor and the derived signal data and the assigned categories for the derived signal data as input to the LDA training, the output of the LDA training generating a centroid in linear discriminant coordinates for each of the categories, a plurality of coefficients for transforming derived signal data into linear discriminant (LD) coordinates, and a mean of group means; and
storing the plurality of coefficients, the plurality of centroids, and the mean of group means in a computer readable medium.
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Accused Products
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.
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Citations
24 Claims
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1. A method of training a classifier for a smoke detector, comprising:
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inputting sensor data from a plurality of tests into a processor, the sensor data indicative of environmental conditions during the tests; using the processor to process the sensor data from the tests to generate derived signal data corresponding to the test data for respective tests; assigning the derived signal data into categories comprising at least one fire group and at least one non-fire group; performing linear discriminant analysis (LDA) training using the processor and the derived signal data and the assigned categories for the derived signal data as input to the LDA training, the output of the LDA training generating a centroid in linear discriminant coordinates for each of the categories, a plurality of coefficients for transforming derived signal data into linear discriminant (LD) coordinates, and a mean of group means; and storing the plurality of coefficients, the plurality of centroids, and the mean of group means in a computer readable medium. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method of detecting a hazardous condition, comprising:
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inputting sensor data from a plurality of tests into a processor, the sensor data indicative of environmental conditions during the test; processing the sensor data from the plurality of tests, using the processor to generate derived signal data corresponding to the test data for respective tests; assigning at least one group to the derived signal data for a respective test, the at least one group selected from a plurality of groups including a normal group, a flaming fire group, and a non-flaming group; performing linear discriminant analysis (LDA) training using the derived signal data and the assigned at least one group for the respective tests as input to the LDA training, the output of the LDA training generating a plurality of transformation coefficients for transforming derived signal data into linear discriminant (LD) coordinates, a mean of group means, and a plurality of centroids in linear discriminant coordinates, wherein the plurality of centroids includes a different centroid for each of the plurality of groups; storing the plurality of transformation coefficients, the mean group of means, and the plurality of centroids into a computer-readable memory of a smoke detector; providing one or more sensors coupled to the smoke detector for sensing present environmental conditions and providing data corresponding to the sensed present environmental conditions, the data being provided in a plurality of data channels; mapping the data from the plurality of data channels into linear discriminant space using the plurality of stored transformation coefficients; determining the nearest centroid of the plurality of stored centroids to the data from the plurality of data channels mapped into linear discriminant space; and signaling an alarm if the nearest centroid is associated with a centroid in a group corresponding to a hazardous condition. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
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23. A smoke detector, comprising:
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a computer readable medium including linear discriminant analysis (LDA) training output data generated by; inputting sensor data from a plurality of tests, the sensor data indicative of environmental conditions during the respective tests; processing the sensor data to generate derived signal data for the respective tests; assigning at least one group to the derived signal data for the respective tests, the at least one group selected from a plurality of groups, each group of the plurality of groups associated with a hazardous condition or a non-hazardous condition; and performing LDA training using the derived signal data and the assigned at least one group for the respective tests as input to the LDA training, the output of the LDA training generating a plurality of transformation coefficients for transforming derived signal data into linear discriminant (LD) coordinates, a mean of group means, and a plurality of centroids in linear discriminant coordinates, wherein the plurality of centroids includes a different centroid for each group of the plurality of groups; at least one sensor configured to observe present environmental conditions, the at least one sensor comprising an aerosol sensor; a processor operatively connected to the computer readable memory and the at least one sensor, the processor configured to; process data from the at least one sensor to provide data in a plurality of data channels indicative of the present environmental conditions; map the data from the plurality of data channels into linear discriminant space using the plurality of transformation coefficients stored in the computer readable medium; classify the present environmental conditions as belonging to one group of the plurality of groups based on the linear discriminant mapping of the data from the plurality of data channels; and signal an alarm condition if the present environmental conditions are classified as belonging to a group associated with a hazardous condition; and an alarm operatively connected to the processor, the alarm generating an audible alert, a visual alert, or a combination thereof in response to the alarm signal. - View Dependent Claims (24)
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Specification