Method and apparatus for event detection permitting per event adjustment of false alarm rate
First Claim
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1. A method for event detection permitting per event adjustment of false alarm rate, comprising the steps of:
- loading operational data;
loading background data;
comparing said operational data to said background data;
determining from said step of comparing, whether said operational data contains predictive data;
tagging predictive data at the time when and where it occurs;
extracting and reporting statistical features from said predictive data;
selecting said statistical features of choice so as to form at least one feature vector;
associating into a plurality of clusters, patterns formed by said at least one feature vector according to the character of said predictive data and said time at which it occurred;
optimizing false alarm thresholds and detection rates for each of said plurality of clusters;
detecting events; and
feeding back results.
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Abstract
Method and apparatus for object or event of interest detection which minimizes the level of false alarms and maximizes the level of detections as defined on a per event or object basis by the analyst. The invention allows for the minimization of false alarms for objects or events of interest which have a close resemblance to all other objects or events mapped to the same multidimensional feature space, and allows for the per event or per object adjustment on false alarms for objects or events of higher interest.
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Citations
14 Claims
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1. A method for event detection permitting per event adjustment of false alarm rate, comprising the steps of:
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loading operational data; loading background data; comparing said operational data to said background data; determining from said step of comparing, whether said operational data contains predictive data; tagging predictive data at the time when and where it occurs; extracting and reporting statistical features from said predictive data; selecting said statistical features of choice so as to form at least one feature vector; associating into a plurality of clusters, patterns formed by said at least one feature vector according to the character of said predictive data and said time at which it occurred; optimizing false alarm thresholds and detection rates for each of said plurality of clusters; detecting events; and feeding back results. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An apparatus for detecting process events, comprising
a computer; -
a software program; and a means for communicating inputs and outputs to and from said computer and said process;
whereinsaid software program further comprises a set of computer-implementable instructions stored on a non-transitory media, which, when executed by said computer cause said computer to perform the following steps; loading operational data; loading background data; comparing said operational data to said background data; determining from said step of comparing, whether said operational data contains predictive data; tagging predictive data at the time when and where it occurs; extracting and reporting statistical features from said predictive data; selecting said statistical features of choice so as to form at least one feature vector; associating into a plurality of clusters, patterns formed by said at least one feature vector according to the character of said predictive data and said time at which it occurred; optimizing false alarm thresholds and detection rates for each of said plurality of clusters; detecting events; and feeding back results. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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Specification