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Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information
Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information
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机译:离散相空间状态的图论分析,用于条件变化检测和信息量化
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摘要
Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.
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