| Budget Amount *help |
¥13,910,000 (Direct Cost: ¥10,700,000、Indirect Cost: ¥3,210,000)
Fiscal Year 2015: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2014: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2013: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2012: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
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| Outline of Final Research Achievements |
Anomaly sign detection is a problem detecting subtle deviation of sensor data from normal data for monitoring patients, industrial plant, and so on. Anomaly sign detection can be realized as a problem measuring the discrepancy between observed data and estimated sensor data by non-linear regression. In this research, we developed anomaly sign detector based on Gaussian Process Regression (GPR). As the results of this research, we proposed 1) “Anomaly Measure” representing the ratio of the discrepancy and GPR estimated standard deviation, 2) multi-scale derivation and visualization of anomaly measure “Spectro Anomaly Gram(SAG)”, 3) an acceleration of GPR computation “Dynamic Active Set(DAS)”, 4) “MultiVariate GPR (MVGPR)” estimating vector output and covariant matrix, 5) “Reweighted MVGPR” to improve improper covariant matrix estimated by MVGPR, and so on. We applied our methods to industrial plant data and ECG data, and confirmed the effectiveness.
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