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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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We assess the ability of variants of anomalous change detection (ACD) to identify human activity associated with large outdoor music festivals as they are seen from synthetic aperture radar (SAR) imagery collected by the Sentinel-1 satellite constellation. We found that, with appropriate feature vectors, ACD using random-forest machine learning was most effective at identifying changes associated with the human activity.
The primary goal of this work is to develop and implement analytic methods for quantitative analysis of microstructure. These analytic methods are demonstrated on microstructures generated by the Sandia kinAic Monte Carlo code SPPARKS. This report documents progress to that end as well as incremental work towards larger aspirations of making process-structure-property connections for complex processes such as welding and additive manufacturing.
A letter from Meredith Boddiford for Brielle Luthman, GEL Laboratories, LLC, to Kim Swanson, Lawrence Livermore National Laboratory on June 29, 2021 regarding EFA Soil Project Level 4.
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This award was relinquished by the University of Alabama at Birmingham (UAB) shortly after transferred to Indiana University. No research activities were conducted at UAB under this award. No personnel were supported, no funds were expended, and no publications or other research outputs were produced at UAB.
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A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec.
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