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Efficient Classification of Polarization Events Based on Field Measurements

Kyle Guan, J. E. Simsarian, Fabien Boitier, Daniel C. Kilper, Jelena Pesic, Michael Sherman

Year
2020
Citations
8

Abstract

We present rare-event classification of polarization transients based on field measurements with data augmentation combined with robot-generated fiber-disturbance data. We compare machine learning methods for accuracy and required number of training sample traces.

Keywords

Computer sciencePolarization (electrochemistry)Artificial intelligenceRobotTraining setEvent (particle physics)Field (mathematics)Machine learningData miningPattern recognition (psychology)

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