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- W3010059102 abstract "The inverse scattering problem of non-spherical particle size estimation is solved using a series of supervised machine learning models trained on a library of light scattering data. By establishing a large library with spheres and spheroids as fundamental shapes and through optimization of model hyperparameters, the trained models are able to accurately estimate a precise equivalent volume sphere radius of particles from an external database and simulations, with root mean square errors of 2.6% and 1.9% for the external and simulated particles, respectively. It was found that classification via a <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline> <mml:mi>k</mml:mi> </mml:math> -nearest neighbor model and refinement via a trained ensemble regression model performed best for equivalent volume measurements." @default.
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- W3010059102 date "2020-03-30" @default.
- W3010059102 modified "2023-10-17" @default.
- W3010059102 title "Non-spherical particle size estimation using supervised machine learning" @default.
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- W3010059102 doi "https://doi.org/10.1364/ao.385750" @default.
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