Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Kennedy Krakalovich"'
Autor:
Keeley Edwards, Nicholas Geddert, Kennedy Krakalovich, Ryan Kruk, Mohammad Asefi, Joe Lovetri, Colin Gilmore, Ian Jeffrey
Publikováno v:
IEEE Access, Vol 8, Pp 207182-207192 (2020)
We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental fi
Externí odkaz:
https://doaj.org/article/108e74ff1c264dfc8584d8cbcb14a12b
Autor:
Nicholas Geddert, Ryan Kruk, Colin Gilmore, Joe LoVetri, Kennedy Krakalovich, Ian Jeffrey, Keeley Edwards, Mohammad Asefi
Publikováno v:
IEEE Access, Vol 8, Pp 207182-207192 (2020)
We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental fi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82ced6f5e89374fd23d573ebd24c9565
https://ieeexplore.ieee.org/document/9260139/
https://ieeexplore.ieee.org/document/9260139/
Autor:
Joe LoVetri, Ian Jeffrey, Colin Gilmore, Vahab Khoshdel, Ryan Kruk, Keeley Edwards, Kennedy Krakalovich
Publikováno v:
2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science.
We present a machine learning work flow for the parametric inversion of grain bin measurements in which a neural network is trained solely on synthetic data for a unique bin geometry. This neural network can subsequently be used to rapidly obtain 4 i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b91d972a5aea97b0c5d1c7a3235a30f9
https://doi.org/10.23919/ursigass49373.2020.9232216
https://doi.org/10.23919/ursigass49373.2020.9232216