Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jason R. Smit"'
Publikováno v:
Atmosphere, Vol 15, Iss 8, p 873 (2024)
We present a novel deep learning approach to a unique image processing application: high-speed (>1000 fps) video footage of lightning. High-speed cameras enable us to observe lightning with microsecond resolution, characterizing key processes previou
Externí odkaz:
https://doaj.org/article/29fbc0df7dd54485b0e137b0bcb5afa6
Publikováno v:
2022 36th International Conference on Lightning Protection (ICLP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd6ade521cd82c90bc495be82c9f0960
https://doi.org/10.1109/iclp56858.2022.9942476
https://doi.org/10.1109/iclp56858.2022.9942476
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8eb29548130ff50084e9f50d4591014a
https://doi.org/10.2139/ssrn.4206677
https://doi.org/10.2139/ssrn.4206677
Autor:
Hugh G.P. Hunt, Carina Schumann, Jason R. Smit, Chandima Gomes, Ken J. Nixon, Ian R. Jandrell, Marcelo M.F. Saba, Tom A. Warner
Publikováno v:
2021 35th International Conference on Lightning Protection (ICLP) and XVI International Symposium on Lightning Protection (SIPDA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b733a47b18c23a25d9bbb9013c62596d
https://doi.org/10.1109/iclpandsipda54065.2021.9627418
https://doi.org/10.1109/iclpandsipda54065.2021.9627418
Publikováno v:
2021 35th International Conference on Lightning Protection (ICLP) and XVI International Symposium on Lightning Protection (SIPDA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a5fbd8a2869185f0059c5815cc0679f
https://doi.org/10.1109/iclpandsipda54065.2021.9627420
https://doi.org/10.1109/iclpandsipda54065.2021.9627420
Publikováno v:
2020 International SAUPEC/RobMech/PRASA Conference.
An investigation into the application of semantic segmentation in generating metrics from high speed footage is presented. Five pre-trained networks were assessed, DeepLabV3performed the best with 87.5 mean accuracy given limited GPU ram for training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3414ffef92da0748aabb53a73e479ec5
https://doi.org/10.1109/saupec/robmech/prasa48453.2020.9041123
https://doi.org/10.1109/saupec/robmech/prasa48453.2020.9041123
Publikováno v:
Atmosphere; Aug2024, Vol. 15 Issue 8, p873, 16p