Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Artem Shulhin"'
Publikováno v:
Journal of KONBiN. 52:167-176
In this article, the computational simulation of the workflow in the primary combustion chamber flame tube of the afterburning turbofan jet engine (ATJE) on the tactical military aircraft was carried out. The geometric model of a flame tube was creat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ef5f0e95a56e0da9773b3e2d3f1cea7
https://doi.org/10.2478/jok-2022-0048
https://doi.org/10.2478/jok-2022-0048
Publikováno v:
Journal of KONBiN, Vol 51, Iss 3, Pp 65-73 (2021)
The operational technique for the major components of the fighter aircraft engine rotors has been introduced basing on the real conditions of their cyclic loading in each flight or ground test and a priori information on their previous operation. It
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51720431fe7e8987f1cbfe9efa9e448b
https://doaj.org/article/48456b72080d4f8cb1f52a3eac47f346
https://doaj.org/article/48456b72080d4f8cb1f52a3eac47f346
Autor:
Oleg Sova, Andrii Shyshatskyi, Yurii Zhuravskyi, Olha Salnikova, Oleksandr Zubov, Ruslan Zhyvotovskyi, Іgor Romanenko, Yevhen Kalashnikov, Artem Shulhin, Alexander Simonenko
Publikováno v:
Eastern-European Journal of Enterprise Technologies, Vol 2, Iss 4 (104), Pp 6-14 (2020)
The method of training artificial neural networks for intelligent decision support systems is developed. A distinctive feature of the proposed method is that it provides training not only of the synaptic weights of the artificial neural network, but
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9130a2266ef38a08e4ed0051503ee5a4
https://doi.org/10.15587/1729-4061.2020.199469
https://doi.org/10.15587/1729-4061.2020.199469
Autor:
Qasim Abbood Mahdi, Andrii Shyshatskyi, Oleksandr Symonenko, Nadiia Protas, Oleksandr Trotsko, Volodymyr Kyvliuk, Artem Shulhin, Petro Steshenko, Eduard Ostapchuk, Tetiana Holenkovska
We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09053e811e9355ad9b63193c2b63615a
https://zenodo.org/record/6362407
https://zenodo.org/record/6362407
