Neural networks to classify atmospheric turbulence from fight test data: an optimization of input parameters for a generic model (2022)
- Authors:
- USP affiliated authors: BIDINOTTO, JORGE HENRIQUE - EESC ; OLIVEIRA, MATHEUS MARCONDES DE - EESC ; MAYOR, GABRIEL SOTTO - EESC ; MACEDO, JOÃO PAULO COSTA ANTUNES DE - EESC
- Unidade: EESC
- DOI: 10.1007/s40430-022-03386-1
- Subjects: REDES NEURAIS; TURBULÊNCIA ATMOSFÉRICA; VOO (ENGENHARIA AERONÁUTICA); ENGENHARIA AERONÁUTICA
- Language: Inglês
- Imprenta:
- Publisher: Springer
- Publisher place: Heidelberg, Germany
- Date published: 2022
- Source:
- Título do periódico: Journal of the Brazilian Society of Mechanical Sciences and Engineering
- ISSN: 1678-5878
- Volume/Número/Paginação/Ano: v. 44, Article number 82, p. 1-11, 2022
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
OLIVEIRA, Matheus Marcondes et al. Neural networks to classify atmospheric turbulence from fight test data: an optimization of input parameters for a generic model. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 44, p. 1-11, 2022Tradução . . Disponível em: https://doi.org/10.1007/s40430-022-03386-1. Acesso em: 19 abr. 2024. -
APA
Oliveira, M. M., Sotto Mayor, G., Macedo, J. P. C. A. de, & Bidinotto, J. H. (2022). Neural networks to classify atmospheric turbulence from fight test data: an optimization of input parameters for a generic model. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, 1-11. doi:10.1007/s40430-022-03386-1 -
NLM
Oliveira MM, Sotto Mayor G, Macedo JPCA de, Bidinotto JH. Neural networks to classify atmospheric turbulence from fight test data: an optimization of input parameters for a generic model [Internet]. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2022 ; 44 1-11.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1007/s40430-022-03386-1 -
Vancouver
Oliveira MM, Sotto Mayor G, Macedo JPCA de, Bidinotto JH. Neural networks to classify atmospheric turbulence from fight test data: an optimization of input parameters for a generic model [Internet]. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2022 ; 44 1-11.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1007/s40430-022-03386-1 - Loss of control in flight: comparing qualitative pilot opinion with quantitative flight data
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Informações sobre o DOI: 10.1007/s40430-022-03386-1 (Fonte: oaDOI API)
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