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Filtros : "PROBABILIDADE" "MACIEL, CARLOS DIAS" Removidos: "Universidade Federal do Ceará (UFC)" "Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)" Limpar

Filtros

Artigo de periodico
A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders (2021)
Fanucchi, Rodrigo Zempulski; Bessani, Michel; Camillo, Marcos Henrique Marçal; Desuó Neto, Luiz; Soares, Anderson da Silva ; Lima, Telma W. de; London Junior, João Bosco Augusto ; Dantas, Luciano B; Maciel, Carlos Dias
Source: Electric Power Systems Research. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, ALGORITMOS, PROBABILIDADE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FANUCCHI, Rodrigo Zempulski et al. A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders. Electric Power Systems Research, v. 196, 2021Tradução . . Disponível em: http://dx.doi.org/10.1016/j.yebeh.2021.108166. Acesso em: 11 out. 2024.
APA
Fanucchi, R. Z., Bessani, M., Camillo, M. H. M., Desuó Neto, L., Soares, A. da S., Lima, T. W. de, et al. (2021). A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders. Electric Power Systems Research, 196. doi:10.1016/j.epsr.2021.107198
NLM
Fanucchi RZ, Bessani M, Camillo MHM, Desuó Neto L, Soares A da S, Lima TW de, London Junior JBA, Dantas LB, Maciel CD. A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders [Internet]. Electric Power Systems Research. 2021 ; 196[citado 2024 out. 11 ] Available from: http://dx.doi.org/10.1016/j.yebeh.2021.108166
Vancouver
Fanucchi RZ, Bessani M, Camillo MHM, Desuó Neto L, Soares A da S, Lima TW de, London Junior JBA, Dantas LB, Maciel CD. A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders [Internet]. Electric Power Systems Research. 2021 ; 196[citado 2024 out. 11 ] Available from: http://dx.doi.org/10.1016/j.yebeh.2021.108166
Artigo de periodico
Efficient methods for learning Bayesian network super-structures (2014)
Villanueva, Edwin; Maciel, Carlos Dias
Source: Neurocomputing. Unidade: EESC
Subjects: INTELIGÊNCIA ARTIFICIAL (SISTEMAS), INFERÊNCIA BAYESIANA E REDES DE CRENÇA, PROBABILIDADE, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA, Edwin e MACIEL, Carlos Dias. Efficient methods for learning Bayesian network super-structures. Neurocomputing, v. 123, n. Ja 2014, p. 3-12, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2012.10.035. Acesso em: 11 out. 2024.
APA
Villanueva, E., & Maciel, C. D. (2014). Efficient methods for learning Bayesian network super-structures. Neurocomputing, 123( Ja 2014), 3-12. doi:10.1016/j.neucom.2012.10.035
NLM
Villanueva E, Maciel CD. Efficient methods for learning Bayesian network super-structures [Internet]. Neurocomputing. 2014 ; 123( Ja 2014): 3-12.[citado 2024 out. 11 ] Available from: https://doi.org/10.1016/j.neucom.2012.10.035
Vancouver
Villanueva E, Maciel CD. Efficient methods for learning Bayesian network super-structures [Internet]. Neurocomputing. 2014 ; 123( Ja 2014): 3-12.[citado 2024 out. 11 ] Available from: https://doi.org/10.1016/j.neucom.2012.10.035

Digital Library of Intellectual Production of Universidade de São Paulo 2012 - 2024