@InProceedings{ReisJrAmbrSousSilv:2016:ReAmSa,
               author = "Reis Junior, Jos{\'e} Daniel and Ambrosio, Ana Maria and Sousa, 
                         Fabiano Luis de and Silva, Douglas Felipe da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Reproducing Amazonia-1 Satellite Thermal Behavior with Artificial 
                         Neural Networks",
            booktitle = "Anais...",
                 year = "2016",
               editor = "Cerqueira, Christopher Shneider and Souza, Alain Giacobini de and 
                         Oliveira Junior, Eloy Martins de and Bertoldo Junior, Jorge and 
                         Yassuda, Irineu dos Santos and Lima, Jeanne Samara dos Santos and 
                         Morais, Marcelo Henrique Essado de and Oliveira, M{\^o}nica 
                         Elizabeth Rocha de and Gondo, Suely Mitsuko Hirakawa and Fornari, 
                         Celso Israel and Toledo, Rafael Cardoso and Fischer, Gustavo 
                         Alexandre Achilles",
         organization = "Workshop em Engenharia e Tecnologias Espaciais, 7. (WETE)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Artificial Satellite, Thermal Control, Real-Time Simulation, 
                         Artificial Neural Networks.",
             abstract = "The Operational Simulator is a software tool designed to support 
                         the operation phase of space systems. The thermal model of the 
                         Operational Simulator can be very demanding in terms of 
                         computational processing, if the simulator aims to be accurate in 
                         comparison to the actual spacecraft thermal model. Here we propose 
                         the use of Artificial Neural Networks to learn and reproduce the 
                         thermal behavior of the Amazonia-1 satellite. The results are very 
                         promising and indicate that this approach can be applied, at least 
                         for a few scenarios as used in this work.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos",
      conference-year = "23-25 ago. 2016",
                 issn = "2177-3114",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGPDW34R/3M6N9UB",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3M6N9UB",
           targetfile = "Artigo WETE 2016 vFinal.doc",
                 type = "Engenharia e Gerenciamento de Sistemas Espaciais",
        urlaccessdate = "28 abr. 2024"
}