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Trabalho de evento
Quantifying uncertainty in bayesian networks structural learning (2024)
Barth, Vitor Bruno de Oliveira; Caetano, Henrique de Oliveira; Maciel, Carlos Dias ; Aiello, Marco
Source: Proceedings. Conference titles: IEEE International Conference on Evolving and Adaptive Intelligent Systems. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, APRENDIZADO COMPUTACIONAL, ENGENHARIA ELÉTRICA
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ABNT
BARTH, Vitor Bruno de Oliveira et al. Quantifying uncertainty in bayesian networks structural learning. 2024, Anais.. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2024. Disponível em: https://dx.doi.org/10.1109/EAIS58494.2024.10570017. Acesso em: 05 dez. 2025.
APA
Barth, V. B. de O., Caetano, H. de O., Maciel, C. D., & Aiello, M. (2024). Quantifying uncertainty in bayesian networks structural learning. In Proceedings. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/EAIS58494.2024.10570017
NLM
Barth VB de O, Caetano H de O, Maciel CD, Aiello M. Quantifying uncertainty in bayesian networks structural learning [Internet]. Proceedings. 2024 ;[citado 2025 dez. 05 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570017
Vancouver
Barth VB de O, Caetano H de O, Maciel CD, Aiello M. Quantifying uncertainty in bayesian networks structural learning [Internet]. Proceedings. 2024 ;[citado 2025 dez. 05 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570017
Trabalho de evento
Intelligent resource allocation in power grids: leveraging variable neighborhood search for optimal switch allocation (2024)
Caetano, Henrique de Oliveira; Fogliatto, Matheus de Souza Sant'Anna ; Desuó Neto, Luiz; Pereira Junior, Benvindo Rodrigues ; Maciel, Carlos Dias ; Aiello, Marco
Source: Proceedings. Conference titles: IEEE International Conference on Evolving and Adaptive Intelligent Systems. Unidade: EESC
Subjects: MÉTODO DE MONTE CARLO, DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, ENGENHARIA ELÉTRICA
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ABNT
CAETANO, Henrique de Oliveira et al. Intelligent resource allocation in power grids: leveraging variable neighborhood search for optimal switch allocation. 2024, Anais.. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2024. Disponível em: https://dx.doi.org/10.1109/EAIS58494.2024.10570012. Acesso em: 05 dez. 2025.
APA
Caetano, H. de O., Fogliatto, M. de S. S. 'A., Desuó Neto, L., Pereira Junior, B. R., Maciel, C. D., & Aiello, M. (2024). Intelligent resource allocation in power grids: leveraging variable neighborhood search for optimal switch allocation. In Proceedings. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/EAIS58494.2024.10570012
NLM
Caetano H de O, Fogliatto M de SS'A, Desuó Neto L, Pereira Junior BR, Maciel CD, Aiello M. Intelligent resource allocation in power grids: leveraging variable neighborhood search for optimal switch allocation [Internet]. Proceedings. 2024 ;[citado 2025 dez. 05 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570012
Vancouver
Caetano H de O, Fogliatto M de SS'A, Desuó Neto L, Pereira Junior BR, Maciel CD, Aiello M. Intelligent resource allocation in power grids: leveraging variable neighborhood search for optimal switch allocation [Internet]. Proceedings. 2024 ;[citado 2025 dez. 05 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570012
Trabalho de evento
A multilevel crews patrolling framework for distribution system recovering (2022)
Fogliatto, Matheus de Souza Sant'Anna; Caetano, Henrique de Oliveira; Maciel, Carlos Dias ; Desuó Neto, Luiz; Fanucchi, Rodrigo Zempulski
Source: Proceedings. Conference titles: Annual IEEE International Systems Conference - SysCon 2022. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, MÉTODO DE MONTE CARLO, ENGENHARIA ELÉTRICA
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ABNT
FOGLIATTO, Matheus de Souza Sant'Anna et al. A multilevel crews patrolling framework for distribution system recovering. 2022, Anais.. Los Alamitos, CA, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/SysCon53536.2022.9773815. Acesso em: 05 dez. 2025.
APA
Fogliatto, M. de S. S. 'A., Caetano, H. de O., Maciel, C. D., Desuó Neto, L., & Fanucchi, R. Z. (2022). A multilevel crews patrolling framework for distribution system recovering. In Proceedings. Los Alamitos, CA, USA: IEEE. doi:10.1109/SysCon53536.2022.9773815
NLM
Fogliatto M de SS'A, Caetano H de O, Maciel CD, Desuó Neto L, Fanucchi RZ. A multilevel crews patrolling framework for distribution system recovering [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/SysCon53536.2022.9773815
Vancouver
Fogliatto M de SS'A, Caetano H de O, Maciel CD, Desuó Neto L, Fanucchi RZ. A multilevel crews patrolling framework for distribution system recovering [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/SysCon53536.2022.9773815
Trabalho de evento
Bayesian network for hydrological model: an inference approach (2022)
Ribeiro, Vitor P.; Cunha, Ângela Silviane Moura; Duarte, Sergio Nascimento ; Padovani, Carlos Roberto ; Marques, Patricia Algélica Alves ; Maciel, Carlos Dias ; Balestieri, José Antonio Perrella
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: ESALQ, EESC
Subjects: EVAPOTRANSPIRAÇÃO, BALANÇO HÍDRICO, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
RIBEIRO, Vitor P. et al. Bayesian network for hydrological model: an inference approach. 2022, Anais.. Piscataway, NJ, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/IJCNN55064.2022.9892468. Acesso em: 05 dez. 2025.
APA
Ribeiro, V. P., Cunha, Â. S. M., Duarte, S. N., Padovani, C. R., Marques, P. A. A., Maciel, C. D., & Balestieri, J. A. P. (2022). Bayesian network for hydrological model: an inference approach. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/IJCNN55064.2022.9892468
NLM
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Vancouver
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Trabalho de evento
AGAVaPS: adaptive genetic algorithm with varying population size (2022)
Ribeiro, Rafael Rodrigues Mendes; Maciel, Carlos Dias
Source: Proceedings. Conference titles: IEEE Congress on Evolutionary Computation - CEC. Unidade: EESC
Subjects: ALGORITMOS GENÉTICOS, HEURÍSTICA, ENGENHARIA ELÉTRICA
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ABNT
RIBEIRO, Rafael Rodrigues Mendes e MACIEL, Carlos Dias. AGAVaPS: adaptive genetic algorithm with varying population size. 2022, Anais.. Picataway, NJ, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/CEC55065.2022.9870394. Acesso em: 05 dez. 2025.
APA
Ribeiro, R. R. M., & Maciel, C. D. (2022). AGAVaPS: adaptive genetic algorithm with varying population size. In Proceedings. Picataway, NJ, USA: IEEE. doi:10.1109/CEC55065.2022.9870394
NLM
Ribeiro RRM, Maciel CD. AGAVaPS: adaptive genetic algorithm with varying population size [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/CEC55065.2022.9870394
Vancouver
Ribeiro RRM, Maciel CD. AGAVaPS: adaptive genetic algorithm with varying population size [Internet]. Proceedings. 2022 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/CEC55065.2022.9870394
Trabalho de evento
CO2 emissions forecasting in multi-source power generation systems using dynamic bayesian network (2021)
Santos, Talysson Manoel de Oliveira; Oliveira Júnior, Jordão Natal de; Maciel, Carlos Dias ; Bessani, Michel
Source: Proceedings. Conference titles: IEEE International Systems Conference - SysCon. Unidade: EESC
Subjects: RECURSOS ENERGÉTICOS, SUSTENTABILIDADE, GERAÇÃO DE ENERGIA ELÉTRICA, GÁS CARBÔNICO
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ABNT
SANTOS, Talysson Manoel de Oliveira et al. CO2 emissions forecasting in multi-source power generation systems using dynamic bayesian network. 2021, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2021. Disponível em: http://dx.doi.org/10.1109/SysCon48628.2021.9447104. Acesso em: 05 dez. 2025.
APA
Santos, T. M. de O., Oliveira Júnior, J. N. de, Maciel, C. D., & Bessani, M. (2021). CO2 emissions forecasting in multi-source power generation systems using dynamic bayesian network. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/SysCon48628.2021.9447104
NLM
Santos TM de O, Oliveira Júnior JN de, Maciel CD, Bessani M. CO2 emissions forecasting in multi-source power generation systems using dynamic bayesian network [Internet]. Proceedings. 2021 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SysCon48628.2021.9447104
Vancouver
Santos TM de O, Oliveira Júnior JN de, Maciel CD, Bessani M. CO2 emissions forecasting in multi-source power generation systems using dynamic bayesian network [Internet]. Proceedings. 2021 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SysCon48628.2021.9447104
Trabalho de evento
Neural network classification, coherence and power spectrum analysis with stress database (2019)
Pedrino, Mateus C.; Arone, Rafael Augusto Caracciolo; Tsukahara, Victor Hugo Batista; Maciel, Carlos Dias
Source: Proceedings. Conference titles: Latin American Workshop on Computational Neuroscience - LAWCN. Unidade: EESC
Subjects: REDES NEURAIS, ELETROENCEFALOGRAFIA
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ABNT
PEDRINO, Mateus C. et al. Neural network classification, coherence and power spectrum analysis with stress database. 2019, Anais.. São João del-Rei: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2019. Disponível em: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf. Acesso em: 05 dez. 2025.
APA
Pedrino, M. C., Arone, R. A. C., Tsukahara, V. H. B., & Maciel, C. D. (2019). Neural network classification, coherence and power spectrum analysis with stress database. In Proceedings. São João del-Rei: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
NLM
Pedrino MC, Arone RAC, Tsukahara VHB, Maciel CD. Neural network classification, coherence and power spectrum analysis with stress database [Internet]. Proceedings. 2019 ;[citado 2025 dez. 05 ] Available from: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
Vancouver
Pedrino MC, Arone RAC, Tsukahara VHB, Maciel CD. Neural network classification, coherence and power spectrum analysis with stress database [Internet]. Proceedings. 2019 ;[citado 2025 dez. 05 ] Available from: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
Trabalho de evento
Robustness of reconfigurable complex systems by a multi-agent simulation: application on power distribution systems (2018)
Bessani, Michel; Ribeiro, Rafael Rodrigues Mendes ; Pagani, Giuliano A.; Aiello, Marco; Maciel, Carlos Dias
Source: Proceedings. Conference titles: Annual IEEE International Systems Conference- SysCon. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, ROBUSTEZ, SIMULAÇÃO
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ABNT
BESSANI, Michel et al. Robustness of reconfigurable complex systems by a multi-agent simulation: application on power distribution systems. 2018, Anais.. Piscataway, NJ: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: http://dx.doi.org/10.1109/SYSCON.2018.8369553. Acesso em: 05 dez. 2025.
APA
Bessani, M., Ribeiro, R. R. M., Pagani, G. A., Aiello, M., & Maciel, C. D. (2018). Robustness of reconfigurable complex systems by a multi-agent simulation: application on power distribution systems. In Proceedings. Piscataway, NJ: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/SYSCON.2018.8369553
NLM
Bessani M, Ribeiro RRM, Pagani GA, Aiello M, Maciel CD. Robustness of reconfigurable complex systems by a multi-agent simulation: application on power distribution systems [Internet]. Proceedings. 2018 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SYSCON.2018.8369553
Vancouver
Bessani M, Ribeiro RRM, Pagani GA, Aiello M, Maciel CD. Robustness of reconfigurable complex systems by a multi-agent simulation: application on power distribution systems [Internet]. Proceedings. 2018 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SYSCON.2018.8369553
Trabalho de evento
An embedded state estimator for reducing data volume and processing in smart grids monitoring (2018)
Gonzaga Junior, Rosvando M; Massignan, Júlio A. D; Maciel, Carlos Dias ; London Junior, João Bosco Augusto ; Almeida, Rodrigo M. A. de; Camillo, Marcos H. M
Source: Proceedings. Conference titles: Workshop on Communication Networks and Power Systems -WCNPS. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, SISTEMAS EMBUTIDOS
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ABNT
GONZAGA JUNIOR, Rosvando M et al. An embedded state estimator for reducing data volume and processing in smart grids monitoring. 2018, Anais.. Brasília, DF: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: http://dx.doi.org/10.1109/WCNPS.2018.8604359. Acesso em: 05 dez. 2025.
APA
Gonzaga Junior, R. M., Massignan, J. A. D., Maciel, C. D., London Junior, J. B. A., Almeida, R. M. A. de, & Camillo, M. H. M. (2018). An embedded state estimator for reducing data volume and processing in smart grids monitoring. In Proceedings. Brasília, DF: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/WCNPS.2018.8604359
NLM
Gonzaga Junior RM, Massignan JAD, Maciel CD, London Junior JBA, Almeida RMA de, Camillo MHM. An embedded state estimator for reducing data volume and processing in smart grids monitoring [Internet]. Proceedings. 2018 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/WCNPS.2018.8604359
Vancouver
Gonzaga Junior RM, Massignan JAD, Maciel CD, London Junior JBA, Almeida RMA de, Camillo MHM. An embedded state estimator for reducing data volume and processing in smart grids monitoring [Internet]. Proceedings. 2018 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/WCNPS.2018.8604359
Trabalho de evento
Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study (2017)
Santos, Fernando P.; Smagula, Stephen F.; Karim, Helmet; Santini, Tales S.; Aizenstein, Howard J.; Ibrahim, Tamer S.; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, RESSONÂNCIA MAGNÉTICA, PROCESSAMENTO DE IMAGENS, ENGENHARIA ELÉTRICA
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ABNT
SANTOS, Fernando P. et al. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study. 2017, Anais.. Lisboa: SciTePress, 2017. Disponível em: https://doi.org/10.5220/0006151601780184. Acesso em: 05 dez. 2025.
APA
Santos, F. P., Smagula, S. F., Karim, H., Santini, T. S., Aizenstein, H. J., Ibrahim, T. S., & Maciel, C. D. (2017). Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study. In Proceedings. Lisboa: SciTePress. doi:10.5220/0006151601780184
NLM
Santos FP, Smagula SF, Karim H, Santini TS, Aizenstein HJ, Ibrahim TS, Maciel CD. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.5220/0006151601780184
Vancouver
Santos FP, Smagula SF, Karim H, Santini TS, Aizenstein HJ, Ibrahim TS, Maciel CD. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.5220/0006151601780184
Trabalho de evento
Determination of directional influences of kinematic data in the stance period during running (2017)
Nakashima, Giovana Yuko; Nakagawa, Theresa Helissa; Santos, Ana Flávia dos; Serrão, Fábio Viadanna; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Joint Conference on Biomedical Engineering Systems and Technologies -BIOSTEC. Unidade: EESC
Assunto: CINEMÁTICA
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ABNT
NAKASHIMA, Giovana Yuko et al. Determination of directional influences of kinematic data in the stance period during running. 2017, Anais.. Porto: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2017. Disponível em: http://dx.doi.org/10.5220/0006167701930197. Acesso em: 05 dez. 2025.
APA
Nakashima, G. Y., Nakagawa, T. H., Santos, A. F. dos, Serrão, F. V., & Maciel, C. D. (2017). Determination of directional influences of kinematic data in the stance period during running. In Proceedings. Porto: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.5220/0006167701930197
NLM
Nakashima GY, Nakagawa TH, Santos AF dos, Serrão FV, Maciel CD. Determination of directional influences of kinematic data in the stance period during running [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.5220/0006167701930197
Vancouver
Nakashima GY, Nakagawa TH, Santos AF dos, Serrão FV, Maciel CD. Determination of directional influences of kinematic data in the stance period during running [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.5220/0006167701930197
Trabalho de evento
A hierarchical framework for complex networks robustness analysis to errors (2017)
Bessani, Michel; Massignan, Júlio A. D; London Junior, João Bosco Augusto ; Maciel, Carlos Dias ; Fanucchi, Rodrigo Zempulski; Camillo, Marcos H. M
Source: Proceedings. Conference titles: IEEE International Systems Conference - SysCon. Unidade: EESC
Subjects: SISTEMAS ELÉTRICOS, DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, ROBUSTEZ, FALHA
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ABNT
BESSANI, Michel et al. A hierarchical framework for complex networks robustness analysis to errors. 2017, Anais.. Piscataway, NJ: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2017. Disponível em: http://dx.doi.org/10.1109/SYSCON.2017.7934812. Acesso em: 05 dez. 2025.
APA
Bessani, M., Massignan, J. A. D., London Junior, J. B. A., Maciel, C. D., Fanucchi, R. Z., & Camillo, M. H. M. (2017). A hierarchical framework for complex networks robustness analysis to errors. In Proceedings. Piscataway, NJ: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/SYSCON.2017.7934812
NLM
Bessani M, Massignan JAD, London Junior JBA, Maciel CD, Fanucchi RZ, Camillo MHM. A hierarchical framework for complex networks robustness analysis to errors [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SYSCON.2017.7934812
Vancouver
Bessani M, Massignan JAD, London Junior JBA, Maciel CD, Fanucchi RZ, Camillo MHM. A hierarchical framework for complex networks robustness analysis to errors [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/SYSCON.2017.7934812
Trabalho de evento
Modeling issues on load flow calculation for meshed distribution systems (2017)
Massignan, Júlio A. D; Hebling, Gustavo Miranda; Marques, Leandro Tolomeu; Bessani, Michel; Maciel, Carlos Dias ; London Junior, João Bosco Augusto ; Camillo, Marcos H. M
Source: Proceedings. Conference titles: IEEE Manchester PowerTech. Unidade: EESC
Assunto: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA
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ABNT
MASSIGNAN, Júlio A. D et al. Modeling issues on load flow calculation for meshed distribution systems. 2017, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2017. Disponível em: http://dx.doi.org/10.1109/PTC.2017.7981154. Acesso em: 05 dez. 2025.
APA
Massignan, J. A. D., Hebling, G. M., Marques, L. T., Bessani, M., Maciel, C. D., London Junior, J. B. A., & Camillo, M. H. M. (2017). Modeling issues on load flow calculation for meshed distribution systems. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/PTC.2017.7981154
NLM
Massignan JAD, Hebling GM, Marques LT, Bessani M, Maciel CD, London Junior JBA, Camillo MHM. Modeling issues on load flow calculation for meshed distribution systems [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/PTC.2017.7981154
Vancouver
Massignan JAD, Hebling GM, Marques LT, Bessani M, Maciel CD, London Junior JBA, Camillo MHM. Modeling issues on load flow calculation for meshed distribution systems [Internet]. Proceedings. 2017 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/PTC.2017.7981154
Trabalho de evento
Failure rate prediction under adverse weather conditions in an electric distribution system using negative binomial regression (2016)
Fanucchi, Rodrigo Zempulski; Bessani, Michel; Camillo, Marcos H. M; London Junior, João Bosco Augusto ; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Conference on Harmonics and Quality of Power - ICHQP. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, MUDANÇA CLIMÁTICA, DISTRIBUIÇÃO DE ENERGIA ELÉTRICA
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ABNT
FANUCCHI, Rodrigo Zempulski et al. Failure rate prediction under adverse weather conditions in an electric distribution system using negative binomial regression. 2016, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2016. Disponível em: http://dx.doi.org/10.1109/ICHQP.2016.7783464. Acesso em: 05 dez. 2025.
APA
Fanucchi, R. Z., Bessani, M., Camillo, M. H. M., London Junior, J. B. A., & Maciel, C. D. (2016). Failure rate prediction under adverse weather conditions in an electric distribution system using negative binomial regression. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/ICHQP.2016.7783464
NLM
Fanucchi RZ, Bessani M, Camillo MHM, London Junior JBA, Maciel CD. Failure rate prediction under adverse weather conditions in an electric distribution system using negative binomial regression [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783464
Vancouver
Fanucchi RZ, Bessani M, Camillo MHM, London Junior JBA, Maciel CD. Failure rate prediction under adverse weather conditions in an electric distribution system using negative binomial regression [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783464
Trabalho de evento
Modeling of an insect proprioceptor system based on different neuron response times (2016)
Lima, Daniel Rodrigues de; Bessani, Michel; Newland, Philip L; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Joint Conference on Biomedical Engineering Systems and Technologies. Unidade: EESC
Subjects: ENTROPIA, BIOMEDICINA
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ABNT
LIMA, Daniel Rodrigues de et al. Modeling of an insect proprioceptor system based on different neuron response times. 2016, Anais.. Rome, Italy: SCITEPRESS, 2016. . Acesso em: 05 dez. 2025.
APA
Lima, D. R. de, Bessani, M., Newland, P. L., & Maciel, C. D. (2016). Modeling of an insect proprioceptor system based on different neuron response times. In Proceedings. Rome, Italy: SCITEPRESS.
NLM
Lima DR de, Bessani M, Newland PL, Maciel CD. Modeling of an insect proprioceptor system based on different neuron response times. Proceedings. 2016 ;[citado 2025 dez. 05 ]
Vancouver
Lima DR de, Bessani M, Newland PL, Maciel CD. Modeling of an insect proprioceptor system based on different neuron response times. Proceedings. 2016 ;[citado 2025 dez. 05 ]
Trabalho de evento-resumo
Synthetic data generation based on mutual information and surrogate data for bayesian network models (2016)
Lima, Daniel Rodrigues de; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Joint Conference on Biomedical Engineering Systems and Technologies. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, ANÁLISE DE SÉRIES TEMPORAIS, BIOMEDICINA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Daniel Rodrigues de e MACIEL, Carlos Dias. Synthetic data generation based on mutual information and surrogate data for bayesian network models. 2016, Anais.. Rome, Italy: SCITEPRESS, 2016. . Acesso em: 05 dez. 2025.
APA
Lima, D. R. de, & Maciel, C. D. (2016). Synthetic data generation based on mutual information and surrogate data for bayesian network models. In Proceedings. Rome, Italy: SCITEPRESS.
NLM
Lima DR de, Maciel CD. Synthetic data generation based on mutual information and surrogate data for bayesian network models. Proceedings. 2016 ;[citado 2025 dez. 05 ]
Vancouver
Lima DR de, Maciel CD. Synthetic data generation based on mutual information and surrogate data for bayesian network models. Proceedings. 2016 ;[citado 2025 dez. 05 ]
Trabalho de evento
Determination of switching sequence of service restoration in distribution systems: application and analysis on a real and large-scale radial system (2016)
Camillo, Marcos H. M.; Delbem, Alexandre Cláudio Botazzo ; Fanucchi, Rodrigo Z; Romero, Marcel E. V.; London Junior, João Bosco Augusto ; Massignan, Julio A. D; Maciel, Carlos Dias ; Marques, Leandro T; Lima, Telma W. de; Soares, Anderson S
Source: Proceedings. Conference titles: IEEE/PES Transmission and Distribution Conference and Exposition - T&D-LA. Unidades: ICMC, EESC
Subjects: SISTEMAS EMBUTIDOS, COMPUTAÇÃO EVOLUTIVA, ROBÓTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAMILLO, Marcos H. M. et al. Determination of switching sequence of service restoration in distribution systems: application and analysis on a real and large-scale radial system. 2016, Anais.. Los Alamitos, CA: IEEE, 2016. Disponível em: https://doi.org/10.1109/TDC.2016.7520034. Acesso em: 05 dez. 2025.
APA
Camillo, M. H. M., Delbem, A. C. B., Fanucchi, R. Z., Romero, M. E. V., London Junior, J. B. A., Massignan, J. A. D., et al. (2016). Determination of switching sequence of service restoration in distribution systems: application and analysis on a real and large-scale radial system. In Proceedings. Los Alamitos, CA: IEEE. doi:10.1109/TDC.2016.7520034
NLM
Camillo MHM, Delbem ACB, Fanucchi RZ, Romero MEV, London Junior JBA, Massignan JAD, Maciel CD, Marques LT, Lima TW de, Soares AS. Determination of switching sequence of service restoration in distribution systems: application and analysis on a real and large-scale radial system [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/TDC.2016.7520034
Vancouver
Camillo MHM, Delbem ACB, Fanucchi RZ, Romero MEV, London Junior JBA, Massignan JAD, Maciel CD, Marques LT, Lima TW de, Soares AS. Determination of switching sequence of service restoration in distribution systems: application and analysis on a real and large-scale radial system [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: https://doi.org/10.1109/TDC.2016.7520034
Trabalho de evento
A statistical analysis and modeling of repair data from a brazilian power distribution system (2016)
Bessani, Michel; Fanucchi, Rodrigo Zempulski; Achcar, Jorge Alberto ; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Conference on Harmonics and Quality of Power - ICHQP. Unidades: EESC, FMRP
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, MODELAGEM DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BESSANI, Michel et al. A statistical analysis and modeling of repair data from a brazilian power distribution system. 2016, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2016. Disponível em: http://dx.doi.org/10.1109/ICHQP.2016.7783446. Acesso em: 05 dez. 2025.
APA
Bessani, M., Fanucchi, R. Z., Achcar, J. A., & Maciel, C. D. (2016). A statistical analysis and modeling of repair data from a brazilian power distribution system. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/ICHQP.2016.7783446
NLM
Bessani M, Fanucchi RZ, Achcar JA, Maciel CD. A statistical analysis and modeling of repair data from a brazilian power distribution system [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783446
Vancouver
Bessani M, Fanucchi RZ, Achcar JA, Maciel CD. A statistical analysis and modeling of repair data from a brazilian power distribution system [Internet]. Proceedings. 2016 ;[citado 2025 dez. 05 ] Available from: http://dx.doi.org/10.1109/ICHQP.2016.7783446
Trabalho de evento
Parallel architecture for smartphones and tablets using GNU/Linux and MPI for data processing (2013)
Zanoni, Felipe Sanches; Maciel, Carlos Dias ; Moreira, Edmilson Marmo; Moreira, Eduardo Marmo
Source: Proceedings. Conference titles: IADIS International Conference Applied Computing. Unidade: EESC
Subjects: LINUX, CLUSTERS, ANDROIDES, PROGRAMAÇÃO PARALELA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZANONI, Felipe Sanches et al. Parallel architecture for smartphones and tablets using GNU/Linux and MPI for data processing. 2013, Anais.. Lisboa: IADIS Press, 2013. . Acesso em: 05 dez. 2025.
APA
Zanoni, F. S., Maciel, C. D., Moreira, E. M., & Moreira, E. M. (2013). Parallel architecture for smartphones and tablets using GNU/Linux and MPI for data processing. In Proceedings. Lisboa: IADIS Press.
NLM
Zanoni FS, Maciel CD, Moreira EM, Moreira EM. Parallel architecture for smartphones and tablets using GNU/Linux and MPI for data processing. Proceedings. 2013 ;[citado 2025 dez. 05 ]
Vancouver
Zanoni FS, Maciel CD, Moreira EM, Moreira EM. Parallel architecture for smartphones and tablets using GNU/Linux and MPI for data processing. Proceedings. 2013 ;[citado 2025 dez. 05 ]
Trabalho de evento
Optimized algorithm for learning bayesian network super-structures (2012)
Villanueva, Edwin; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Conference on Pattern Recognition Applications and Methods - ICPRAM. Unidade: EESC
Subjects: ALGORITMOS (OTIMIZAÇÃO), INFERÊNCIA BAYESIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA, Edwin e MACIEL, Carlos Dias. Optimized algorithm for learning bayesian network super-structures. 2012, Anais.. Lisboa: SciTePress, 2012. . Acesso em: 05 dez. 2025.
APA
Villanueva, E., & Maciel, C. D. (2012). Optimized algorithm for learning bayesian network super-structures. In Proceedings. Lisboa: SciTePress.
NLM
Villanueva E, Maciel CD. Optimized algorithm for learning bayesian network super-structures. Proceedings. 2012 ;[citado 2025 dez. 05 ]
Vancouver
Villanueva E, Maciel CD. Optimized algorithm for learning bayesian network super-structures. Proceedings. 2012 ;[citado 2025 dez. 05 ]

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