R
Rafael Coradi Leme
Researcher at Universidade Federal de Itajubá
Publications - 43
Citations - 450
Rafael Coradi Leme is an academic researcher from Universidade Federal de Itajubá. The author has contributed to research in topics: AC power & Multilayer perceptron. The author has an hindex of 12, co-authored 42 publications receiving 393 citations. Previous affiliations of Rafael Coradi Leme include University of Tennessee.
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Probabilistic voltage stability assessment considering renewable sources with the help of the PV and QV curves
Adriano Batista de Almeida,Eliane Valenca De Lorenci,Rafael Coradi Leme,Antonio Carlos Zambroni de Souza,B.I.L. Lopes,Kwok Lun Lo +5 more
TL;DR: In this paper, the stability and reliability of voltage in a power system with distributed generation is analyzed using simulation techniques, and reliability theory is also considered in the proposed voltage collapse analysis methodology.
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Long-term load forecasting via a hierarchical neural model with time integrators
Otavio A. S. Carpinteiro,Rafael Coradi Leme,Antonio Carlos Zambroni de Souza,Carlos A. M. Pinheiro,Edmilson M. Moreira +4 more
TL;DR: A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed, made up of two self-organizing map nets and a single-layer perceptron that has application into domains which require time series analysis.
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A normal boundary intersection with multivariate mean square error approach for dry end milling process optimization of the AISI 1045 steel
Danielle Martins Duarte Costa,Tarcisio Gonçalves Brito,Anderson Paulo de Paiva,Rafael Coradi Leme,Pedro Paulo Balestrassi +4 more
TL;DR: In this article, a new methodology to optimize a multivariate dry end milling process of the AISI 1045 steel was proposed, which combines the normal boundary intersection (NBI) with multivariate mean square error (MMSE) functions.
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Forecasting financial series using clustering methods and support vector regression
TL;DR: An analysis of the construction and use of clusters associated with a series volatility study shows that data obtained from only one type of volatility are enough to provide sufficient knowledge to the model so that it is able to forecast future values with good accuracy.
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A multivariate robust parameter optimization approach based on Principal Component Analysis with combined arrays
Anderson Paulo de Paiva,José Henrique de Freitas Gomes,Rogério Santana Peruchi,Rafael Coradi Leme,Pedro Paulo Balestrassi +4 more
TL;DR: This paper presents a multiobjective hybrid approach combining response surface methodology (RSM) with Principal Component Analysis (PCA) to study a multi-response dataset with an embedded noise factor, using a DOE combined array.