PPG.EGPSA/ITEGAM
URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1
A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.
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4 resultados
Resultados da Pesquisa
Item Uma Proposta Inovadora Utilizando Blockchain para a Gestão Financeira em Obras Públicas, Tendo como Base o Sistema Brasileiro(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) PARENTE, Ricardo Silva; NASCIMENTO, Manoel Henrique ReisThis chapter presents an innovative proposal for financial management in public works in Brazil using blockchain technology. The objective is to increase transparency and efficiency in the use of public resources, reducing the possibility of corruption and financial deviations. The proposal includes the creation of a blockchain-based system that securely and immutably records and monitors all financial transactions, allowing real-time audits. The study discusses the practical implications of implementing this technology in the public sector and proposes a model adapted to the Brazilian reality.Item Algoritmo de Previsão de Velocidade do Vento - HW+NARX(Instituto Nacional da Propriedade Industrial (INPI), 2021-06-27) PARENTE, Ricardo Silva; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188This document describes a computer program registered under number BR512023001329-3, which presents an algorithm for forecasting wind speed using a combination of exponential smoothing methods (Holt-Winters, HW) and autoregressive neural networks with exogenous inputs (NARX) . The goal of the program is to improve the accuracy of wind speed predictions, which is crucial for diverse applications such as wind energy and climate monitoring. The combination of HW+NARX methods provides a robust approach, capable of dealing with the variability and complexity of meteorological data. Program registration is valid for 50 years from January 1, 2022.Item Digital technologies review for manufacturing processes(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-03-31) PARENTE, Ricardo Silva; UHLMANN, Iracyanne RettoIt is apparent the industrial processes transformations caused by industry 4.0 are in advance in some countries like China, Japan, Germany and United States. But, in return, the developing countries, as the emergent Brazil, seem like to have a long way to achieve digital era. Considering manufacturing processes as the starting point the rise of industry 4.0, this research aims to show a review about the most important technologies used in smart manufacturing, including the main challenges to implement it at Brazil. The papers were collected from Web of Science (WoS), comprising 114 articles and 2 books to underpin this study. This exploratory research resulted in the presentation of some challenges faced by Brazilian industry to join the new industrial era, such as poor technological infrastructure, besides lack of investment in technologies and training of qualified people. Even though the primary motivation of this research was to present a panorama of smart manufacturing for Brazil, this study results contributes to the most of emergent countries, bringing together general concepts and addressing practical applications developed by several researchers from the international academic community.Item Modelo híbrido utilizando holt-winters e rede neural não linear autoregressiva com entradas exógenas (narx) para previsão da velocidade do vento(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-07-27) PARENTE, Ricardo Silva; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188The wind energy matrix is gradually increasing in recent years and its importance for the renewable energy industry is increasingly linked to the benefits in relation to the environment, with this growing energy matrix the research around the wind power generation is also increasing, and one of the strands is the wind speed prediction, because with this it is possible to predict the wind power generation and decrease the error rate in decision making in the industry of electricity generation through wind power matrix. Considering the problem of decision making and the unpredictability of wind speed, this paper aims to develop a hybrid model for wind speed prediction and consequently wind power generation, based on HoltWinters Exponential Smoothing and Nonlinear Auto-Regressive Neural Network with Exogenous Inputs (NARX). In the materials and methods, the database of the SONDA project (System of National Organization of Environmental Data) organized by INPE (National Institute for Space Research) was used, in which it was chosen to use the anemometric data from the station of Brasilia - BRB and Petrolina - PTR, where data from the years February 2005 to March 2019 of the BRB station were used for training, validation and testing, and from January 2006 to December 2015 of the PTR station for simulations of the HW, NARX and proposed model. The results obtained with the proposed hybrid HW-NARX model were compared with the HW and NARX seasonal time series forecasting algorithms, in which the proposed model was able to achieve better performance and predictability results than HW and NARX for the ultra-short-term, medium-term, and long-term time horizons.