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URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/5

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    Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; FONSECA JUNIOR, Milton; LEITE, Jandecy Cabral;
    With the expansion of means of river transportation, especially in the caseof small and medium-sized vessels that make routes of greater distances, the cost of fuel, if not taken as an analysis criterion for a larger profit margin, is considered to be a primary factor , considering that the value of fuel specifically diesel to power internal combustion machines is high. Therefore, the use of tools that assist in decision making becomes necessary, as is the case of the present research, which aims to contribute with a computational model of prediction and optimization of the best speed to decrease the fuel cost considering the characteristics of the SCANIA 315 machine. propulsion model, of a vessel from the river port of Manaus that carries out river transportation to several municipalities in Amazonas. According to the results of the simulations, the best training algorithm of the Artificial Neural Network (ANN) was the BFGS Quasi-Newton considering the characteristics of the engine for optimization with Genetic Algorithm (AG).
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    Fuzzy Logic-Based Compliance Assessment For Display Testing In Industrial Measurement Systems
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-10-01) SOUZA, Márcio André de Oliveira; NASCIMENTO, Manoel Henrique Reis; LEITE, Jandecy Cabral; BRITO JUNIOR, Jorge de Almeida; QUEIROZ JUNIOR, Fernando Cardoso de
    This study presents the development and implementation of a fuzzy logic-based system aimed at automating the compliance evaluation process for display testing in industrial measurement systems. The fuzzy inference system was programmed in Python, using linguistic variables and fuzzy rules to assess performance parameters like accuracy, response time, and error margins. Results showed that the system classified 93% of devices in accordance with established compliance standards, improving the reliability and efficiency of display testing in industrial environments.