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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11 resultados
Resultados da Pesquisa
Item Aplicação de Inferência Fuzzy Para Tomada de Decisão em Processos de SMT(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) LEITE, Herbety Lima; NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa de; BRITO JUNIOR, Jorge de AlmeidaSoftware developed in Python applying fuzzy inference for decision-making processes in SMT (Surface-Mount Technology), targeting industrial process optimization. The registration is recognized under Brazilian intellectual property law and categorized within engineering and automation domains.Item Sistema Inteligente para monitoramento de subestações elétricas integrado à plataforma SGE: uma aplicação da Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SANTOS JUNIOR, Hélio Andrade dos; NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa dePython-based software developed for intelligent monitoring of electrical substations. Integrated with the SGE platform, it provides functionalities tailored for Industry 4.0 applications, enabling automation, real-time data acquisition, and remote diagnostics of critical infrastructure. Classified under electrical engineering and industrial automation.Item Sistema inteligente para detecção de falhas utilizando algoritmo de máquina de vetores de suporte – SVM(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SILVA, Carlos Américo de Souza; PALADINI, Edson Pacheco; PENEDO, Jorge Eduardo Santos; LEITE, Jandecy CabralPython-based software using Support Vector Machine (SVM) algorithms for fault detection systems. Aimed at intelligent automation in industrial settings, the software enhances predictive decision-making accuracy in the context of Industry 4.0.Item Sistema inteligente para classificação de falhas na manufatura de placas utilizando algoritmo de Machine Learning KNN(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) PENEDO, Jorge Eduardo Santos; PALADINI, Edson Pacheco; SILVA, Carlos Américo de Souza; LEITE, Jandecy CabralPython-based software using the K-Nearest Neighbors (KNN) machine learning algorithm to classify failures in PCB manufacturing lines. Designed for Industry 4.0 environments, it aims to improve predictive failure detection accuracy in automated industrial contexts.Item Sistema inteligente para classificação de defeitos na manufatura de placas com Rede Neural Convolucional(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SILVA, Carlos Américo de Souza; LEITE, Jandecy Cabral; PENEDO, Jorge Eduardo Santos; PALADINI, Edson PachecoPython-based software designed to classify defects in PCB manufacturing using Convolutional Neural Networks (CNN). Applied in Industry 4.0 environments, the system improves fault detection efficiency and reliability in production lines through intelligent pattern recognition.Item Implementação de um sistema de visão com deep learning para otimizar o processo de inspeção de emendas das bobinas na fabricação do cinturão de segurança(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOUZA, Kerlisson Silva de; SANTOS, Eliton Smith dosProduct quality is one of the primary criteria considered by customers when choosing an item. Additionally, it is an essential factor for companies to stand out in a highly competitive market. In the Manaus Industrial Hub (PIM), in a machine used for producing safety belts, defect detection is a crucial stage in the production process. To enhance this task, Artificial Intelligence (AI) was implemented, standing out for its high efficiency in analyzing and processing data in industrial environments. The data was captured in image format by a camera, and using Deep Learning (DL) techniques, an intelligent algorithm capable of detecting faults was developed. Due to its autonomous learning capability and ability to identify and characterize defects, this algorithm represents the future of automated inspection. It has already achieved significant success in applications such as object identification and classification, facial recognition, and fault diagnostics. Given this context, the aim of this study is to propose an ideal solution to minimize failures in the production process of safety belts. The proposal seeks to automate the currently manual step using the concept of computer vision with AI, ensuring greater efficiency and reliability in the production process. The research, development, and application of AI with the algorithm in the case study were conducted in the R&D laboratory of the company located in the Manaus Industrial Hub (PIM). The project utilized product inputs, a camera equipped with a lens for capturing images, and a computer for data storage and algorithm development. The application of AI in this environment uses computer vision systems to process image data. For this, a program was developed in Python with the PySimpleGUI library. The trained model was evaluated based on loss and accuracy metrics on the test set, achieving values of 0 and 100%, respectively. During testing, new belts were used, reaching 100% accuracy in the results. The proposed model showed excellent results. With data processed by AI using Deep Learning (DL) techniques, real-time inspection of the belts was achieved. Additionally, the network achieved perfect accuracy in all tests conducted on the belts, demonstrating the effectiveness of the solution.Item Desenvolvimento de um Sistema de Monitoramento de Carga em Baterias Fotovoltaicas Utilizando Python(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) David Barbosa de; LEITE, Jandecy CabralThe article presents a Load Monitoring System for Photovoltaic Batteries developed in Python, aiming to provide users with precise and real-time information about the charge status of the batteries. This system is particularly relevant due to the increasing adoption of photovoltaic solar energy as a renewable energy source, allowing the optimization of photovoltaic systems' performance and contributing to more efficient energy consumption management.Item Aplicação de Inteligência Artificial para Previsões e Ajustes de Fator de Potência (FP) por Fase(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) ALMEIDA, Anderson Alexandre Silva de; LEITE, Jandecy CabralThis document certifies the registration of the software entitled "Application of Artificial Intelligence for Predictions and Adjustments of Power Factor (PF) by Phase." Developed in Python, the program leverages artificial intelligence techniques to accurately predict and adjust the power factor of electrical systems. The solution aims to optimize the energy performance of three-phase systems, reducing losses and promoting greater operational efficiency.Item Software de coleta inteligente de dados TCP (SCIDT) - Gate Move 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-09-02) AMARAL, Carlos Henrique; LEITE, Jandecy Cabral; DIRANE, Eduardo Nunes; MENDONÇA, Pedro Henrique Barros; RIBEIRO, Paulo Francisco da SilvaDocument for the registration of computer software by the National Institute of Industrial Property (INPI), validating the software "Intelligent data collection software TCP (SCIDT) - Gate Move 4.0", developed in Python, valid for 50 years.Item Controle e navegação de AMRs(Instituto Nacional da Propriedade Industrial (INPI), 2024-09-12) COSTA, Gledyson Cidade da; LEITE, Jandecy Cabral; RODRIGUES, Carlos Manuel TabosaDocument for the registration of computer software by the National Institute of Industrial Property (INPI), validating the software "Control and navigation of AMRs", developed in Python, valid for 50 years.