Mestrado em Engenharia Elétrica e da Computação (EMC)
URI Permanente para esta coleção
Navegar
Navegando Mestrado em Engenharia Elétrica e da Computação (EMC) por Por Unidade Acadêmica "Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RMG)"
Agora exibindo 1 - 3 de 3
Resultados por página
Opções de Ordenação
Item Sistema de comunicação alternativa para pessoas com distúrbios neuromotores severos usando redes neurais artificiais(Universidade Federal de Goiás, 2024-12-15) Floriano, Carolina de Souza; Silva, Adson Rocha; Brito, Leonardo da Cunha; http://lattes.cnpq.br/6660680440182900; Brito, Leonardo da Cunha; Rocha, Adson Silva; Gomide, Renato de SousaCommunication difficulties are frequent for many people with severe motor disabilities, making it difficult for them to interact with their families, caregivers and society in general. Augmentative and Alternative Communication (AAC) then aims to compensate for the communication deficit of these people, providing the individual with a better quality of life. However, these individuals with severe neuromotor disorders who have severe movement restrictions find great challenges in the use of several current assistive technologies. In this context, the objective of this research is to present an Alternative Communication System based on Artificial Neural Networks with a user-centered approach and their needs, for use by this public. The input and signal processing are carried out by reading facial landmark points, using the MediaPipe FaceMesh library. The development of the gesture/facial expression classifier is performed through the implementation and comparison of two different models: a Convolutional Neural Network (CNN) model and a Recurrent Neural Network model using Long Short-Term Memory (LSTM) units and dense layers. Dynamic challenges were implemented to conduct a more in-depth analysis of the models’ performance in various contexts, varying parameters such as the quantity of samples and the inclusion of similar gestures. Real-time overall results indicate a consistent performance of the proposed system, suggesting that, in both approaches, the Convolutional Neural Network (CNN) stands out significantly compared to the Long Short-Term Memory Recurrent Neural Network (LSTM) in gesture recognition.Item Sistema de comunicação alternativa para pessoas com distúrbios neuromotores severos usando redes neurais artificiais(Universidade Federal de Goiás, 2023-12-15) Floriano, Carolina Souza; Silva, Adson Rocha; http://lattes.cnpq.br/4116708456419800; Brito, Leonardo da Cunha; http://lattes.cnpq.br/6660680440182900; Brito, Leonardo da Cunha; Gomide, Renato de Sousa; Rocha, Adson SilvaCommunication difficulties are frequent for many people with severe motor disabilities, making it difficult for them to interact with their families, caregivers and society in general. Augmentative and Alternative Communication (AAC) then aims to compensate for the communication deficit of these people, providing the individual with a better quality of life. However, these individuals with severe neuromotor disorders who have severe movement restrictions find great challenges in the use of several current assistive technologies. In this context, the objective of this research is to present an Alternative Communication System based on Artificial Neural Networks with a user-centered approach and their needs, for use by this public. The input and signal processing are carried out by reading facial landmark points, using the MediaPipe FaceMesh library. The development of the gesture/facial expression classifier is performed through the implementation and comparison of two different models: a Convolutional Neural Network (CNN) model and a Recurrent Neural Network model using Long Short-Term Memory (LSTM) units and dense layers. Dynamic challenges were implemented to conduct a more in-depth analysis of the models’ performance in various contexts, varying parameters such as the quantity of samples and the inclusion of similar gestures. Real-time overall results indicate a consistent performance of the proposed system, suggesting that, in both approaches, the Convolutional Neural Network (CNN) stands out significantly compared to the Long Short-Term Memory Recurrent Neural Network (LSTM) in gesture recognition.Item Minimização da ondulação de torque em motores a relutância variável por meio de correntes de fase de referência otimizadas por algoritmo genético(Universidade Federal de Goiás, 2023-12-18) Soares, Israel Rodrigues; Paula, Geyverson Teixeira de; http://lattes.cnpq.br/0140145167826333; Paula, Geyverson Teixeira de; Oliveira, Eduardo Sylvestre Lopes de; Almeida, Thales Eugenio Portes deThis work proposes an innovative control strategy for the Switched Reluctance Motor with the aim of minimizing torque ripple. The strategy is based on an algorithm for generating current profiles that prioritize the smooth commutation mode of the asymmetric half-bridge converter. This algorithm employs genetic algorithms to calculate these profiles through simulations in a finite element model developed based on a 6x4 Switched Reluctance Motor from the Laboratório de Ensaios de Pequenos Motores at the Universidade Federal de Goiás. To enhance the adaptability of the proposed control, the addition of a compensation derived from torque error to these profiles has been suggested. Simulations compared the Proposed Control with Direct Instantaneous Torque Control and the Proposed Control without the addition of compensation under various operating conditions. The results highlight significant average reductions in metrics used to evaluate torque ripple. In the Torque Ripple metric, there was an average reduction of 16.02% compared to Direct Instantaneous Torque Control and 13.14% compared to the Proposed Control without compensation. As for the Torque Ripple Factor metric, this reduction was 15.34% and 15.96%, respectively. The study concludes by affirming the good performance of the generated current profiles, demonstrating that the inclusion of compensation derived from torque error in these profiles was crucial for the low levels of torque ripple achieved by the proposed control technique.