http://repositorio.ufc.br/handle/riufc/23981 2026-04-09T23:06:33Z http://repositorio.ufc.br/handle/riufc/85670 Título: Projeto e análise de uma rede GPON FTTH no distrito de Araquém Autor(es): Fernandes, Maria Darly Teles Abstract: This work addresses the increasing dependence of modern society on communication means, focusing on optical communications, specifically on optical fiber and Gigabit Passive Optical Network (GPON). Initially, the basic elements of telecommunications networks are discussed, followed by the main types of wired and wireless access networks, highlighting their advantages and disadvantages. The thesis also explores the fundamentals of optical fiber, including types and associated losses. Subsequently, it focuses on optical networks, emphasizing passive networks and discussing the main components of Passive Optical Network (PON) and their characteristics. Finally, the study presents the projection of a GPON network using Fiber To The Home (FTTH) architecture for a district in the municipality of Coreaú, in which the quantities of Optical Termination Boxes (CTOs), Optical Splices Boxes (CEOs) and other components. Tipo: TCC 2024-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85667 Título: Aprendizado de máquina para a predição da saúde mental e qualidade de vida de estudantes universitários Autor(es): Fonseca, Lucas dos Santos Abstract: In recent years, there has been a significant increase in the number of studies focused on Mental Health (MH) worldwide. Students, from elementary school to university, have gained notoriety due to the rise in anxiety and depression rates. In the task of identifying the causes of these elevated numbers, sociodemographic indicators and data related to mental health and quality of life are of utmost importance. Therefore, this study aims to evaluate the effectiveness of using machine learning techniques to predict the levels of MH and Quality of Life (QoL) of students at the University of Vale do Acaraú (UVA) and to identify the main causes of anxiety and depression experienced by them. The dataset used consists of responses from 880 students to three questionnaires: one related to sociodemographic, academic, and clinical data, the Mental Health Inventory (MHI), and the WHOQOL-Bref. Data processing was based on the concepts and processes of Data Mining (DM) and Knowledge Discovery in Databases (KDD), and the trained algorithms were: Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Adaptive Boosting (AdaBoost). After hyperparameter tuning, done via grid search, and feature selection performed with the Sequential Forward Floating Selection (SFFS) and Sequential Backward Floating Selection (SBFS) algorithms, the results achieved an accuracy of 78.44% for the prediction of MH and 77.40% for the prediction of QoL, both using RF as the model. Additionally, the importance of the attributes was evaluated by calculating information gain and stepwise regression, with the most important attributes for MH being the scores of the WHOQOL-Bref domains, and for QoL, the scores of the MHI domains. Tipo: TCC 2024-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85666 Título: Estudo comparativo dos efeitos da integração de geração distribuída fotovoltaica no alimentador teste de 13 nós do IEEE Autor(es): Santos Júnior, Juarez José Teixeira dos Abstract: Given the expansion of Distributed Photovoltaic Generation (DGPV) systems in distribution networks, driven mainly by incentive laws and price reductions for photovoltaic components, the following study was carried out with the aim of analyzing the effects associated with the implementation of these systems, focusing on voltage rise, reverse flow and technical losses. The study was carried out through simulations using the software OpenDSS, applying the IEEE node test feeder. The applied methodology proposed to investigate, through comparative analysis, the impacts mentioned in five different scenarios, where the base case consisted of the feeder without DGPV, the following three scenarios, connecting the DGPV in different nodes and the last one with the three DGPVs active simultaneously in the circuit. The results, discussed with the aid of graphs and tables, indicated the existence of reverse flow in all branches equipped with DGPV during the period of greatest solar irradiation (penetration). During this period, there was also an increase in voltage at the nodes analyzed and a reduction in losses in all cases with connection of the generating units, except for two times in scenario 5. Tipo: TCC 2024-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85472 Título: Sistema de monitoramento de consumo de energia elétrica residencial Autor(es): Parente, Joan Kennedy Caetano Abstract: This thesis focuses on the development of a prototype for a residential smart meter. The project for this device was conceived in response to the constant increase in electricity tariffs, which has encouraged the search for more efficient ways to control energy consumption. The smart meter allows users to monitor energy consumption in real-time through an interactive web platform, providing more conscious and efficient management of energy resources. The system was programmed to measure electrical quantities such as current, voltage, and instantaneous consumption, transmitting this data to a cloud database and presenting it in an accessible manner to the user via the web platform. In addition to detailing the device development process, the thesis discusses the calibration and performance tests conducted, comparing the results obtained with other meters available on the market. The prototype demonstrated accuracy in measurements and a considerably lower cost compared to commercial alternatives, making it viable for implementation in homes as an energy efficiency tool. The relevance of the project aligns with the growing demand for sustainable solutions and technologies that promote home automation and the rational use of energy resources. Tipo: TCC 2024-01-01T00:00:00Z