http://repositorio.ufc.br/handle/riufc/464 2026-06-27T12:38:33Z 2026-06-27T12:38:33Z Queiroz Filho, Edvaldo de Sousa http://repositorio.ufc.br/handle/riufc/86882 2026-06-23T18:52:02Z 2026-01-01T00:00:00Z

Título: Análise da viabilidade técnica e financeira da modernização do parque de iluminação pública do município de Fortaleza: eficiência energética com tecnologia LED e IoT. Autor(es): Queiroz Filho, Edvaldo de Sousa Abstract: Public lighting plays a crucial role in security, quality of life, and economic development in cities. In Brazil, it accounts for about 3% of electricity consumption, making it one of the largest fixed costs for local governments. With population growth and urbanization, the public lighting system has expanded significantly but still faces challenges such as high energy consumption, expensive maintenance, and inequality in distribution, particularly in peripheral areas. The replacement of conventional lamps with LEDs has proven to be the main solution to this scenario, as it offers advantages such as greater durability, energy savings of up to 50%, better color reproduction, and reduced maintenance needs. Furthermore, the Internet of Things (IoT) allows the implementation of intelligent systems for real-time monitoring, adjustment of light intensity, and fault diagnosis, providing improvements in public safety and environmental sustainability. This study aims to analyze the technical and economic feasibility of the progressive replacement of conventional lamps with LEDs in the municipality of Fortaleza, as well as the implementation of an intelligent energy management system focusing on efficient energy monitoring and management of the public lighting system in the city. The research also evaluates the implementation of an IoT-based energy management system, considering real data obtained from the ENEL-CE utility company and reports from the Fortaleza Department of Conservation and Public Services. Financial indicators such as Payback, IRR (Internal Rate of Return), and NPV (Net Present Value) are used for the analysis.The main hypothesis is that the modernization of public lighting with LEDs and IoT not only brings economic benefits but also contributes to social inclusion, security, digital governance, and urban sustainability. The study seeks to provide subsidies for more effective and sustainable public policies, aligned with the guidelines of renewable energy and smart cities. Tipo: Dissertação

2026-01-01T00:00:00Z Guerra, Lucca Lemos Costa http://repositorio.ufc.br/handle/riufc/86725 2026-06-11T19:18:05Z 2026-01-01T00:00:00Z

Título: Platform for optimization and sizing of hybrid photovoltaic-battery systems for industrial decarbonization Autor(es): Guerra, Lucca Lemos Costa Abstract: Battery Energy Storage Systems (BESS) have been experiencing accelerated growth worldwide, driven by electrification, the expansion of intermittent renewable energy sources, and the increasing need for operational flexibility in the power sector. In this context, this work develops and implements a computational platform for the sizing and optimization of hybrid photovoltaicbattery systems (PV-BESS), aimed at industrial decarbonization. The methodology is based on a Mixed-Integer Linear Programming (MILP) optimization model, implemented in Python using the Pyomo library. The tool was validated using real data from a medium-sized industry located in the state of Ceará, Brazil, classified as a Group A consumer (high voltage), with an annual electricity consumption of approximately 1.4 GWh. The model simultaneously considers energy, operational, and economic aspects, including capital investment costs (CAPEX), electricity consumption tariffs, grid energy injection/compensation, and peakhour operation. As the main methodological differentiator, the sizing can be conducted according to two distinct operational profile approaches, associated with the user’s desired level of risk aversion. The f irst approach is based on a critical day, using the 95th percentile (P95) to model demand and restricting photovoltaic generation to the 10th percentile (P10) of historical solar irradiance, with an additional safety margin, resulting in a solution oriented toward maximum operational robustness. The second approach uses average hourly load and irradiance profiles, optimizing the project for typical day-to-day operating conditions while reducing implementation costs. For the analyzed case study, the sizing based on the average profile resulted in an 800 kW photovoltaic system combined with a 3,916 kWh BESS, providing 92.7% self-sufficiency and an estimated reduction of 608.37 tons of CO2 avoided annually. Additionally, the comparison between the “critical day” and “average day” strategies highlighted the tradeoff between operational robustness, energy resilience, and economic viability, reinforcing the practical applicability of the tool for energy transition and industrial decarbonization projects. Tipo: Dissertação

2026-01-01T00:00:00Z Barbosa, Klysmann Gladson Ferreira http://repositorio.ufc.br/handle/riufc/86617 2026-06-05T00:48:01Z 2026-01-01T00:00:00Z

Título: Controle de posição da junta de manipulador cilíndrico acionado por motor de indução trifásico com transmissão flexível e otimização conjunta de sintonia Autor(es): Barbosa, Klysmann Gladson Ferreira Abstract: This work investigates the position control of the base rotational joint of a cylindrical manipulator driven by a three-phase induction motor with a flexible mechanical transmission, with emphasis on the systematic tuning of controllers. The electromechanical system is modeled in an integrated manner, including the three-phase induction motor under Indirect Field-Oriented Control (IFOC), the manipulator dynamics, and the elastic motor-load coupling represented by a two-mass model derived from the Euler-Lagrange formulation. The study focuses on the influence of flexible mechanical coupling on control performance and on the systematic determination of controller parameters using metaheuristic and probabilistic optimization methods. Different cascaded control architectures combining P and PI controllers with Adaptive Sliding Mode Control (ASMC) are evaluated, both in sensored operation and in sensorless configuration using a Sliding Mode Observer (SMO). Additionally, the influence of measurements obtained from a non-ideal incremental encoder on the quality of state feedback is considered. The results show that transmission elasticity introduces resonant modes that significantly degrade performance when conventional controller parameters are used. Systematic optimization-based tuning improves the trade-off between response speed, accuracy, and robustness, and allows the identification of operating regions in which PID controllers achieve performance comparable to robust control strategies, as well as conditions in which ASMC provides significant advantages under parametric uncertainties and external disturbances. Tipo: Dissertação

2026-01-01T00:00:00Z Landim, Pedro Lino Azevedo http://repositorio.ufc.br/handle/riufc/86616 2026-06-04T23:52:20Z 2025-01-01T00:00:00Z

Título: Plataforma web de anotações interativas para segmentação de neuroimagens por ressonância magnética Autor(es): Landim, Pedro Lino Azevedo Abstract: The growing volume and complexity of medical imaging, especially magnetic resonance imaging, have increasingly overwhelmed healthcare professionals and highlighted the need for computer-aided diagnostic tools. In this context, this work presents the development of an interactive web platform for the segmentation of magnetic resonance neuroimages, integrating artificial intelligence techniques and accessible visualization. The tool was designed with a modular architecture composed of a Flutter Web interface, a Flask-based API, and deep learning models implemented in PyTorch. The system performs automatic segmentation using the U-Net architecture and enables dataset enhancement through mask generation using the Flood Fill algorithm, validated by a CNN. The interface provides features for image upload, region of interest marking, result visualization, and retraining of the model with customized data. Load and stress tests were conducted to assess system performance, along with both quantitative and qualitative analyses of the segmentations. The U-Net segmentation model achieved a mean Dice Score of 90.22% , while the CNN for mask validation obtained 97.49% accuracy. These results, combined with the API’s high success rate under stress, demonstrate the feasibility of applying the platform in clinical and research environments, highlighting its flexibility, adaptability, and integration with medical workflows. Tipo: Dissertação

2025-01-01T00:00:00Z