https://repositorio.unifei.edu.br/jspui/handle/123456789/65 Fri, 30 Oct 2026 19:33:19 GMT 2026-10-30T19:33:19Z https://repositorio.unifei.edu.br/jspui/handle/123456789/3173 Aquisição e validação de sinal de ruído eletroquímico Corrosion is a global problem, which implies costs in industrialized countries of up to 4.5% of GDP, with either economic, but also social and environmental impacts. In the case of Brazil, the waste of water supply networks due to leaks loss is quite significant and much of it is caused by network degradation, indicating that corrosion control should be promoted whenever possible. This study proposes a corrosion monitoring system, in system subject to the use of inhibitor, with the approach of passive technique for monitoring corrosion by electrochemical noise (EN), in which the classification of events in a corrosion sensor by EN is part of methodological study for structural integrity (or “health”) monitoring system (SHM). Due to very dynamic and stochastic nature of the signal, this study and analysis of EN measurements (ENM) considers numerical and graphic characteristics of two corrosion systems both in saline aqueous solution: carbon steel and stainless steel. These experiments are repeated for accumulating data, which allow the generation of several graphs in time and frequency domains, from which at least one characteristic is extracted, which has a good correlation with data from corrosion processes. Then, based on a supervised machine learning system, the training data allows the model to be calibrated. From the test data, the correctness rate of the model above 50% is verified. Dissertação Thu, 17 Feb 2022 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/3173 2022-02-17T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4488 Autodrill: framework autoML para previsão da taxa de perfuração em poços de petróleo Predicting the Rate of Penetration (ROP) plays a central role in optimizing operations in the oil and gas industry, directly influencing operational efficiency and drilling-related costs. However, conventional machine learning models have limitations in this context due to the strong non-linearity of the variables involved and, above all, the high lithological heterogeneity and low reliability of downhole sensors, resulting in noisy and gapped time series. This work proposes AutoDrill, an Automated Machine Learning (AutoML) framework specifically designed for the drilling domain. In contrast to generalist approaches, AutoDrill incorporates a preprocessing pipeline specialized in drilling signals (including adaptive smoothing, transient correction, and data interpolation) coupled with an automated hyperparameter search and tuning mechanism. The experimental evaluation was conducted on various datasets and geological scenarios, using records from three offshore repositories (Equinor Volve, USROP, and NEATS NOPIMS). The performance of AutoDrill was compared to the AutoGluon and H2O AutoML frameworks, considered representative of the state of the art in AutoML. The results indicate that AutoDrill exhibits consistent and competitive performance, achieving an average coefficient of determination (R2) of 0.91 across all analyzed scenarios, particularly in environments characterized by high heterogeneity and noise, such as in the case of NEATS NOPIMS. In this specific scenario, AutoDrill significantly outperformed the reference frameworks, reducing the mean absolute error (MAE) by up to 80% and raising the R2 from levels below 0.20 to 0.94. In conclusion, the systematic incorporation of domain knowledge into the machine learning automation cycle is crucial for the robustness of real-time prediction. From this perspective, AutoDrill presents itself as a practical, scalable, and domain-oriented approach for ROP prediction, contributing to the advancement of machine learning techniques in petroleum engineering. Dissertação Fri, 04 Sep 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4488 2026-09-04T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4487 Ferramenta web gamificada para o estudo introdutório de análise de dados The growing volume of data generated globally across diverse activities is accompanied by an equally increasing demand to interpret it and extract valuable information. In this scenario, data analysis, an essential component of the Data Science domain, enables this process by transforming raw data into useful knowledge for decision-making. However, as students develop analytical skills, they face challenges such as subjective interpretation of visualizations, high-dimensional data, the need to understand sometimes abstract statistical concepts, and the required proficiency with specific computational tools. These factors point to the need for methodologies and tools that can support teachers and motivate students in this field. In this context, the research proposes developing a gamified web tool to support introductory data analysis. The tool aims to facilitate the study of concepts and data analysis techniques through a solution that incorporates game elements and was evaluated by volunteer students in computer science courses at the Federal University of Itajubá. The evaluation captured students’ perceptions of the developed tool from multiple perspectives. The results of this evaluation indicated high rates of usability, acceptance, and satisfaction. Dissertação Tue, 11 Aug 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4487 2026-08-11T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4486 Framework multicritério para seleção de modelos de linguagem em contextos educacionais: uma análise de desempenho, custo e eficiência energética The adoption of artificial intelligence models in Virtual Learning Environments (VLEs) requires a balance among response quality, latency, computational cost, and energy consumption, especially in continuous and large-scale usage scenarios. This study proposes a multicriteria decision framework for selecting between Small Language Models (SLMs) and Large Language Models (LLMs) in educational contexts, modeling the choice as a Profile Matching problem based on the Euclidean distance between the scenario’s target profile and the normalized empirical performance of each model. Following the logic of Design Science Research (DSR), 21 models (13 base models and 8 fine-tuned models) were evaluated, ranging from 1 to 70 billion parameters, across 240 multiple-choice questions (120 original and 120 from ENEM), distributed among eight subjects and three difficulty levels. The results indicate that LLMs achieve higher accuracy, with a clear advantage on difficult questions, whereas untuned SLMs are substantially faster (approximately 8.56 times lower latency) and more energy-efficient (approximately 4.83 times less energy). Fine-tuning, under the conditions evaluated, increased operational costs without a consistent gain in accuracy. Across the ten simulated scenarios, the framework selected eight distinct models, demonstrating that the best choice depends on the context and that profile-based selection outperforms the maximization of isolated metrics. Dissertação Tue, 28 Jul 2026 00:00:00 GMT https://repositorio.unifei.edu.br/jspui/handle/123456789/4486 2026-07-28T00:00:00Z