https://repositorio.unifei.edu.br/jspui/handle/123456789/65 2026-08-05T08:42:23Z 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 2022-02-17T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4465 Classificação morfológica de galáxias com Deep Learning nos Surveys SDSS e S-PLUS Morphological classification of galaxies in large photometric surveys is central to galaxy evolution studies, but it remains challenging when deep learning models are transferred across different data domains. This dissertation addresses the problem by developing and evaluating deep learning models for binary galaxy classification (regular vs. peculiar), with emphasis on quantifying the domain shift between the Sloan Digital Sky Survey (SDSS) and the Southern Photometric Local Universe Survey (S-PLUS). Using transfer learning, a benchmark of 11 pre-trained architectures was conducted, including EfficientNet (B0-B4), ResNet/ResNeXt, and Vision Transformer (ViT-B/16 and ViT-B/32), plus a simple CNN trained from scratch. The models were trained and validated on a curated dataset of 11,662 galaxies with images from SDSS and S-PLUS, labeled from the RC3 catalog. In the SDSS domain, the best model was ViT-B/32, with an F1-score of 0.923 and an AUC of 0.963, followed by EfficientNet-B4 (F1-score 0.910) and ViT-B/16 (F1-score 0.899). The superiority of attention-based models suggests that long-range spatial modeling is advantageous for this task. In contrast, the simple CNN showed the worst performance (F1-score 0.655), reinforcing the importance of transfer learning. Evaluation of the best model on S-PLUS quantifies the performance degradation, with a drop of 14.8 percentage points in F1-score (from 0.923 to 0.775), confirming a significant domain shift effect. The Peculiar class is the most affected, with a 29.8% drop in recall, while average predictive uncertainty increases by 65.4% and calibration worsens by more than 50%. This work contributes a comparative architecture benchmark, a curated dataset, and a quantitative analysis of cross-survey generalization, showing that domain adaptation is an indispensable methodological step for the practical use of these models in different observational contexts. Dissertação 2025-11-14T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4464 Teletrabalho no contexto brasileiro: fatores socioeconômicos, ambientais e de transporte a partir de uma abordagem de aprendizado de máquina Telework emerges as a potential tool for reducing commuting, lowering external costs, and promoting quality of life. In this context, the main objective of this research is to investigate the adoption of telework in the Brazilian context from a machine learning perspective. Initially, a comprehensive conceptual model was developed to examine the relationships between telework and socioeconomic, transportation, and environmental variables, based on a systematic literature review of studies published up to 2025. The temporal analysis of the literature highlights the growing academic interest in the topic, emphasizing the profile of teleworkers, characterized by factors such as age, educational level, income, and occupation type. Additionally, interactions between telework and urban aspects are explored, including the adoption of sustainable transportation modes, such as walking and cycling, as well as its relationship with urban sprawl. The impacts on quality of life are also discussed, particularly regarding schedule flexibility and work–life balance. The thesis analyzes the evolution of telework in Brazil from 2022 to 2025 through the application of machine learning models to representative microdata from the Continuous National Household Sample Survey, covering approximately 210,000 households per period. A standardized processing workflow was implemented, including data preprocessing, handling of missing values, class balancing through random undersampling, variable encoding and normalization, as well as stratified data splitting with k-fold cross-validation. Nine classification algorithms were evaluated, including Multinomial Logistic Regression, Decision Trees, Random Forest, XGBoost, Support Vector Machines, MARS, and Neural Networks, with hyperparameter tuning performed using the ANOVA racing method. Model performance showed consistently high results (ROC AUC > 0.80) across all analyzed periods. The analysis of variable importance indicates that the main determinants of telework remain relatively stable over time, although their contributions vary, with a notable increase in the influence of gender in more recent periods. The results demonstrate that telework in Brazil is driven by a combination of sociodemographic and occupational factors, reinforcing its selective nature. Overall, this thesis contributes to a deeper understanding of the multiple dimensions of telework by integrating theoretical and empirical evidence. Furthermore, the findings provide relevant insights for policymakers, organizational managers, and urban planners, highlighting how telework shapes urban dynamics and can support efforts to address contemporary urban challenges. Tese 2026-05-20T00:00:00Z https://repositorio.unifei.edu.br/jspui/handle/123456789/4463 Proposta de melhoria da jornada empreendedora de uma incubadora de base tecnológica: um estudo de caso universitário Entrepreneurship plays a strategic role in regional socioeconomic development, particularly in contexts marked by the need for economic diversification. In this scenario, university-based technology business incubators are relevant mechanisms for fostering innovation and supporting the creation of new ventures. In this context, the present study aims to propose the improvement of the entrepreneurial journey of a university-based technology incubator, based on the evaluation of its first operational cycle and on the theoretical foundation of startup development models throughout the entrepreneurial process. To achieve this objective, a basic research approach was adopted, with a descriptive nature and a qualitative methodology, conducted through a single case study. Data collection was carried out through bibliographic research, document analysis, non-participant observation, and semi-structured interviews with the incubator team and the incubated ventures. The data were analyzed using content analysis techniques, allowing for an understanding of the perceptions, experiences, and practices associated with the incubation process. The results showed that the initial entrepreneurial journey, composed of three stages, contributed to the maturation of ideas, market understanding, and the initial structuring of the ventures, with mentoring, advisory support, and networking standing out as the main valuegenerating factors. However, relevant operational limitations were identified, such as the use of a generic entrepreneurial journey model that did not address the specificities of technology-based businesses, as well as the absence of systematized tools for monitoring and tracking the incubated ventures. Based on the analysis of the collected data and the literature on startup development models, a new entrepreneurial journey structured into five stages, focused on technology-based businesses, was proposed. Additionally, a prototype of a digital system was developed to monitor the progress of startups. The evaluation of the perception of the revised journey and the prototype screens was carried out by specialists, confirming its importance for the incubation process and for the management of the incubator. Dissertação 2026-05-28T00:00:00Z