FE - DI | Documentos por Auto-Depósito
Permanent URI for this collection
Browse
Recent Submissions
- Carbon Fiber Epoxy Composites for Both Strengthening and Health Monitoring of StructuresPublication . Salvado, Rita; Lopes, Catarina; Szojda, Leszek; Araújo, Pedro; Górski, Marcin; Velez, Fernando J.; Castro-Gomes, João; Krzywon, RafalThis paper presents a study of the electrical and mechanical behavior of several continuous carbon fibers epoxy composites for both strengthening and monitoring of structures. In these composites, the arrangement of fibers was deliberately diversified to test and understand the ability of the composites for self-sensing low strains. Composites with different arrangements of fibers and textile weaves, mainly unidirectional continuous carbon reinforced composites, were tested at the dynamometer. A two-probe method was considered to measure the relative electrical resistance of these composites during loading. The measured relative electrical resistance includes volume and contact electrical resistances. For all tested specimens, it increases with an increase in tensile strain, at low strain values. This is explained by the improved alignment of fibers and resulting reduction of the number of possible contacts between fibers during loading, increasing as a consequence the contact electrical resistance of the composite. Laboratory tests on strengthening of structural elements were also performed, making hand-made composites by the “wet process”, which is commonly used in civil engineering for the strengthening of all types of structures in-situ. Results show that the woven epoxy composite, used for strengthening of concrete elements is also able to sense low deformations, below 1%. Moreover, results clearly show that this textile sensor also improves the mechanical work of the strengthened structural elements, increasing their bearing capacity. Finally, the set of obtained results supports the concept of a textile fabric capable of both structural upgrade and self-monitoring of structures, especially large structures of difficult access and needing constant, sometimes very expensive, health monitoring.
- Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora ClassificationPublication . Corceiro, Ana; Pereira, Nuno José Matos; Alibabaei, Khadijeh; Gaspar, Pedro DinisThe global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.
- Imputação de Valores Omissos em Análise Descritiva de Dados, em RPublication . Salambiaku, Luzizila; Prata, Paula; Ferrão, Maria EugéniaOs valores omissos representam um problema frequente no processo de análise de dados. Neste artigo foram comparados seis métodos distintos de imputação, disponíveis no software R e avaliado o seu desempenho em conjuntos de dados relacionados com a área da educação. Foi estudada uma amostra de 20408 estudantes para testar os seis algoritmos em quatro conjuntos de dados gerados por simulação com diferentes percentagens de valores omissos, considerando 5%, 10%, 15% e 20% nas variáveis de interesse. Foram explorados métodos de imputação simples (Média, Mediana e Moda), métodos baseados em aprendizagem automática (kNN e bPCA) e um método de imputação múltipla (MICE). Foi avaliado o desempenho de cada método calculando os respetivos erros de imputação através as métricas RMSE e MAE. Os resultados obtidos mostram que a imputação pela Moda forneceu quase de forma constante menores valores de erro.
- Anonymized Data Assessment via Analysis of Variance: An Application to Higher Education EvaluationPublication . Ferrão, Maria Eugénia; Prata, Paula; Fazendeiro, PauloThe assessment of the utility of an anonymized data set can be operational-ized by the determination of the amount of information loss. To investigate the possible degradation of the relationship between variables after anony-mization, hence measuring the loss, we perform an a posteriori analysis of variance. Several anonymized scenarios are compared with the original data. Differential privacy is applied as data anonymization process. We assess data utility based on the agreement between the original data structure and the anonymized structures. Data quality and utility are quantified by standard metrics, characteristics of the groups obtained. In addition, we use analysis of variance to show how estimates change. For illustration, we apply this ap-proach to Brazilian Higher Education data with focus on the main effects of interaction terms involving gender differentiation. The findings indicate that blindly using anonymized data for scientific purposes could potentially un-dermine the validity of the conclusions.
- Teaching in conditions of difficult knowledge transfer due to the state of emergency caused by the pandemicPublication . Mravik, Miloš; Šarac, Marko; Veinovic, Mladen; Pombo, NunoIntroduction/purpose: This paper presents the transformation of the current, classical approach to teaching. Online platforms enable students with and without disabilities to follow classes without hindrance during the lecture period. After the lecture, they are allowed to view video and presentation materials. The main advantage of this way of teaching is the possibility of attending classes from any location and from any device; it is only important to be connected to the Internet. Methods: Full integration with the already existing Faculty Information System has been performed. The paper describes a new approach to teaching and illustrates the expected benefits of online teaching. The platforms used in this integration are Microsoft Azure, Microsoft Office 365 Admin, Microsoft Teams, Microsoft Stream and Microsoft SharePoint. Results: The result of the test of work with students showed that by introducing a system for online teaching, we directly affect the improvement and quality of teaching. Conclusion: Considering all the results, it can be concluded that the transition to the online way of teaching allows end listeners a comprehensive transfer of knowledge as well as re-listening to the same. This model can be used for an unlimited number of users in all Institutions, regardless of whether the field of activity of these Institutions is of educational origin.
- Garantia de Privacidade Versus Utilidade dos Dados em Anonimização: um estudo no ensino superiorPublication . Prata, Paula; Ferrão, Maria Eugénia; Santos, Wilson; Sousa, GonçaloNo mundo digital, toda a atividade humana deixa um rasto de dados que constitui um recurso cada vez mais valioso, para avaliação e definição de estratégias nos mais variados domínios. A partilha desses dados, sendo socialmente importante, implica o respeito pela privacidade individual e portanto a sua anonimização. As atuais leis e regulamentos sobre privacidade oferecem orientações limitadas para lidar com um vasto leque de tipos de dados, ou com técnicas de reidentificação. Este trabalho pretende ilustrar um processo de anonimização, comparando para vários modelos de privacidade a perda de informação e a utilidade do conjunto de dados resultante. Encontrar o equilíbrio entre privacidade e utilidade é um desafio que pode ser mais facilmente alcançado por quem melhor conhece o significado dos dados e dos objetivos que se pretendem alcançar com eles.
- Estratégias de Tolerância a Falhas em Computação Móvel na NuvemPublication . Catumbela, Euclides; Prata, PaulaApesar de os periféricos móveis possuírem cada vez mais capacidade de computação e armazenamento, a ligação da computação móvel com a computação na núvem (cloud) é também, cada vez mais, forte. Aplicações móveis que processem ou partilhem grandes quantidades de dados usam a nuvem para superar a limitação de recursos imposta por smartphones e tablets. Estes sistemas trazem novos desafios em termos de tolerância a falhas. Por um lado funcionam com baterias cuja carga tem duração limitada e por outro lado, a mobilidade do utilizador pode dificultar a obtenção de conectividade contínua e com largura de banda invariável como seria desejável. Neste trabalho propomos e avaliamos mecanismos de tolerância a falhas para dois tipos de falhas comuns em computação móvel na nuvem: Falha da carga da bateria e falhas na ligação à rede.
- Parameterization of an Agent-Based Model of Spatial Distribution of SpeciesPublication . Bioco, João; Fazendeiro, Paulo; Canovas, Fernando; Prata, PaulaAgent-based models (ABMs) have been widely applied in several fields such as ecology, biology, climate changes, engineering and many other fields. In ABMs approach, the behaviour of a system is determined by the local interactions between its individuals (agents), and the interactions between these individuals with the environment where they exist. Due to its interactions at the individual’s level, ABMs can produce quite realistic results regarding to the models behaviour. Therefore it is necessary to perform several analysis from the point of view of the models parametrization. In this paper we perform a parametric study in ways to analyze the implications of models parameterization in the models output, by implementing an agent-based model to simulate spatial distribution of species in an heterogeneous environment. The models output resulting from several parameters combination are compared and discussed.
- Multiple imputation in big identifiable data for educational research: An example from the Brazilian education assessment systemPublication . Ferrão, Maria Eugénia; Prata, Paula; Alves, Maria Teresa G.Almost all quantitative studies in educational assessment, evaluation and educational research are based on incomplete data sets, which have been a problem for years without a single solution. The use of big identifiable data poses new challenges in dealing with missing values. In the first part of this paper, we present the state-of-art of the topic in the Brazilian education scientific literature, and how researchers have dealt with missing data since the turn of the century. Next, we use open access software to analyze real-world data, the 2017 Prova Brasil , for several federation units to document how the naïve assumption of missing completely at random may substantially affect statistical conclusions, researcher interpretations, and subsequent implications for policy and practice. We conclude with straightforward suggestions for any education researcher on applying R routines to conduct the hypotheses test of missing completely at random and, if the null hypothesis is rejected, then how to implement the multiple imputation, which appears to be one of the most appropriate methods for handling missing data.
- Data Anonymization: K-anonymity Sensitivity AnalysisPublication . Santos, Wilson; Sousa, Gonçalo; Prata, Paula; Ferrão, Maria EugéniaThese days the digitization process is everywhere, spreading also across central governments and local authorities. It is hoped that, using open government data for scientific research purposes, the public good and social justice might be enhanced. Taking into account the European General Data Protection Regulation recently adopted, the big challenge in Portugal and other European countries, is how to provide the right balance between personal data privacy and data value for research. This work presents a sensitivity study of data anonymization procedure applied to a real open government data available from the Brazilian higher education evaluation system. The ARX k-anonymization algorithm, with and without generalization of some research value variables, was performed. The analysis of the amount of data / information lost and the risk of re-identification suggest that the anonymization process may lead to the under-representation of minorities and sociodemographic disadvantaged groups. It will enable scientists to improve the balance among risk, data usability, and contributions for the public good policies and practices.