Dissertações em Computação Aplicada (Mestrado) - PPCA/NDAE/Tucuruí

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/9399

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Clusterização de padrões espaço-temporais de precipitação na Amazônia via deep convolutional autoencoder
(Universidade Federal do Pará, 2023-07-07) SILVA, Vander Augusto Oliveira da; TEIXEIRA, Raphael Barros; http://lattes.cnpq.br/4902824086591521; https://orcid.org/0000-0003-2993-802X
Studies using different machine learning methods for knowledge discovery and pattern recognition in precipitation time series are increasingly frequent in the literature. Identify and analyze patterns in precipitation time series in a particular region is fundamental for its socioeconomic development. Therefore, it can be stated that knowledge and understanding of the rainfall characteristics of the regions are important to enable the planning of the use, management and conservation of water resources. The natural phenomenon of precipitation is a fundamental process with a direct impact on watersheds and on human and environmental development. The variability of this phenomenon has important implications for the navigability of rivers, individual abundance and species richness. In recent years, many studies with this approach have been carried out in Brazil, mainly in the Amazon region. This research aimed to develop a computational method for analyzing time series of precipitation using machine learning techniques with unsupervised learning, in order to propose an method capable of extracting complex features from the data, obtaining a map of attributes at low dimensionality for pattern recognition, discovery of homogeneous regions with respect to precipitation and approximate reconstruction of precipitation time series in the Legal Amazon. The proposed deep learning neural network model is trained to learn the main and most complex features of the original data and present them in low dimensionality in latent space. After the training, the results are promising, the observations of the reconstructed data showed a good performance as evaluated by the RMSE and NRMSE metric with resulting values equal to 0.06610 and 0.3355 respectively. The analysis of the representation of the data in low dimension was applied and analyzed by a clustering structure using hierarchical agglomerative with Ward’s method. This methodology also showed good results, as it carried out consistent groupings characterizing ho- mogeneous regions in relation to precipitation data. Thus, demonstrating that the representation in low dimensionality carried the main characteristics of the time series of the analyzed data. It is noteworthy that the method developed in this study can be applied not only in the Amazon region, but also in other areas with similar challenges related to time series analysis.
Acesso aberto (Open Access)
ICM Space Game: uma interface baseada na imaginação de movimentos
(Universidade Federal do Pará, 2023-03-10) CALVINHO, Jhoanyn Valois Fantin; MERLIN, Bruno; http://lattes.cnpq.br/7336467549495208; HTTPS://ORCID.ORG/0000-0001-7327-9960; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Brain-Machine Interfaces can help users participate in routine tasks, such as moving around. The scientific community works daily in an attempt to offer increasingly robust Brain-Machine Interface systems, with better responses to user commands. However, these works usually focus on improving the system itself. Therefore, the objective of this work is to offer an alternative to the users to help in the learning of the use of equipment of a Brain-Machine Interface based on the imagination of movements. For this, a computational tool based on a virtual game is developed in an attempt to improve the accuracy of users in controlling the devices of these systems. The results show that the tool works when connected to a Brain-Machine Interface, and can serve as an alternative in the process of collecting EEG signals. Throughout this work, programming languages dedicated to ICMs, such as OpenVibe, are used, as well as a language widely used in the programming of electronic games, Python. In the experiment carried out with 8 volunteers, there is no discrepant difference between the classification rates performed with the aid of the conventional protocol and the ICM Space Game, approximately 56% for both

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