Instrumento de avaliação de processos cognitivos para a aprendizagem de subestações de energia em ambientes 2D e realidade virtual

This study describes the development of a prototype to evaluate the potential of environments based on two-dimensional modeling and virtual reality as power substations learning objects into training environments from a central operation and control of power utility Cemig. Initially, there was an identification modeling features and cognitive processes in 2D and RV, from which it was possible to create frames that serve to guide the preparation of a checklist with assigning a metric weight for measuring cognitive potential learning in the study sites. From these contents twenty-four questions were prepared and each was assigned a weight that was used in the calculation of the metric; the questions were grouped into skill sets and similar cognitive processes called categories. Were then developed two distinct environments: the first, the prototype features an interactive checklist and your individual results. And, second, a system of data management environment for the configuration and editing of the prototype, and the observation and analysis of the survey results. For prototype validation, were invited to access the virtual checklist and answer it, five professionals linked to Cemig's training area. The results confirmed the validity of this instrument application to assess the possible potential of modeling in 2D and RV as learning objects in power substations, as well as provide feedback to developers of virtual environments to improve the system.

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.