Avaliação de fiabilidade de sistemas elétricos e de automação em instalações de frio industrial

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia/Automação e Eletrónica Industrial

O terceiro corpo na escrita cénica contemporânea : uma proposta de intervenção para a formação do intérprete

Doutoramento em Motricidade Humana na especialidade de Dança

Divulgação de informação voluntária : análise empírica às empresas do PSI-20

Mestrado em Contabilidade e Análise Financeira

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Persistência no desempenho dos fundos de investimento em ações

Mestrado em Contabilidade e Análise Financeira

Previsão de diagramas de carga via Holt-Winters com três sazonalidades

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Establishment of optimal physical assets inspection frequency based on risk principles

Risk Based Inspection (RBI) is a risk methodology used as the basis for prioritizing and managing the efforts for an inspection program allowing the allocation of resources to provide a higher level of coverage on physical assets with higher risk. The main goal of RBI is to increase equipment availability while improving or maintaining the accepted level of risk. This paper presents the concept of risk, risk analysis and RBI methodology and shows an approach to determine the optimal inspection frequency for physical assets based on the potential risk and mainly on the quantification of the probability of failure. It makes use of some assumptions in a structured decision making process. The proposed methodology allows an optimization of inspection intervals deciding when the first inspection must be performed as well as the subsequent intervals of inspection. A demonstrative example is also presented to illustrate the application of the proposed methodology.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Earthquakes in western Iberia: improving the understanding of lithospheric deformation in a slowly deforming region

Mainland Portugal, on the southwestern edge of the European continent, is located directly north of the boundary between the Eurasian and Nubian plates. It lies in a region of slow lithospheric deformation (< 5 mm yr(-1)), which has generated some of the largest earthquakes in Europe, both intraplate (mainland) and interplate (offshore). Some offshore earthquakes are nucleated on old and cold lithospheric mantle, at depths down to 60 km. The seismicity of mainland Portugal and its adjacent offshore has been repeatedly classified as diffuse. In this paper, we analyse the instrumental earthquake catalogue for western Iberia, which covers the period between 1961 and 2013. Between 2010 and 2012, the catalogue was enriched with data from dense broad-band deployments. We show that although the plate boundary south of Portugal is diffuse, in that deformation is accommodated along several distributed faults rather than along one long linear plate boundary, the seismicity itself is not diffuse. Rather, when located using high-quality data, earthquakes collapse into well-defined clusters and lineations. We identify and characterize the most outstanding clusters and lineations of epicentres and correlate them with geophysical and tectonic features (historical seismicity, topography, geologically mapped faults, Moho depth, free-air gravity, magnetic anomalies and geotectonic units). Both onshore and offshore, clusters and lineations of earthquakes are aligned preferentially NNE-SSW and WNW-ESE. Cumulative seismic moment and epicentre density decrease from south to north, with increasing distance from the plate boundary. Only few earthquake lineations coincide with geologically mapped faults. Clusters and lineations that do not match geologically mapped faults may correspond to previously unmapped faults (e.g. blind faults), rheological boundaries or distributed fracturing inside blocks that are more brittle and therefore break more easily than neighbour blocks. The seismicity map of western Iberia presented in this article opens important questions concerning the regional seismotectonics. This work shows that the study of low-magnitude earthquakes using dense seismic deployments is a powerful tool to study lithospheric deformation in slowly deforming regions, such as western Iberia, where high-magnitude earthquakes occur with long recurrence intervals.