Simulation of rectifier voltage malfunction on OWECS, four-level converter, HVDC light link: Smart grid context tool

This paper presents a model for the simulation of an offshore wind system having a rectifier input voltage malfunction at one phase. The offshore wind system model comprises a variable-speed wind turbine supported on a floating platform, equipped with a permanent magnet synchronous generator using full-power four-level neutral point clamped converter. The link from the offshore floating platform to the onshore electrical grid is done through a light high voltage direct current submarine cable. The drive train is modeled by a three-mass model. Considerations about the smart grid context are offered for the use of the model in such a context. The rectifier voltage malfunction domino effect is presented as a case study to show capabilities of the model. (C) 2015 Elsevier Ltd. All rights reserved.

A saúde dos imigrantes em Portugal : que necessidades em saúde? : que utilização dos cuidados de saúde?

RESUMO - Portugal, país de imigração, viu aumentar a população imigrante em 4,56% de 2006 a 2008. Assim, torna-se importante conhecer não só as características socioeconómicas desta população imigrante, mas também quais as suas necessidades em saúde e que utilização fazem dos cuidados de saúde. Este trabalho baseou-se no IV Inquérito Nacional de Saúde realizado em 2005 e 2006 pelo INSA e analisou as populações portuguesa e imigrante nas variáveis de saúde e de utilização dos cuidados. Para a análise do rendimento utilizou-se a Curva de concentração proposta por Wagstaff, Índices de Concentração da Doença, de Utilização e Índice de LeGrand. Os resultados sugeriram melhor estado de saúde da população imigrante relativamente à população portuguesa (estado de saúde auto-reportado, sensação de mal-estar ou adoentado, dias de actividade limitada e dias de acamamento). Nas doenças crónicas (diabetes, asma e dor crónica), a população imigrante apresentou piores resultados na asma. Foram encontrados piores resultados em saúde entre as mulheres nos dois grupos de população, mas também mais frequência de utilização. Os imigrantes revelam também menor acessibilidade a consultas médicas e consumo de medicamentos. A análise do rendimento enquanto factor gerador de desigualdades em saúde permitiu concluir que existem desigualdades na distribuição do rendimento que condicionam tanto a população portuguesa como a população imigrante. Outros estudos poderão ser considerados para análise da saúde da população imigrante, especialmente os que incluam os cidadãos indocumentados, análise das populações por país de nascimento, os anos de permanência em Portugal e as causas de mortalidade. ---------------------------- ABSTRACT - Portugal, a country of immigration, has seen its immigrant population increasing 4.56% from 2006 to 2008. Therefore, it is important to analyse, not only the socioeconomic characteristics of immigrant population, but also their health needs and utilization of health care. This work was based on the IV National Health Survey conducted in 2005 and 2006 by INSA and analyzed the Portuguese and Immigrant populations in the variables of Health and Utilization of Health Services. In order to analyse the income, the Concentration Curve proposed by Wagstaff and the Concentration Index was used. The results suggested a better health in immigrant population compared with Portuguese population (state of self-reported health, feeling sick or ill, days of limited activity and days of lodging). For the variables of chronic diseases (diabetes, asthma and chronic pain), immigrants have shown worse results in asthma. In both groups (Immigrants and Portuguese), women have had more health problems than men. Lower utilization among Immigrants was found in outpatient visits and in prescription drug utilization. In conclusion, it can be stated that the analysis of the income as a generator of health inequalities showed inequalities in the income distribution that affects both Portuguese and immigrants’ health. Other studies may be considered to analyze immigrants’ health especially those that include undocumented immigrants, analysis of populations by country of birth, years of residence in Portugal and the causes of mortality.

SPE-LC-FD Determination of Polycyclic Aromatic Hydrocarbon Monohydroxy Derivatives in Cephalopods

A new analytical methodology, based on liquid chromatography with fluorescence detection (LC-FD), after extraction, enzymatic hydrolysis, and solid-phase extraction (SPE) through Oasis HLB cartridges, was developed and validated for the simultaneous determination of three monohydroxy derivatives of polycyclic aromatic hydrocarbons (PAHs). The optimized analytical method is sensitive, accurate, and precise, with recoveries between 62 and 110% and limits of detection of 227, 9, and 45 ng/g for 1-hydroxynaphthalene, 2-hydroxyfluorene, and 1-hydroxypyrene, respectively. Their levels were estimated in different cephalopod matrices (edible tissues and hemolymph). The methodology was applied to samples of the major cephalopod species consumed worldwide. Of the 18 samples analyzed, 39% were contaminated with 1-hydroxynaphthalene, which was the only PAH metabolite detected. Its concentration ranged from 786 to 1145 ng/g. This highly sensitive and specific method allows the identification and quantitation of PAH metabolites in forthcoming food safety and environmental monitoring programs.

Enantiomeric fraction evaluation of pharmaceuticals in environmentalmatrices by liquid chromatography-tandem mass spectrometry

The interest for environmental fate assessment of chiral pharmaceuticals is increasing and enantioselective analytical methods are mandatory. This study presents an enantioselective analytical method for the quantification of seven pairs of enantiomers of pharmaceuticals and a pair of a metabolite. The selected chiral pharmaceuticals belong to three different therapeutic classes, namely selective serotonin reuptake inhibitors (venlafaxine, fluoxetine and its metabolite norfluoxetine), beta-blockers (alprenolol, bisoprolol, metoprolol, propranolol) and a beta2-adrenergic agonist (salbutamol). The analytical method was based on solid phase extraction followed by liquid chromatography tandem mass spectrometry with a triple quadrupole analyser. Briefly, Oasis® MCX cartridges were used to preconcentrate 250 mL of water samples and the reconstituted extracts were analysed with a Chirobiotic™ V under reversed mode. The effluent of a laboratory-scale aerobic granular sludge sequencing batch reactor (AGS-SBR) was used to validate the method. Linearity (r2 > 0.99), selectivity and sensitivity were achieved in the range of 20–400 ng L−1 for all enantiomers, except for norfluoxetine enantiomers which range covered 30–400 ng L−1. The method detection limits were between 0.65 and 11.5 ng L−1 and the method quantification limits were between 1.98 and 19.7 ng L−1. The identity of all enantiomers was confirmed using two MS/MS transitions and its ion ratios, according to European Commission Decision 2002/657/EC. This method was successfully applied to evaluate effluents of wastewater treatment plants (WWTP) in Portugal. Venlafaxine and fluoxetine were quantified as non-racemic mixtures (enantiomeric fraction ≠ 0.5). The enantioselective validated method was able to monitor chiral pharmaceuticals in WWTP effluents and has potential to assess the enantioselective biodegradation in bioreactors. Further application in environmental matrices as surface and estuarine waters can be exploited.

Sparse matrix multiplication on a reconfigurable many-core architecture

Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.

Desenvolvimento de métodos de análise físico-química em águas usando um analisador multiparamétrico AQUAKEM

Trabalho Final de mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Simulation of offshore wind system with three-level converters: HVDC power transmission in cloud scope

This paper is on a simulation for offshore wind systems in deep water under cloud scope. The system is equipped with a permanent magnet synchronous generator and a full-power three-level converter, converting the electric energy at variable frequency in one at constant frequency. The control strategies for the three-level are based on proportional integral controllers. The electric energy is injected through a HVDC transmission submarine cable into the grid. The drive train is modeled by a three-mass model taking into account the resistant stiffness torque, structure and tower in the deep water due to the moving surface elevation. Conclusions are taken on the influence of the moving surface on the energy conversion. © IFIP International Federation for Information Processing 2015.

Simulação da Central de Cogeração da Refinaria de Matosinhos (Petrogal)

Mestrado em Engenharia Química – Ramo Optimização Energética na Indústria Química

A estruturação dos determinantes relevantes no processo de decisão do investimento direto estrangeiro em Portugal e em Espanha

Dissertação de Mestrado apresentada ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Dr. Luís Pereira Gomes

Modelling a cable structure for a new wind energy production device

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Mecânica Especialização em Concepção e Produção

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Identificação, avaliação e controlo de riscos numa empresa da indústria dos colchões

Relatório de Estágio

Combate à fraude em jogos de casino, assistida por computador

No decorrer dos últimos anos tem-se verificado um acréscimo do número de sistemas de videovigilância presentes nos mais diversos ambientes, sendo que estes se encontram cada vez mais sofisticados. Os casinos são um exemplo bastante popular da utilização destes sistemas sofisticados, sendo que vários casinos, hoje em dia, utilizam câmeras para controlo automático das suas operações de jogo. No entanto, atualmente existem vários tipos de jogos em que o controlo automático ainda não se encontra disponível, sendo um destes, o jogo Banca Francesa. A presente dissertação tem como objetivo propor um conjunto de algoritmos idealizados para um sistema de controlo e gestão do jogo de casino Banca Francesa através do auxílio de componentes pertencentes à área da computação visual, tendo em conta os contributos mais relevantes e existentes na área, elaborados por investigadores e entidades relacionadas. No decorrer desta dissertação são apresentados quatro módulos distintos, os quais têm como objetivo auxiliar os casinos a prevenir o acontecimento de fraudes durante o decorrer das suas operações, assim como auxiliar na recolha automática de resultados de jogo. Os quatro módulos apresentados são os seguintes: Dice Sample Generator – Módulo proposto para criação de casos de teste em grande escala; Dice Sample Analyzer – Módulo proposto para a deteção de resultados de jogo; Dice Calibration – Módulo proposto para calibração automática do sistema; Motion Detection – Módulo proposto para a deteção de fraude no jogo. Por fim, para cada um dos módulos, é apresentado um conjunto de testes e análises de modo a verificar se é possível provar o conceito para cada uma das propostas apresentadas.