992 resultados para Selection tests


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Resource constraints are becoming a problem as many of the wireless mobile devices have increased generality. Our work tries to address this growing demand on resources and performance, by proposing the dynamic selection of neighbor nodes for cooperative service execution. This selection is in uenced by user's quality of service requirements expressed in his request, tailoring provided service to user's speci c needs. In this paper we improve our proposal's formulation algorithm with the ability to trade o time for the quality of the solution. At any given time, a complete solution for service execution exists, and the quality of that solution is expected to improve overtime.

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OBJECTIVE To investigate differences in HIV infection- related risk practices by Female Sex Workers according to workplace and the effects of homophily on estimating HIV prevalence. METHODS Data from 2,523 women, recruited using Respondent-Driven Sampling, were used for the study carried out in 10 Brazilian cities in 2008-2009. The study included female sex workers aged 18 and over. The questionnaire was completed by the subjects and included questions on characteristics of professional activity, sexual practices, use of drugs, HIV testing, and access to health services. HIV quick tests were conducted. The participants were classified in two groups according to place of work: on the street or indoor venues, like nightclubs and saunas. To compare variable distributions by place of work, we used Chi-square homogeneity tests, taking into consideration unequal selection probabilities as well as the structure of dependence between observations. We tested the effect of homophily by workplace on estimated HIV prevalence. RESULTS The highest HIV risk practices were associated with: working on the streets, lower socioeconomic status, low regular smear test coverage, higher levels of crack use and higher levels of syphilis serological scars as well as higher prevalence of HIV infection. The effect of homophily was higher among sex workers in indoor venues. However, it did not affect the estimated prevalence of HIV, even after using a post-stratification by workplace procedure. CONCLUSIONS The findings suggest that strategies should focus on extending access to, and utilization of, health services. Prevention policies should be specifically aimed at street workers. Regarding the application of Respondent-Driven Sampling, the sample should be sufficient to estimate transition probabilities, as the network develops more quickly among sex workers in indoor venues.

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Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

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The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, there were identified five broad selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. After the identification criteria, a survey was elaborated and companies were contacted in order to understand which factors have more weight in their decisions to choose the partners. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Value Analysis. The goal of the paper it's to supply a selection reference model that can represent an orientation/pattern for a decision making on the suppliers/partners selection process

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In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

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Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia do Ambiente, Gestão de Sistemas Ambientais

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This paper presents a layered Smart Grid architecture enhancing security and reliability, having the ability to act in order to maintain and correct infrastructure components without affecting the client service. The architecture presented is based in the core of well design software engineering, standing upon standards developed over the years. The layered Smart Grid offers a base tool to ease new standards and energy policies implementation. The ZigBee technology implementation test methodology for the Smart Grid is presented, and provides field tests using ZigBee technology to control the new Smart Grid architecture approach. (C) 2014 Elsevier Ltd. All rights reserved.

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The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

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Mestrado integrado em Engenharia do Ambiente, perfil: Gestão de Sistemas Ambientais

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Micro-abrasion wear tests with ball-cratering configuration are widely used. Sources of variability are already studied by different authors and conditions for testing are parameterized by BS EN 1071-6: 2007 standard which refers silicon carbide as abrasive. However, the use of other abrasives is possible and allowed. In this work, ball-cratering wear tests were performed using four different abrasive particles of three dissimilar materials: diamond, alumina and silicon carbide. Tests were carried out under the same conditions on a steel plate provided with TiB2 hard coating. For each abrasive, five different test durations were used allowing understanding the initial wear phenomena. Composition and shape of abrasive particles were investigated by SEM and EDS. Scar areas were observed by optical and electronic microscopy in order to understand the wear effects caused by each of them. Scar geometry and grooves were analyzed and compared. Wear coefficient was calculated for each situation. It was observed that diamond particles produce well-defined and circular wear scars. Different silicon carbide particles presented dissimilar results as consequence of distinct particle shape and size distribution.

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In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

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Introdução: O hóquei em patins é a modalidade desportiva que em Portugal conquistou mais títulos, quer a nível de clubes quer a nível de selecções , por isso, torna-se pertinente verificar qual o efeito da flexibilidade na aptidão física desta mesma modalidade. Objectivos: Verificar a influência do aumento de flexibilidade numa bateria de testes de aptidão física, num atleta de hóquei em patins. Metodologia: Realizou-se um estudo de caso num atleta de hóquei em patins com défices de flexibilidade . O atleta foi sujeito a duas avaliações/reavaliações: flexibilidade global (flexiteste) e bateria de testes para avaliação da aptidão física. Entre a avaliação e reavaliação o atleta realizou um programa de treino Stretching global activo (SGA) para ganhos de flexibilidade, durante 6 semanas. Resultados: Ao fim das 6 semanas, podemos verificar que o atleta melhorou os seus resultados nos vários testes realizados . Conclusão: Com a utilização de um protocolo de treino de flexibilidade ( SGA) obteve -se um aumento da flexibilidade no atleta em estudo, verificando-se que esses ganhos provocaram melhorias nos resultados dos testes de aptidão física efectuados.

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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.

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Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

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O aço inoxidável é um material de alta durabilidade e resistência, utilizado nos mais diversos setores comerciais. O conhecimento das suas características e propriedades é essencial para uma escolha mais seletiva e vantajosa. Pretende‐se com este trabalho, estudar as propriedades mecânicas, a resistência ao desgaste e o comportamento, em ambientes mais agressivos de três tipos de aços inoxidáveis duplex, produzidos pela OUTOKUMPU e comercialmente conhecidos pelas designações LDX 2101, SAF 2507 e o SAF 2205. Para tal efeito foram realizados ensaios de Dureza Vickers, antes e após dobragem; ensaios de tracção em duas diferentes situações: seguindo a norma NP EN 10002‐ 1:2006 e após a realização de alguns ciclos de carga e descarga; ensaio de Impacto à temperatura ambiente e após arrefecimento criogénico; ensaio de resistência à corrosão. Também foi feito o estudo de resistência ao desgaste com base na técnica de micro‐abrasão por esfera rotativa e o estudo metalográfico. Foi também objetivo deste trabalho, relacionar o uso dos referidos aços duplex e as consequências que esse uso tem para o meio ambiente, bem como o seu comportamento quando exposto a condições extremas. Para tal, foram colocadas amostras dos referidos aços, em dois tipos de solos com condições de humidade e acidez diferentes analisando o seu estado após imersão em solo com condições controladas durante 272 dias.