23 resultados para First Position
em Universidade do Minho
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The main features of most components consist of simple basic functional geometries: planes, cylinders, spheres and cones. Shape and position recognition of these geometries is essential for dimensional characterization of components, and represent an important contribution in the life cycle of the product, concerning in particular the manufacturing and inspection processes of the final product. This work aims to establish an algorithm to automatically recognize such geometries, without operator intervention. Using differential geometry large volumes of data can be treated and the basic functional geometries to be dealt recognized. The original data can be obtained by rapid acquisition methods, such as 3D survey or photography, and then converted into Cartesian coordinates. The satisfaction of intrinsic decision conditions allows different geometries to be fast identified, without operator intervention. Since inspection is generally a time consuming task, this method reduces operator intervention in the process. The algorithm was first tested using geometric data generated in MATLAB and then through a set of data points acquired by measuring with a coordinate measuring machine and a 3D scan on real physical surfaces. Comparison time spent in measuring is presented to show the advantage of the method. The results validated the suitability and potential of the algorithm hereby proposed
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Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1ºCiclo do Ensino Básico
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Tese de Doutoramento em Sociologia
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Tese de Doutoramento em Contabilidade
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Tese de Doutoramento em Ciências da Cultura - Especialidade em Culturas do Extremo Oriente
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Tese de Doutoramento em Engenharia Têxtil
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Dissertação de mestrado integrado em Psicologia
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Tese de Doutoramento em Tecnologias e Sistemas de Informação
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The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.
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Dissertação de mestrado em Engenharia Mecânica
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Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores (área de especialização em Robótica)
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Dissertação de mestrado em Química Medicinal
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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores
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Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas
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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores