999 resultados para Bunge-wand-weber Model


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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We present the first model-independent measurement of the helicity of W bosons produced in top quark decays, based on a 1 fb(-1) sample of candidate t (t) over bar events in the dilepton and lepton plus jets channels collected by the D0 detector at the Fermilab Tevatron p (p) over bar Collider. We reconstruct the angle theta(*) between the momenta of the down-type fermion and the top quark in the W boson rest frame for each top quark decay. A fit of the resulting cos theta(*) distribution finds that the fraction of longitudinal W bosons f(0)=0.425 +/- 0.166(stat)+/- 0.102(syst) and the fraction of right-handed W bosons f(+)=0.119 +/- 0.090(stat)+/- 0.053(syst), which is consistent at the 30% C.L. with the standard model.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.