47 resultados para Subexponential distributions


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This paper investigates the variation of the integrated density of states with conduction activation energy in hydrogenated amorphous silicon thin film transistors. Results are given for two different gate insulator layers, PECVD silicon oxide and thermally grown silicon dioxide. The different gate insulators produce transistors with very different initial transfer characteristics, but the variation of integrated density of states with conduction activation energy is shown to be similar.

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A parametric set of velocity distributions has been investigated using a flat plate experiment. Three different diffusion factors and peak velocity locations were tested. These were designed to mimic the suction surfaces of Low Pressure (LP) turbine blades. Unsteady wakes, inherent in real turbomachinery flows, were generated using a moving bar mechanism. A turbulence grid generated a freestream turbulence level that is believed to be typical of LP turbines. Measurements were taken across a Reynolds number range of 50,000-220,000 at three reduced frequencies (0.314, 0.628, 0.942). Boundary layer traverses were performed at the nominal trailing edge using a Laser Doppler Anemometry system and hot-films were used to examine the boundary layer behaviour along the surface. For every velocity distribution tested, the boundary layer separated in the diffusing flow downstream of the peak velocity. The loss production is dominated by the mixing in the reattachment process, mixing in the turbulent boundary layer downstream of reattachment and the effects of the unsteady interaction between the wakes and the boundary layer. A sensitive balance governs the optimal location of peak velocity on the surface. Moving the velocity peak forwards on the blade was found to be increasingly beneficial when bubblegenerated losses are high, i.e. at low Reynolds number, at low reduced frequency and at high levels of diffusion. Copyright © 2008 by ASME.

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Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly- suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the domain of existing models. Such models allow us to specify the distribution over the number of features per data point, and can achieve better performance on data sets where the number of features is not well-modeled by the original distribution.

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We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset. © 2012 IEEE.