219,091 research outputs found
Semanario erudito y curioso de Salamanca
No full textTítulo tomado de la cubierta del tomoAlgunos n. con suplementoÍndices: Publicados en Tomo X, n. 318 (31 marzo 1796) y Tomo XI, n. 344 (28 jun. 1796)Descripción basada en: N. 71 (3 jun. 1794)A partir del N. 278 (29 dic. 1795) el impresor es: Oficina de Francisco de ToxarA partir del N. 380 (1 nov. 1796) el impresor es: Imprenta de la calle Prior, por los impresores Manuel de Vega y Manuel Rodrígue
Normalized Power Prior Bayesian Analysis
No full textThe elicitation of power prior distributions is based on the availability of historical data, and is realized by raising the likelihood function of the historical data to a fractional power. However, an arbitrary positive constant before the like- lihood function of the historical data could change the inferential results when one uses the original power prior. This raises a question that which likelihood function should be used, one from raw data, or one from a su±cient-statistics. We propose a normalized power prior that can better utilize the power parameter in quantifying the heterogeneity between current and historical data. Furthermore, when the power parameter is random, the optimality of the normalized power priors is shown in the sense of maximizing Shannon's mutual information. Some comparisons between the original and the normalized power prior approaches are made and a water-quality monitoring data is used to show that the normalized power prior is more sensible.Bayesian analysis, historical data, normalized power prior, power prior, prior elicitation, Shannon's mutual information.
Development of active directional antennae for use in small UAVs
No full textFive different light-weight, medium and high gain directional antennas have been developed, which can be operated together with a gimbals system on the UAV or an antenna tracker system on the ground or with both to extend the range of communication and improve the quality of video signals. The antennas are based on PCB to reduce the mass. There are two types of antennas developed for different transmission frequency: Patch Antenna and Yagi-Uda Antenna. The gimbals system are under developing, which will be based on Arduino Micro Development Board, also quite light, and the board will drive a pan and tilt structure constructed with two small but powerful servos to ensure the antenna to point at the ground station
Prior upper body exercise reduces cycling work capacity but not critical power
No full textPurpose: This study examined whether metabolite accumulation, induced by prior upper body exercise, affected the power–duration relationship for leg cycle ergometry
Semanario erudito y curioso de Salamanca
No full textTítulo tomado de la cubierta del tomoAlgunos n. con suplementoCopia digital. España : Ministerio de Cultura y Deporte. Subdirección General de Cooperación BibliotecariaÍndices: Publicados en Tomo X, n. 318 (31 marzo 1796) y Tomo XI, n. 344 (28 jun. 1796)Descripción basada en: N. 71 (3 jun. 1794)A partir del N. 278 (29 dic. 1795) el impresor es: Oficina de Francisco de ToxarA partir del N. 380 (1 nov. 1796) el impresor es: Imprenta de la calle Prior, por los impresores Manuel de Vega y Manuel Rodrígue
Including design in e-manufacturing
No full textThis paper reviews major issues in the implementation of e-manufacturing, particularly the design aspects. It will examine recent progress, drawing out particular issues that are being addressed. Use will be made of the work by the author and colleagues to devise rule-based design and Internet-based control of machines to illustrate how these developments affect the integrated e-manufacturing environment. A dynamic Simulink model of the way e-manufacture is affected by overall design delays is used to evaluate general solutions for partial and complete e-based companies. These models show how changing to improved designs reduces WI
Prior elicitation in Bayesian quantile regression for longitudinal data
No full text© 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original auhor and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.In this paper, we introduce Bayesian quantile regression for longitudinal data in terms of informative priors and Gibbs sampling. We develop methods for eliciting prior distribution to incorporate historical data gathered from similar previous studies. The methods can be used either with no prior data or with complete prior data. The advantage of the methods is that the prior distribution is changing automatically when we change the quantile. We propose Gibbs sampling methods which are computationally efficient and easy to implement. The methods are illustrated with both simulation and real data.This article is made available through the Brunel Open Access Publishing Fund
Empirical measurements of small unmanned aerial vehicle co-axial rotor systems
No full textSmall unmanned aerial vehicles (SUAV) are beginning to dominate the area of intelligence, surveillance, target acquisition and reconnaissance (ISTAR) in forward operating battlefield scenarios. Of particular interest are vertical take-off and landing (VTOL) variants. Within this category co-axial rotor designs have been adopted due to their inherent advantages of size and power to weight ratio. The inter-rotor spacing attribute of a co-axial rotor system appears to offer insight into the optimum design characteristic. The H/D ratio has been cited as a significant factor in many research papers, but to date has lacked an empirical value or an optimal dimensionless condition. In this paper the H/D ratio of a SUAV has been explored thoroughly, reviewing the performance of these systems at incremental stages, the findings from this study have shown that a range of H/D ratios in the region of (0.41-0.65) is advantageous in the performance of SUAV systems. This finding lends itself to the theory of inter-rotor spacing as a non-dimensionally similar figure, which cannot be applied across a spectrum of systems; this could be attributed to the viscous losses of flight at low Reynolds Numbers (< 50,000
Prior elicitation and variable selection for bayesian quantile regression
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression coefficients vector given a specific model and Bayesian computational efficiency (Chen et al., 1999). These difficulties become more challenging in Bayesian quantile regression framework when one is interested in assigning priors that depend on different quantile levels. The objective of Bayesian quantile regression (BQR), which is a newly proposed tool, is to deal with unknown parameters and model uncertainty in quantile regression (QR). However, Bayesian subset selection in quantile regression models is usually a difficult issue due to the computational challenges and nonavailability of conjugate prior distributions that are dependent on the quantile level. These challenges are rarely addressed via either penalised likelihood function or stochastic search variable selection (SSVS). These methods typically use symmetric prior distributions for regression coefficients, such as the Gaussian and Laplace, which may be suitable for median regression. However, an extreme quantile regression should have different regression coefficients from the median regression, and thus the priors for quantile regression coefficients should depend on quantiles. This thesis focuses on three challenges: assigning standard quantile dependent prior distributions for the regression coefficients, assigning suitable quantile dependent priors over model space and achieving computational efficiency. The first of these challenges is studied in Chapter 2 in which a quantile dependent prior elicitation scheme is developed. In particular, an extension of the Zellners prior which allows for a conditional conjugate prior and quantile dependent prior on Bayesian quantile regression is proposed. The prior is generalised in Chapter 3 by introducing a ridge parameter to address important challenges that may arise in some applications, such as multicollinearity and overfitting problems. The proposed prior is also used in Chapter 4 for subset selection of the fixed and random coefficients in a linear mixedeffects QR model. In Chapter 5 we specify normal-exponential prior distributions for the regression coefficients which can provide adaptive shrinkage and represent an alternative model to the Bayesian Lasso quantile regression model. For the second challenge, we assign a quantile dependent prior over model space in Chapter 2. The prior is based on the percentage bend correlation which depends on the quantile level. This prior is novel and is used in Bayesian regression for the first time. For the third challenge of computational efficiency, Gibbs samplers are derived and setup to facilitate the computation of the proposed methods. In addition to the three major aforementioned challenges this thesis also addresses other important issues such as the regularisation in quantile regression and selecting both random and fixed effects in mixed quantile regression models
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