Higher order statistics for the κ–μ distribution

Closed-form expressions for the level crossing rate and average fade duration of a kappa–mu distributed fading signal envelope are presented. The proposed equations are validated by reduction to known Rice, Rayleigh and Nakagami-m special cases. They are also compared with measured data obtained from field trials analysing human body to body radio channels and shown to provide good agreement.

The structure of the Statistics Anxiety Rating Scale: A confirmatory factor analysis using UK psychology students

This study aimed to examine the structure of the statistics anxiety rating scale. Responses from 650 undergraduate psychology students throughout the UK were collected through an on-line study. Based on previous research three different models were specified and estimated using confirmatory factor analysis. Fit indices were used to determine if the model fitted the data and a likelihood ratio difference test was used to determine the best fitting model. The original six factor model was the best explanation of the data. All six subscales were intercorrelated and internally consistent. It was concluded that the statistics anxiety rating scale was found to measure the six subscales it was designed to assess in a UK population.

Inscribir el declive en el tiempo. Detroit: Auge y crisis de la ciudad industrial

A comienzos del siglo XX, Detroit era una ciudad dinámica en pleno desarrollo. Pronto se convirtió en la cuarta ciudad de Estados Unidos, la capital de la naciente industria automovilística. El crecimiento se prolongó hasta finales de los años 50, cuando, a pesar del auge económico de Estados Unidos y de su área metropolitana, Detroit comenzó a mostrar los primeros signos de estancamiento. La crisis se ha prolongado hasta hoy, cuando Detroit constituye el paradigma de la ciudad industrial en declive. Estas dos imágenes contrapuestas, el auge y la crisis, no parecen explicar por sí mismas las causas de la intensidad y persistencia del declive de Detroit. Analizar las interacciones entre crecimiento económico, políticas públicas locales y desarrollo urbano a lo largo del tiempo permitirá subrayar las continuidades y comprender en qué medida el declive de Detroit ancla sus raíces en el modelo planteado durante la etapa de auge.

Deflation based nonlinear canonical correlation analysis

This paper introduces two new techniques for determining nonlinear canonical correlation coefficients between two variable sets. A genetic strategy is incorporated to determine these coefficients. Compared to existing methods for nonlinear canonical correlation analysis (NLCCA), the benefits here are that the nonlinear mapping requires fewer parameters to be determined, consequently a more parsimonious NLCCA model can be established which is therefore simpler to interpret. A further contribution of the paper is the investigation of a variety of nonlinear deflation procedures for determining the subsequent nonlinear canonical coefficients. The benefits of the new approaches presented are demonstrated by application to an example from the literature and to recorded data from an industrial melter process. These studies show the advantages of the new NLCCA techniques presented and suggest that a nonlinear deflation procedure should be considered. (c) 2006 Elsevier B.V. All rights reserved.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.