Search results for "Support"

Set valued Kurzweil-Henstock-Pettis integral

2005

It is shown that the obvious generalization of the Pettis integral of a multifunction obtained by replacing the Lebesgue integrability of the support functions by the Kurzweil--Henstock integrability, produces an integral which can be described -- in case of multifunctions with (weakly) compact convex values -- in terms of the Pettis set-valued integral.

The Psycho-Biological Bases of Sports Supporters' Behaviour: The Virtuous Supporter

2012

Given current studies in moral psychology and following recent cases of wrong behaviour occurred in elite sporting events – e.g. the racist chants scandals in the English Premier League or the events following Mourinho's poke in the eye scandal – I shall analyse the extent to which supporters' brain make-up is determining them to behave in an ‘unfair way’. Yet this paper is not just a work on descriptive ethics, but a normative ethics work. Therefore, once I have developed the ‘psycho-biological account of sports supporters’, I shall explore whether or not a more virtuous account of sports supporting can be drawn. In order to fulfil this normative task I shall appeal to the concept of ‘fair…

Technical support for a judge when assessing a priori odds

2015

Importance of torsion and invariant volumes in Palatini theories of gravity

2013

We study the field equations of extensions of general relativity formulated within a metric-affine formalism setting torsion to zero (Palatini approach). We find that different (second-order) dynamical equations arise depending on whether torsion is set to zero (i) a priori or (ii) a posteriori, i.e., before or after considering variations of the action. Considering a generic family of Ricci-squared theories, we show that in both cases the connection can be decomposed as the sum of a Levi-Civita connection and terms depending on a vector field. However, while in case (i) this vector field is related to the symmetric part of the connection, in (ii) it comes from the torsion part and, therefo…

Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

2015

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load…

A novel four-quadrant power supply for low-energy correction magnets

2003

Abstract This paper describes an efficient power supply to feed low-energy correction magnets in particle accelerator applications, where a controlled current with trapezoidal profile and four-quadrant operation is needed. The selected design is based on an AC–DC matrix converter topology, which uses the Space Vector Modulation (SVM) technique to obtain a near unity power factor at the AC input and output DC current regulation. This topology allows performing high-frequency isolation, while four-quadrant operation is maintained, and reducing volume and weight as compared with the classical thyristor (SCR)-based technology. Control tasks are implemented on an all-digital control card: output…

Decomposing encoding and decisional components in visual-word recognition: a diffusion model analysis.

2014

In a diffusion model, performance as measured by latency and accuracy in two-choice tasks is decomposed into different parameters that can be linked to underlying cognitive processes. Although the diffusion model has been utilized to account for lexical decision data, the effects of stimulus manipulations in previous experiments originated from just one parameter: the quality of the evidence. Here we examined whether the diffusion model can be used to effectively decompose the underlying processes during visual-word recognition. We explore this issue in an experiment that features a lexical manipulation (word frequency) that we expected to affect mostly the quality of the evidence (the dri…

A support vector domain method for change detection in multitemporal images

2010

This paper formulates the problem of distinguishing changed from unchanged pixels in multitemporal remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere-shaped decision boundary with minimal volume that embraces changed pixels is approached in the context of the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. Unlike the standard SVDD, the proposed formulation of the SVDD uses both target and outlier samples for defi…

Shape Description for Content-Based Image Retrieval

2000

The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be employed for CBIR applications.To this aim, a recognition system has been developed, that detects automatically image ROIs containing single objects, and classifies them as belonging to a particular class of shapes.In our approach we make use of the eigenvalues of the covariance matrix computed from the pixel rows of a single ROI. These quantities are arranged in a vector form, and are classified using Support Vector Machines (SVMs). The selected feature allows us to recognize shapes in a robust fashion, despite rotations or…

Cluster kernels for semisupervised classification of VHR urban images

2009

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and wor…