Abstract
In this study M-mode echocardiograms were performed on 22 top class racing cyclists in order to assess the incidence of structural heart alterations. A normal control group was composed of 20 sedentary men of similar age and surface body area. Statistical analysis was performed using the unpaired Student t-test. A normal LV mass was found in all athletes and normal left Ventricle diastolic diametre (LVDD) in 23 cyclists. Global and regional LV systolic functions were normal in all cyclists. Mitral valve analysis showed normal LV compliance in all athletes. Thus, the cyclists admitted to our study did not present LV hypertrophy and most of them had normal LVDD. Systolic and diastolic functions were normal in all of them.

Abstract
The authors present a novel automatic transient stability classification approach, based on a two-stage hierarchical classifier obtained through the combination of two different pattern recognition structures. The first-level classifier uses low-cost computational variables evaluated at the fault occurrence moment. The classification structure used in this first level is a linear one and is obtained through the combination of a Fisher discriminant transformation and a Bayesian probabilistic approach for the independent term determination. The second-level classifier uses a new set of transient variables, directly related to kinetic and potential energies at the fault clearance moment. The classification structure in this second level is based on a weighted K nearest-neighbor rule, using a Euclidian metric defined in an optimum discriminant plane, where all data have been previously projected. Results obtained in the CIGRE test system are presented, showing that this method provides an efficient tool for online transient stability assessment of electric power systems.

Abstract
This paper presents a new candidate method for on-line transient security assessment and enhancement based on pattern recognition techniques. Transient state variables are used as primary features and the classifier is obtained in two phases, through the combination of an optimum discriminant transformation together with a probabilistic procedure. The proposed security functions can be further refined by means of a second level classifier based on an optimal discrimination plane and using a weighted K nearest neighbor rule. It is shown that this method provides also an efficient and simple tool for on-line preventive control. Results obtained for several contingencies in the CIGRE test system are discussed.

Abstract

Abstract

Abstract
The autoregressive (AR) model is a widely used tool in electroencephalogram (EEG) analysis. The dependence of the AR model on both the segment length and several characteristic EEG patterns is addressed. The best AR model order is computed with three different criteria. The results show that the Rissanen criteria provides the more consistent order estimate for the EEG patterns considered. This study shows that for our data set, a 5th order AR model represents adequately 1- or 2-s EEG segments with the exception of featureless background, where higher order models are necessary. © 1987.