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Frequency-domain Analysis (frequency-domain + analysis)
Selected AbstractsHigh-frequency behavior of power inductor windings using an accurate multiconductor transmission line model: input impedance evaluationEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 5 2008J. A. Brandão Faria Abstract This research and tutorial paper is the second part of a work dedicated to the analysis and computation of the electromagnetic behavior of inductor windings operating at high-frequency regimes,a critical issue for very fast transient overvoltage studies. The inductor winding, wound around a ferromagnetic core, containing a total number of N dielectric coated cylindrical turns, is modeled by using a multiconductor transmission line (MTL) approach (proximity effects being accounted) whose constitution and characterization was presented in a former paper. In the present work, we make use of the R, G, L, and C constitutive matrices of the structure in order to develop a modal analysis technique-based formulation aimed at the evaluation of the winding's input impedance in the frequency-domain. Results obtained show that the input impedance critically depends not only on the number of layers of the winding but also, and, more importantly, on the frequency, where resonance phenomena play a key role. Frequency-domain analysis is complemented with simulation results in the time-domain that clearly illustrate how critical and sensitive the system response can be under minute changes of the winding's excitation current. Copyright © 2007 John Wiley & Sons, Ltd. [source] Frequency-based fatigue analysis of non-stationary switching random loadsFATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 11 2007D. BENASCIUTTI ABSTRACT The service loadings in real systems are not only random, but also non-stationary. The spectral methods based on a frequency-domain characterization of random loads, which have been used in alternative to classical time-domain approaches, cannot be applied to non-stationary loads, because the conventional spectral density spectrum is not able to capture the evolutionary frequency characteristics of non-stationary loads. This clearly restricts the applicability of the existing frequency-based methods only to loads which are stationary. At the same time, it is also very difficult to propose general models valid for all types of load non-stationarity encountered in practice. Therefore, a practical approach is to restrict the analysis to a specific class of non-stationary loads; in this work, we consider particular non-stationary loads (i.e. switching loads), which are piecewise stationary in their variance. A frequency-domain analysis of such loads is proposed, which is based on a combination of the frequency-based analysis of each adjacent stationary segment, which can be either Gaussian or non-Gaussian. Numerically simulated load histories, as well as loads measured on mountain bikes in special tracks, are analysed to validate the proposed methodology. The presented results also show the correlation between load non-stationarity and non-Gaussianity. [source] Ectopic Beats in Heart Rate Variability Analysis: Effects of Editing on Time and Frequency Domain MeasuresANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 1 2001Mirja A. Salo M.Sc. Background: Various methods can be used to edit biological and technical artefacts in heart rate variability (HRV), but there is relatively little information on the effects of such editing methods on HRV. Methods: The effects of editing on HRV analysis were studied using R-R interval data of 10 healthy subjects and 10 patients with a previous myocardial infarction (Ml). R-R interval tachograms of verified sinus beats were analyzed from short-term (,5 min) and long-term (,24 hours) recordings by eliminating different amounts of real R-R intervals. Three editing methods were applied to these segments: (1) interpolation of degree zero, (2) interpolation of degree one, and (3) deletion without replacement. Results: In time domain analysis of short-term data, the standard deviation of normal-to-normal intervals (SDANN) was least affected by editing, and 30%-50% of the data could be edited by all the three methods without a significant error (< 5%). In the frequency domain analysis, the method of editing resulted in remarkably different changes and errors for both the high-frequency (HF) and the low-frequency (LF) spectral components. The editing methods also yielded in different results in healthy subjects and AMI patients. In 24-hour HRV analysis, up to 50% could be edited by all methods without an error larger than 5% in the analysis of the standard deviation of normal to normal intervals (SDNN). Both interpolation methods also performed well in the editing of the long-term power spectral components for 24-hour data, but with the deletion method, only 5% of the data could be edited without a significant error. Conclusions: The amount and type of editing R-R interval data have remarkably different effects on various HRV indices. There is no universal method for editing ectopic beats that could be used in both the time-domain and the frequency-domain analysis of HRV. A.N.E. 2001;6(1):5,17 [source] Decrease in heart rate variability with overtraining: assessment by the Poincaré plot analysisCLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING, Issue 1 2004Laurent Mourot Summary Numerous symptoms have been associated with the overtraining syndrome (OT), including changes in autonomic function. Heart rate variability (HRV) provides non-invasive data about the autonomic regulation of heart rate in real-life conditions. The aims of the study were to: (i) characterize the HRV profile of seven athletes (OA) diagnosed as suffering of OT, compared with eight healthy sedentary (C) and eight trained (T) subjects during supine rest and 60° upright, and (ii) compare the traditional time- and frequency-domain analysis assessment of HRV with the non-linear Poincaré plot analysis. In the latter each R-R interval is plotted as a function of the previous one, and the standard deviations of the instantaneous (SD1) and long-term R-R interval variability are calculated. Total power was higher in T than in C and OA both in supine (1158 ± 1137, 6092 ± 3554 and 2970 ± 2947 ms2 for C, T and OA, respectively) and in upright (640 ± 499, 1814 ± 806 and 1092 ± 712 ms2 for C, T and OA, respectively; P<0·05) positions. In supine position, indicators of parasympathetic activity to the sinus node were higher in T compared with C and OA (high-frequency power: 419·1 ± 381·2, 1105·3 ± 781·4 and 463·7 ± 715·8 ms2 for C, T and OA, respectively; P<0·05; SD1: 29·5 ± 18·5, 75·2 ± 17·2 and 37·6 ± 27·5 for C, T and OA, respectively; P<0·05). OA had a marked predominance of sympathetic activity regardless of the position (LF/HF were 0·47 ± 0·35, 0·47 ± 0·50 and 3·96 ± 5·71 in supine position for C, T and OA, respectively, and 2·09 ± 2·17, 7·22 ± 6·82 and 12·04 ± 10·36 in upright position for C, T and OA, respectively). The changes in HRV indexes induced by the upright posture were greater in T than in OA. The shape of the Poincaré plots allowed the distinction between the three groups, with wide and narrow shapes in T and OA, respectively, compared with C. As Poincaré plot parameters are easy to compute and associated with the ,width' of the scatter gram, they corroborate the traditional time- and frequency-domain analysis. We suggest that they could be used to indicate fatigue and/or prevent OT. [source] | ||||||||||