Radu Neagu

The determination of relaxation parameters and their distributions using thermally stimulated discharge current measurements

For any elemental peak obtained by the thermally stimulated depolarization current technique, there is a relationship between the activation energy W, the pre-exponential factor τ0, the temperature of the maximum current Tm and the heating rate b. This relationship can yield useful information concerning the values and the distribution of the relaxation parameters. Numerical simulations, using concrete experimental data obtained for nylon 11, are used to demonstrate the analysis for organic dielectrics. Lower limits for the incertitude intervals of W and τ0 represent the natural or minimum incertitude intervals expected for an elemental relaxation process. Taking advantage of the fact that the natural incertitude interval in activation energies is ΔWkTm (k is Boltzmanns constant), the natural incertitude interval for τ0 is deduced as Δτ0τ0. For example, assuming Tm = 300 K and Δτ0 = 0, the interval ΔTm for two resolved neighbour elemental peaks, in other words the interval in which Tm can have values as W varies in the limits on the natural incertitude interval, increases from 7.1 to 11.9 K as W decreases from 1.05 to 0.59 eV. An experimental thermogram can be decomposed into a limited number of elemental peaks having W and τ0 distributed in the limits of the natural or minimum incertitude intervals (ΔWkTm and Δτ0τ0). The distribution function for a relaxation parameter cannot be determined unambiguously for the case when the width of the distribution is comparable with the natural standard deviation for the given conditions. Only one parameter or only one distribution must be avoided considering any analysis as variable.

Tracking and separating non-stationary multi-component chirp signals

Abstract In this paper we are concerned with the problems of (1) tracking or estimating the unknown, time-varying instantaneous frequency (IF) of a chirp signal from a multi-component signal (we assume our multi-component signal to be formed of additive chirp signals, disjoint in the time–frequency domain, and Gaussian noise) and (2) reconstructing a specific chirp signal based on the estimate of its IF found at (1). The algorithm we developed is based on a previously proposed method adapted now for the case of multi-component signals. It combines an adaptive smoothing procedure with a noise resistant Fourier filter to generate an algorithm with an extremely fine frequency resolution. The method is non-parametric, that is, we assume no prior knowledge about the form of the time-varying IF of the chirp or about the chirp itself. We demonstrate how the method works on simulated data and compare its performance to other presently used procedures.

Algorithm for adaptively smoothing the log-periodogram

We use the principle of minimum cross entropy (MCE) to build a non-parametric adaptive algorithm for smoothing the log-transformed periodogram, and construct an optimal estimate for the spectral density function of a process. We show that this estimate minimizes the cross-entropy with the log-transformed spectral density function of the process. The method is non-parametric and performs very well for the case of processes having rapidly changing spectra that exhibits a variable order of smoothness. The algorithm is locally based on linearly approximating the information present in the process, and uses this approximation to allow the bandwidth of the spectral window in the smoothed log-periodogram to vary. We extend the algorithm empirically for better application to processes having mixed, narrow-band spectra. Comparisons with other currently used procedures are performed through the means of simulated examples.

The Study of Dipolar and Space Charges in Semicrystalline Polymers

The contribution of dipolar and space charge to the released current for two polar semicrystalline polymers, in a temperature range including the glass-rubber transition temperature Tg, is analysed. Both types of charge are present for a wide range of polarization conditions. The space charge is captured in deeper traps and consequently is more stable. The activation energy continuously increases as the temperature increases and a compensation behavior was observed. While the peak is centred around Tg the position and magnitude of the space charge peak changes significantly with polarization conditions. There is a narrow distribution of the relaxation parameters. For Nylon 11 samples a pyroelectric behavior was observed.

Six Sigma Quality Improvement with Minitab

The coverage of the book is certainly concise and practically-based, but inclusion of a discussion on the use of EM-algorithm [particularly in the case of the location parameter being zero; see McLachlan and Krishnan (1997)] and some recent work on inference under progressive censoring and associated reliability sampling plans [see, for example, Balasooriya et al. (2000) and Ng et al. (2002, 2004)] would have been beneficial.

Analysis of Dielectric Relaxation Parameters in the Range of Glass Transition

The apparent activation energy W, the temperature Ta at which the molecular movement is assumed frozen (the relaxation time is infinitely large) and the preexponential factor t0 were determined for Nylon 11 using the data obtained by fractional polarization thermally stimulated discharge current technique. The aim was to investigate the temperature dependence of the relaxation time in the range of the glass transition temperature. An experimental thermogram can be analyzed equally well using the Vogel-Fulcher-Tammann-Hess relationship or the Arrhenius equation. Experimental results and simulations, for nearby peaks, demonstrate that the relaxation time at peak temperature changes from about 100 to 500 s revealing a very narrow distribution in relaxation times. On contrary, W and especially t0 varies in large limits and the values for t0 are in many situations physically meaningless. It was found that the temperature at which the molecular movement can be assumed frozen for electric effects is about 150 K below the glass transition temperature in Nylon 11. A small change in W (in the limit ± kTm which represents the minimum uncertainty interval in W) induces a change in t0 of about two decades, indicating (i) that any analysis assuming t0 constant should be avoided and (ii) the selectivity limit of the method.

The Six Sigma Practitioner's Guide to Data Analysis

The Six Sigma Practitioners Guide to Data Analysis by Donald J. Wheeler RADU NEAGU Taguchis Quality Engineering Handbook by Genichi Taguchi, Subir Chowdhury, and Yuin Wu ............................................................... THOMAS P. RYAN Introduction to Statistical Quality Control, Fifth Edition by Douglas C. Montgomery ........................................................... WILLIAM H. WOODALL

Observations Concerning Relaxation Parameters for Polarisation Decay and their Distributions in the Range of Glass Transition

An improved expression for the thermally stimulated discharge current is proposed which takes into account that the activation energy W and the pre-exponential factor 0 change simultaneously. The minimum uncertainty intervals affecting the relaxation parameters are estimated and it is shown that they produce a broadening of a theoretical peak. The distribution of the activation energies and pre-exponential factors cannot be determined accurately if the width of the distribution in W is about kTm or the width of the distribution in 0 is about 0.