Gaussian Fitting Algorithm for Spectral Overlapping Peaks and Terahertz Time-Domain Curves

2021 ◽  
Vol 58 (4) ◽  
pp. 0407002
Author(s):  
杨家懿 Yang Jiayi ◽  
熊永前 Xiong Yongqian
Keyword(s):  
Time Domain ◽  
Gaussian Fitting ◽  
Fitting Algorithm ◽  
Overlapping Peaks

High-resolution bootstrapped differential semblance

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. U39-U47 ◽  
Author(s):  
Brahim Abbad ◽  
Bjørn Ursin
Keyword(s):  
Time Domain ◽  
Field Data ◽  
Standard Errors ◽  
Velocity Analysis ◽  
Residual Velocity ◽  
Fitting Algorithm ◽  
Parameter Tracking ◽  
Multichannel Data ◽  
Resampling Procedure ◽  
Statistical Resampling

We formulated two coherency measures, based on the bootstrapped differential semblance (BDS) estimator, that offered higher resolution in parameter tracking than did standard normalized differential semblance. Bootstrapping is a statistical resampling procedure used to infer estimates of standard errors and confidence intervals from data samples for which the statistical properties are unattainable via simple means, or when the probability density function is unkown or difficult to estimate. The first proposed estimator was based on a deterministic sorting of original offset traces by alternating near and far offsets to achieve maximized time shifts between adjacent traces. The near offsets were indexed with odd integers, while the even integers were used to index far offsets that were located at a constant index increment from the previous trace. The second was the product of several BDS terms, with the first term being the deterministic BDS defined above. The other terms were generated by random sorting of traces that alternated near and far offsets in an unpredictible manner. The proposed estimators could be applied in building velocity (and anellipticity) spectra for time-domain velocity analysis, depth-domain residual velocity update, or to any parameter-fitting algorithm involving discrete multichannel data. The gain in resolution provided by the suggested estimators over the differential semblance coefficient was illustrated on a number of synthetic and field data examples.

scholarly journals Combining the Distribution of Relaxation Times from EIS and Time-Domain Data for Parameterizing Equivalent Circuit Models of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 52
Author(s):  
Leo Wildfeuer ◽  
Philipp Gieler ◽  
Alexander Karger
Keyword(s):  
Lithium Ion Batteries ◽  
Frequency Domain ◽  
Time Domain ◽  
Partial Information ◽  
Measurement Data ◽  
Lithium Ion ◽  
Constant Current ◽  
Full Potential ◽  
Fitting Algorithm ◽  
Parameterized Model

ECM are a widely used modeling approach for lithium-ion batteries in engineering applications. The RC elements, which display the dynamic loss processes of the cell, are usually parameterized by fitting the ECM to experimental data in either the time-domain or the frequency-domain. However, both types of data have limitations with regard to the observable time constants of electrochemical processes. This work proposes a method to combine time-domain and frequency-domain measurement data for parameterization of RC elements by exploiting the full potential of the DRT. Instead of using only partial information from the DRT to supplement a conventional fitting algorithm, we determine the parameters of an arbitrary number of RC elements directly from the DRT. The difficulties of automated deconvolution of the DRT, including regularization and the choice of an optimal regularization factor, is tackled by using the L-curve criterion for optimized calculation of the DRT via Tikhonov regularization. Three different approaches to merge time- and frequency-domain data are presented, including a novel approach where the DRT is simultaneously calculated from EIS and pulse relaxation measurements. The parameterized model for a commercial 18650 NCA cell was validated during a validation cycle consisting of constant current and real-world automotive cycling and yields a relative improvement of over 40 % compared to a conventional EIS-fitting algorithm.

Sign in / Sign up

Export Citation Format

Share Document