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Each secular variation model consists of six statistically significant coefficients for each magnetic field component modelled.
A dimension variation model was developed based on thermal stress prediction with finite element analysis (FEA).
In this paper, we propose a novel total variation model based on exponential function (ETV).
Geometric variation model is introduced to infer key parts and key features, helping forming tolerance specification while assuring rationality and sufficiency of structure decomposition.
In this paper, we propose a hybrid variational model which takes advantages of the wavelet tight frame model and the total variation model for image restoration.
Using the FEA-based dimension variation model, deformations of typical composite structures were studied and regression-based dimension variation models were developed.
The total variation model has been considered to be one of the most successful and representative denoising models that can preserve edges well.
A secular variation model valid for 2010.0 2015.0, based on the extrapolation through exponential smoothing of observatory monthly mean values, is proposed as a predictive secular variation model.
Fig. 4. Power spectra of the degree 8 IGRF-10 predictive secular variation model (solid) and of the degree 13 EIGRF secular variation model (dashes).
The obtained secular variation model is compared with models based on CHAMP satellite data.
We also provided SV predictions to degree and order 13 as a test secular variation model.
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