Search Results

You are looking at 1 - 10 of 77 items for :

  • "regularization" x
  • Refine by Access: All Content x
Clear All

Abstract  

Regularization method in the reconstruction of an infinite monoenergetic radiation field scanned by a directional detector is considered in the paper. A comparison of the reconstructions with an without regularization is given and the principles for using the regularization method are presented.

Restricted access

Abstract  

To find presumed regularities by means of analysing observations on physical phenomena is a problem which frequently arises in calculation practice. An attempt is made to provide a general mathematical formulation of this problem and to connect its occurrence to an effective research strategy. A general calculational approach to the analysis of latent regularities, based on regularized and autoregularized iteration processes for solving non-linear problems, is suggested. Some particular classes of problems are discussed and actual numerical examples are given.

Restricted access

The Polar Regions are not covered by satellite gravity gradiometry data if the orbital inclination of the satellite is not equal to 90°. This paper investigates the feasibility of determining gravity anomaly (at sea level) by inversion of satellite gravity gradiometry data in these regions. Inversion of each element of tensor of gravitation as well as their joint inversion are investigated. Numerical studies show that gravity anomaly can be recovered with an error of 3 mGal in the north polar gap and 5 mGal in south polar gaps in the presence of 1 mE white noise in the satellite data. These errors can be reduced to 1 mGal and 3 mGal, respectively, by removing the regularization bias from the recovered gravity anomalies.

Restricted access
Restricted access

In this paper, we study the conjugate Gradient iterative method applied on a discrete Stokes problem obtain by adding in the second equation a stabilising term depending on a parameter a. We also establish the convergence rate as a function of a.

Restricted access

complexity of these models raises concerns about their inclination to overfit the training data, impeding their adaptability to unseen data instances [ 2 ]. Regularization techniques are pivotal in mitigating this challenge, encouraging model generalization

Open access

An ill-posed problem which involves heterogonous data can yield good results if the weight of observations is properly introduced into the adjustment model. Variance component estimation can be used in this respect to update and improve the weights based on the results of the adjustment. The variance component estimation will not be as simple as that is in an ordinary adjustment problem, because the result of the solution of an ill-posed problem contains a bias due to stabilizing the adjustment model. This paper investigates the variance component estimation in those ill-posed problems solved by the truncation singular value decomposition. The biases of the variance components are analyzed and the biased-corrected and the biased-corrected non-negative estimators of the variance components are developed. The derivations show that in order to estimate unbiased variance components, it suffices to estimate and remove the bias from the estimated residuals.

Restricted access

‘How effective is the path that leads them to administrative regularization?’ This work aims to verify the extent to which existing legislation effectively facilitates the legal exit of unaccompanied migrant minors from the protection system. The basic

Full access