Simulations and experiments show that the proposed linear calibration method can accurately solve the detailed error parameters and decrease measurement errors of magnetic gradient tensor system remarkably.
Based on the detailed analysis of systematic errors, mathematical model of error parameters is constructed and linear calibration method is proposed for magnetic gradient tensor system.
Lastly, it discusses recent advances in the field (figures of merit, detection limit, non-linear calibration, method comparison), together with modern literature references.
It should be noted that curved effects can be modeled by linear multivariate calibration methods such as PLS, but these necessitate the retention of a larger number of factors, with the subsequent risk of inclusion of irrelevant sources of variance in the model [ 29].
The analytical uncertainty of these procedures is typically ± 3 5% with a weighted linear regression calibration method.
We used the linear regression calibration method to adjust for measurement error.
The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data.
Three calibration methods, linear and cubic regression as well as probit analysis, were applied and compared based on three statistical criteria.
Comparatively, the initial camera parameters acquired through linear calibration are optimized through two different methods: one is the conventional method and the other is the novel method presented by this paper.
This strategy has been produced for the pharmaceutical applications and dedicated to the preliminary evaluation of analytical methods where linear calibration model, which is very often occurred in practice, could be the most appropriate to fit experimental data.
The TOT dependence on the energy is not linear and a calibration method is implemented in the ASIC.
