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The improved parameter estimation method makes our MRF methods more stable and more reliable.
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Compared with some other methods, NSFD method is more stable [1, 3, 5].
Whilst not as accurate as the maximum likelihood estimation method, our simpler method is more stable for many of our plots, providing both the intercept and slope indicators of population structure [82].
Whilst not as accurate as the maximum likelihood estimation method, our simpler method is more stable for many of our plots, providing both the intercept and slope indicators of population structure [ 82].
Therefore, the localization performance of the two methods gets more stable with the larger number of the snapshots.
At contrast, the conservative finite difference methods are more stable in nonlinear phase and the Hermite WENO reconstruction avoids spurious oscillations.
The new methods become more stable and accurate when a sub-stepping procedure is adopted to obtain the Green's and step response matrices and their time derivatives at the end of the time step.
Because the results on LBPHF method are more stable among these compared methods, we also calculated the recognition rate of different radius and different neighbors fusion features LBPHF8,1 + 16,2 + 24,3 which reaches at 71.77%.
The DPPC vesicles with the new method were more stable than those with the other method.
That is, the prognostic method is more stable and precise than the traditional method for incomplete trial information.
Numerical simulations demonstrate that the proposed method is more stable and computationally efficient than other semi-implicit methods, preserving temporal second-order accuracy.
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