After the initial measurement, for every target, we first establish the search space based on the corresponding measured target; then, we optimize the measured target by our proposed ELPSO algorithm to achieve the optimal localization, which is represented by the red pentagonal stars.
After modeling, we optimize the measured target by our proposed ELPSO algorithm to achieve the optimal localization, which is represented by the red dots.
For this, a modified kinematic model of the track geometry involving transition and circular curves is proposed based on the tangential as well as normal acceleration, and both a nonlinear Extended Kalman Filter (EKF) and an augmented Unscented Kalman Filter (UKF) are applied to optimize the measured data.
In order to conquer the computational burden of SA procedure, we adopt a spectral fc-means method for optimizing the measure and apply it to a yeast PPI network.
Firstly, aim to solve the large scale, high precision and longtime stability measurement system issues, a measurement network based on laser tracker metrology was established, key technology for the metrology including: deducing the error in point measurement and uncertainty ellipsoid of laser tracker; optimizing the measuring stations and carrying out weighted measurement network regression.
Surprisingly, even though the MWER algorithm does not directly optimize the MEC measure, it produces the best haplotypes in respect to this measure for all but two chromosomes.
To optimize the control measure, a series of experiments have been conducted.
The proposed method is employed to optimize the quadratic measure of ideal characteristics of the prototype filter and filter bank at quadrature frequency.
We then propose several co-fix-aware prioritization approaches to dynamically optimize the new measure, based on the historical defect co-fixes.
Robust control architecture was used to optimize the fidelity measure with constraints on the free motion tracking requirements and robust stability of the system under uncertainties of the soft environment.
