Once the instance was solved, the third screen (the "results", Fig. 9C) showed the solution's deviation from the optimum as a percentage.
When comparing the best out of five random runs to the optimal solutions, the average deviation reduces to 0.05% (0.06%).
Thus, to examine the RLS-MERRY algorithms convergence to the optimal solution, the mean square deviation (MSD) performance metric is used.
The deviating solutions are classified according to the deviation type, thus whether the solution constitutes a different tautomer, protonation state, or redox form.
In addition, the consistent smaller (finer) navigation solution standard deviation in the 7th iteration compared to the initial navigation solution verifies the positive influences of VCE effort on IMU/GNSS KF, which is due to the scaled-down Q and R from the conservatively large initial values.
Compared to the results obtained from NPs in solution (Fig. 2(c)), the deviation from shot-noise expectation is significantly smaller due to the heat confinement to the cells, which effectively reduces the photothermal signal residue at the area used for noise calculation.
However, the deviation regarding to solution C is much higher, which demonstrates that this solution cannot adjust all the wind samples well.
To solve this problem, intuitively, one can obtain the expression of the solution to equations (3) and (4) and then evaluate the unknown boundary conditions to minimize the deviation between the solution and the actual observed values.
If the deviation between the solution based on parameters containing errors and the true optimal (but unavailable) solution based on error-free parameters is significant, the following decision-making could be meaningless.
Approximate solutions are then obtained for the deviation of sphere trajectories from fluid stream lines and the results compared with laboratory model experiments in a low Reynolds number settling tank.
