Although we cannot see any subjective differences among relit images for (I), (II), and (III), quantitatively, the errors become smaller when we employed L1 and the method (I) which uses both PC and L1 gives the best scores for near range.
Soon the errors become the foundation upon which learning grows.
When N was large enough, with appropriate ratio M/K and essential W E, the errors became quite small, enabling broadening of the range of feasible window width.
The errors became smaller as the number of observations increased, which was consistent with the results in Figure 1.
As shown, the Walsh Towler algorithm consistently underestimates Bo and Rs, and the error becomes more significant at lower pressure.
Immediately after the beginning of the experiment, the error was greater than 0.6 mm; however, after a few seconds, the nozzle followed the desired trajectory well and the error became less than 0.1 mm.
As the peak counts in Fig. 9(d) decrease from evening toward midnight, the error bars (random errors) of the wind velocities and the fitting errors become large.
In 1999 (and subsequently), the error rate and magnitude of the error becomes considerably larger.
In the second peak, both ARVUE and CRAMP showed similar results, where the error becomes as low as 0.25.
As in Figures 2 and 3, we can conclude that the regularized solution converges to the exact one as the error becomes smaller and smaller.
