Figure 1.
Bases for q-Bézier triangular.
Figure 1.
Bases for q-Bézier triangular.
Figure 2.
Cubic q-Bézier triangular with different values of q.
Figure 2.
Cubic q-Bézier triangular with different values of q.
Figure 3.
Control points for cubic q-Bézier triangular patch.
Figure 3.
Control points for cubic q-Bézier triangular patch.
Figure 4.
Directionals , , and .
Figure 4.
Directionals , , and .
Figure 5.
Two adjacent q-Bézier triangular patches.
Figure 5.
Two adjacent q-Bézier triangular patches.
Figure 6.
Triangulation domain for (a) 36 data points, (b) 65 data points, and (c) 100 data points.
Figure 6.
Triangulation domain for (a) 36 data points, (b) 65 data points, and (c) 100 data points.
Figure 7.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 7.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 8.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 8.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 9.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 9.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 10.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 10.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 11.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 11.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 12.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 12.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 13.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 13.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 14.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 14.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 15.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 15.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 16.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 16.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 17.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 17.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 18.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 18.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 19.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 19.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 20.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 20.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 21.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 21.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 22.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 22.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 23.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 23.
Surface interpolation based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 24.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 24.
Contour plots based on function with 36, 65, and 100 data points when : (a) true surface; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 25.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 25.
Surface interpolation for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 26.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 26.
Contour plots for 36 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 27.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 27.
Surface interpolation for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 28.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 28.
Contour plots for 65 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 29.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 29.
Surface interpolation for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 30.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 30.
Contour plots for 100 data points based on function. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 31.
Example for (a) true surface, (b) 3D interpolation, and (c) Delaunay triangulation of Seamount data points.
Figure 31.
Example for (a) true surface, (b) 3D interpolation, and (c) Delaunay triangulation of Seamount data points.
Figure 32.
Surface interpolation with different q values based on Seamount data points. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 32.
Surface interpolation with different q values based on Seamount data points. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 33.
Three-dimensional interpolation with different q values based on Seamount data points. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 33.
Three-dimensional interpolation with different q values based on Seamount data points. (a) q = 0.25. (b) q = 0.5. (c) q = 0.75. (d) q = 1.
Figure 34.
Example of electric potential of two point charges (Adopted from [
38]).
Figure 34.
Example of electric potential of two point charges (Adopted from [
38]).
Figure 35.
Delaunay triangulation of 25 data points (adopted from [
39]).
Figure 35.
Delaunay triangulation of 25 data points (adopted from [
39]).
Figure 36.
Surface and contour plot for (
a) true function and (
b) Ali et al. [
39].
Figure 36.
Surface and contour plot for (
a) true function and (
b) Ali et al. [
39].
Figure 37.
Delaunay triangulation of 25, 35, 65, and 100 data points: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 37.
Delaunay triangulation of 25, 35, 65, and 100 data points: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 38.
Surface interpolation with 25, 36, 65, and 100 data points for q value of 1: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 38.
Surface interpolation with 25, 36, 65, and 100 data points for q value of 1: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 39.
Contour plot with 25, 36, 65, and 100 data points for q value of 1: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 39.
Contour plot with 25, 36, 65, and 100 data points for q value of 1: (a) 25 data points; (b) 36 data points; (c) 65 data points; (d) 100 data points.
Figure 40.
values for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Figure 40.
values for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Figure 41.
RMSE values for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Figure 41.
RMSE values for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Figure 42.
CPU time (in seconds) for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Figure 42.
CPU time (in seconds) for 36, 65, and 100 data points across three test functions, evaluated at four selected q values.
Table 1.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of 0.25 and 0.50.
Table 1.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of 0.25 and 0.50.
Function | Data Points | | RMSE | CPU Time (s) |
---|
Q 1 | Q 2 | Q 1 | Q 2 | Q 1 | Q 2 |
---|
| 36 | 0.9974 | 0.9974 | 0.0038 | 0.0037 | 0.0981 | 0.1734 |
| 65 | 0.9990 | 0.9991 | 0.0023 | 0.0023 | 0.2621 | 0.2743 |
| 100 | 0.9998 | 0.9998 | 0.0011 | 0.0010 | 0.4495 | 0.4800 |
| 36 | 0.9832 | 0.9832 | 0.0129 | 0.0129 | 0.1554 | 0.1819 |
| 65 | 0.9970 | 0.9971 | 0.0055 | 0.0053 | 0.2714 | 0.3210 |
| 100 | 0.9986 | 0.9987 | 0.0037 | 0.0036 | 0.5213 | 0.4021 |
| 36 | 0.9973 | 0.9975 | 0.0042 | 0.0040 | 0.1336 | 0.1562 |
| 65 | 0.9993 | 0.9994 | 0.0021 | 0.0020 | 0.2817 | 0.2079 |
| 100 | 0.9998 | 0.9999 | 0.0011 | 0.0010 | 0.3653 | 0.3178 |
Table 2.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of 0.75 and 1.00.
Table 2.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of 0.75 and 1.00.
Function | Data Points | | RMSE | CPU Time (s) |
---|
Q 3 | Q 4 | Q 3 | Q 4 | Q 3 | Q 4 |
---|
| 36 | 0.9976 | 0.9977 | 0.0036 | 0.0035 | 0.1966 | 0.1893 |
| 65 | 0.9991 | 0.9992 | 0.0022 | 0.0020 | 0.2424 | 0.2545 |
| 100 | 0.9999 | 0.9999 | 0.0009 | 0.0009 | 0.3431 | 0.2557 |
| 36 | 0.9831 | 0.9829 | 0.0129 | 0.0130 | 0.2106 | 0.1758 |
| 65 | 0.9973 | 0.9975 | 0.0051 | 0.0049 | 0.2550 | 0.1910 |
| 100 | 0.9987 | 0.9988 | 0.0035 | 0.0035 | 0.2885 | 0.2807 |
| 36 | 0.9977 | 0.9978 | 0.0038 | 0.0037 | 0.2145 | 0.1850 |
| 65 | 0.9995 | 0.9995 | 0.0019 | 0.0018 | 0.2301 | 0.2583 |
| 100 | 0.9999 | 0.9999 | 0.0008 | 0.0007 | 0.3059 | 0.3024 |
Table 3.
Overall error measurements for 36, 65, and 100 data points between the proposed method and Goodman and Said’s method for q-Bézier with value of 0.75.
Table 3.
Overall error measurements for 36, 65, and 100 data points between the proposed method and Goodman and Said’s method for q-Bézier with value of 0.75.
Function | Data Points | | RMSE | CPU Time (s) |
---|
PS 1 | GS 2 | PS 1 | GS 2 | PS 1 | GS 2 |
---|
| 36 | 0.9976 | 0.9976 | 0.0036 | 0.0036 | 0.1966 | 0.1670 |
| 65 | 0.9991 | 0.9992 | 0.0022 | 0.0021 | 0.2424 | 0.2045 |
| 100 | 0.9999 | 0.9999 | 0.0009 | 0.0008 | 0.3431 | 0.3071 |
| 36 | 0.9831 | 0.9824 | 0.0129 | 0.0132 | 0.2106 | 0.1640 |
| 65 | 0.9973 | 0.9973 | 0.0051 | 0.0052 | 0.2550 | 0.1768 |
| 100 | 0.9987 | 0.9987 | 0.0035 | 0.0036 | 0.2885 | 0.2968 |
| 36 | 0.9977 | 0.9977 | 0.0038 | 0.0038 | 0.2145 | 0.1746 |
| 65 | 0.9995 | 0.9994 | 0.0019 | 0.0019 | 0.2301 | 0.1817 |
| 100 | 0.9999 | 0.9999 | 0.0008 | 0.0007 | 0.3059 | 0.2988 |
Table 4.
Overall error measurements for 36, 65, and 100 data points between the proposed method and Goodman and Said’s method for q-Bézier with value of 1.00.
Table 4.
Overall error measurements for 36, 65, and 100 data points between the proposed method and Goodman and Said’s method for q-Bézier with value of 1.00.
Function | Data Points | | RMSE | CPU Time (s) |
---|
PS 1 | GS 2 | PS 1 | GS 2 | PS 1 | GS 2 |
---|
| 36 | 0.9977 | 0.9976 | 0.0035 | 0.0036 | 0.1893 | 0.1986 |
| 65 | 0.9992 | 0.9992 | 0.0020 | 0.0021 | 0.2166 | 0.2374 |
| 100 | 0.9999 | 0.9999 | 0.0009 | 0.0008 | 0.2557 | 0.3051 |
| 36 | 0.9829 | 0.9824 | 0.0130 | 0.0132 | 0.1758 | 0.1769 |
| 65 | 0.9975 | 0.9973 | 0.0049 | 0.0052 | 0.1910 | 0.2638 |
| 100 | 0.9988 | 0.9987 | 0.0035 | 0.0036 | 0.2807 | 0.3589 |
| 36 | 0.9978 | 0.9977 | 0.0037 | 0.0038 | 0.1373 | 0.2090 |
| 65 | 0.9995 | 0.9994 | 0.0018 | 0.0019 | 0.1570 | 0.2496 |
| 100 | 0.9999 | 0.9999 | 0.0007 | 0.0007 | 0.2078 | 0.4441 |
Table 5.
CPU time comparison between different q values.
Table 5.
CPU time comparison between different q values.
q Values | CPU Time (s) |
---|
First Run | Second Run | Third Run | Average |
---|
0.25 | 163.25 | 159.80 | 158.05 | 160.37 |
0.50 | 160.45 | 158.11 | 157.78 | 158.78 |
0.75 | 157.74 | 157.76 | 159.17 | 158.22 |
1.00 | 157.88 | 158.37 | 157.73 | 157.99 |
Table 6.
Overall error measurements for 25, 36, 65, and 100 data points for q-Bézier.
Table 6.
Overall error measurements for 25, 36, 65, and 100 data points for q-Bézier.
Method | Data Points | | RMSE | CPU Time (s) |
---|
Q 1 | Q 2 | Q 1 | Q 2 | Q 1 | Q 2 |
---|
Fatin | 25 | 0.8958 | 0.8934 | 1.9374 | 1.9592 | 0.1244 | 0.0989 |
Proposed | 25 | 0.8868 | 0.8871 | 2.0195 | 2.0166 | 0.0743 | 0.1184 |
Proposed | 36 | 0.9356 | 0.9356 | 1.5233 | 1.5225 | 0.1463 | 0.1746 |
Proposed | 65 | 0.9791 | 0.9789 | 0.8674 | 0.8709 | 0.1954 | 0.2669 |
Proposed | 100 | 0.9908 | 0.9907 | 0.5753 | 0.5777 | 0.2696 | 0.3106 |
Table 7.
Overall error measurements for 25, 36, 65, and 100 data points for q-Bézier.
Table 7.
Overall error measurements for 25, 36, 65, and 100 data points for q-Bézier.
Method | Data Points | | RMSE | CPU Time (s) |
---|
Q 3 | Q 4 | Q 3 | Q 4 | Q 3 | Q 4 |
---|
Fatin | 25 | 0.8901 | 0.8852 | 1.9898 | 2.0335 | 0.1654 | 0.1502 |
Proposed | 25 | 0.8881 | 0.8893 | 2.0079 | 1.9971 | 0.0715 | 0.0995 |
Proposed | 36 | 0.9361 | 0.9364 | 1.5173 | 1.5136 | 0.1160 | 0.1495 |
Proposed | 65 | 0.9786 | 0.9780 | 0.8776 | 0.8898 | 0.1889 | 0.1958 |
Proposed | 100 | 0.9905 | 0.9902 | 0.5836 | 0.5956 | 0.3125 | 0.2644 |
Table 8.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of from 0.90 to 1.00 with step size of 0.01.
Table 8.
Overall error measurements for 36, 65, and 100 data points for q-Bézier with values of from 0.90 to 1.00 with step size of 0.01.
q Value | Data Points |
---|
36 | 65 | 100 |
---|
| | | | | | | | |
---|
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9974 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.90 | 0.0036 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1449 | 0.1419 | 0.1487 | 0.1752 | 0.2267 | 0.2210 | 0.2256 | 0.3326 | 0.2341 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.91 | 0.0036 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1758 | 0.1598 | 0.1827 | 0.2049 | 0.2051 | 0.1908 | 0.3662 | 0.3654 | 0.3049 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.92 | 0.0036 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1895 | 0.1722 | 0.1731 | 0.2196 | 0.2047 | 0.1917 | 0.3483 | 0.3253 | 0.3222 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.93 | 0.0036 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1509 | 0.1966 | 0.2206 | 0.2154 | 0.2106 | 0.2609 | 0.3322 | 0.3873 | 0.3712 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.94 | 0.0036 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1748 | 0.1850 | 0.1643 | 0.2268 | 0.2256 | 0.1848 | 0.2829 | 0.3162 | 0.3039 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.95 | 0.0035 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1530 | 0.1695 | 0.1820 | 0.2018 | 0.2054 | 0.2528 | 0.3995 | 0.3927 | 0.3929 |
| 0.9977 | 0.9830 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.96 | 0.0035 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1670 | 0.1601 | 0.1737 | 0.1840 | 0.2091 | 0.2310 | 0.2172 | 0.3576 | 0.2682 |
| 0.9977 | 0.9829 | 0.9979 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.97 | 0.0035 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0008 | 0.0035 | 0.0007 |
| 0.1738 | 0.1344 | 0.1882 | 0.1901 | 0.2289 | 0.2058 | 0.2811 | 0.5423 | 0.3327 |
| 0.9977 | 0.9829 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.98 | 0.0035 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0009 | 0.0035 | 0.0007 |
| 0.1689 | 0.0850 | 0.1773 | 0.2088 | 0.2134 | 0.2185 | 0.3095 | 0.2941 | 0.6070 |
| 0.9977 | 0.9829 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
0.99 | 0.0035 | 0.0130 | 0.0037 | 0.0021 | 0.0050 | 0.0018 | 0.0009 | 0.0035 | 0.0007 |
| 0.1689 | 0.0852 | 0.1309 | 0.1898 | 0.1944 | 0.1814 | 0.2792 | 0.2571 | 0.2659 |
| 0.9977 | 0.9829 | 0.9978 | 0.9992 | 0.9975 | 0.9995 | 0.9999 | 0.9988 | 0.9999 |
1.00 | 0.0035 | 0.0130 | 0.0037 | 0.0020 | 0.0049 | 0.0018 | 0.0009 | 0.0035 | 0.0007 |
| 0.1893 | 0.1758 | 0.1850 | 0.2545 | 0.1910 | 0.2583 | 0.2557 | 0.2807 | 0.3024 |