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Table 3 Coefficients of Variation of the Mean Cross-Sectional Maximum Curvature and Correlation Coefficient of the Cross-Sectional Maximum Curvature Function
2002
"... In PAGE 8: ... As an example, the same COV and correlation coefficient statistics were collected for the cross-sectional maximum curvature function C(s). Table3 shows that, in terms of mean reproducibility, the results are slightly better than the area measurements. The scan-rescan experiment shows a COV equal to 1.... ..."
Cited by 1
TABLE II OUTLINE FOR TRANSMISSION INCREMENTAL OPTIMIZATION TRANSFER (TRIOT) ALGORITHM USING MAXIMUM CURVATURE (MC).
2004
Cited by 8
Table 7 Curvature radii of both curves (mm) before and after manual preparation; arc lengths (mm) to point of maximum curvature before and after manual preparation
Table 8 Curvature radii of both curves (mm) before and after rotary preparation; arc lengths (mm) to point of maximum curvature before and after rotary preparation
Table 1: Discretization errors in the convolution integral, taken at the point of maximum error in the curvature approximation along a unit circle ( = 0:2).
1998
"... In PAGE 7: ...cales). Growth of these singularities is even more severe for non-monotonic kernels. Any numerical approximation to the convolution of second order and higher derivatives, therefore, will require many discrete points within jxj lt; to accurately approximate to the convolution. Table1 provides evidence for this tendency, where the error between the exact convolution integral of @2K8=@x2 with f and @2K8=@y2 with f and our numerical approximations to these quantities is quite large, even with many discrete points within . 3 DETERMINING THE INTERFACE TOPOLOGY It is di cult to obtain high-order accurate approximations to n and when the interface is represented by a color function with a steep transition region.... In PAGE 8: ... The divergence in (3) can also be approximated by convolving f with the second derivatives of the kernel as was done in [AP95]. However, as shown in Table1 , the relative error asso- ciated with discretizing the convolution of the kernel second derivatives with f can be an order of magnitude larger than the relative error associated with discretizing the convolution of the kernel rst derivatives with f. Furthermore, at least for the case shown in Table 1,... ..."
Cited by 3
Table 5. Maximum sandwich core depth changes for different curvature angles q .
1999
"... In PAGE 13: ...core depth overcomes the effect of its thermal expansion. Table5 lists the numerical values of used in plotting figure 13. Figure 14 shows the distribution of the sandwich core depth changes D h (positive for stretching) for the simply supported case in the l-q space, where l is defined in figure 1.... ..."
Cited by 1
Table 1 Comparison of max. curvatures according to fitting methods
"... In PAGE 3: ... The figure shows that the Modified B-spline is the smoothest curve among them. The maximum curvature of the Modified B-spline is the smallest as shown in Table1 . From the curvature analysis using the CATIA (Dassalt Systems, France), the Modified B-spline approximation generates the... ..."
Table 1. Cascade performance of LLM networks for the estimation of the curvature maximum on training and test set. Normalized error after 5 cascade steps and cascade e ect (in %).
TABLE I Algorithm outline for a paraboloidal surrogates algorithm with coordinate descent (PSCD). The curvature choice shown here is the maximum second derivative.
1999
Cited by 38
Table 1. Main optical and physical parameters of the DM: centers and radii of curvature at vertex, maximum, minimum, peak-to-valley (ptv), mean, rms, and standard deviation (std) of thickness, and mass.
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