Weighting factors for radiation quality: how to unite the two current concepts

2004 ◽  
Vol 110 (1-4) ◽  
pp. 781-787 ◽  
Author(s):  
A. M. Kellerer
Keyword(s):  
Radiation Quality ◽  
Weighting Factors

Restricted LET Remains a Good Parameter of Radiation Quality

1988 ◽  
Vol 23 (1-4) ◽  
pp. 79-82
Author(s):  
D. Harder ◽  
R. Blohm ◽  
M. Kessler
Keyword(s):  
Radiation Quality ◽  
Good Parameter

Calibration of a Radiation Quality Model for Sparse and Uncertain Data

2021 ◽  
Author(s):  
Luis G. Crespo ◽  
Tony C. Slaba ◽  
Sean P. Kenny ◽  
Mathew W. Swinney ◽  
Daniel P. Giesy
Keyword(s):  
Uncertain Data ◽  
Quality Model ◽  
Radiation Quality

Radiation quality control in diagnostic x-ray equipment

1983 ◽  
Vol 17 (5) ◽  
pp. 175-177
Author(s):  
N. N. Blinov ◽  
G. D. Gromov ◽  
N. M. Kuskova ◽  
T. A. Popova ◽  
V. V. Popov
Keyword(s):  
Quality Control ◽  
Radiation Quality ◽  
X Ray

Velocity control algorithm in glass polishing based on the cubic NURBS curve

2017 ◽  
Vol 232 (4) ◽  
pp. 685-696 ◽  
Author(s):  
Junzhao Han ◽  
Wenhua Chen
Keyword(s):  
Control Algorithm ◽  
Linear Acceleration ◽  
Control Points ◽  
Velocity Control ◽  
Polishing Process ◽  
Nurbs Curve ◽  
Weighting Factors ◽  
Final Velocity ◽  
Contact Points ◽  
Glass Polishing

To limit velocity fluctuations and to achieve a controllable jerk value in a glass polishing process, a new velocity control algorithm is proposed based on nonuniform rational B-splines (NURBS). The key of this algorithm is replacing the traditional linear acceleration–deceleration with flexible NURBS acceleration–deceleration. Based on the linear acceleration–deceleration algorithm, the control points of the NURBS curve are confirmed, and the final velocity of the polishing wheel center is solved using the Preston equation. With jerk continuity and limitations of the servo system, nonlinear equations are constructed, and the weighting factors corresponding to the control points are obtained. Cubic velocity control equations can be derived from the obtained feature parameters, which include the final velocity, control points, weighting factors and knot vectors. Based on the proposed NURBS acceleration–deceleration algorithm, a fourth-order Runge–Kutta formula was used to obtain the initial points, and the Milne–Hamming equation was used to predict and correct the next point. The predictor-corrector interpolation algorithm for parametric trajectory was implemented during the polishing process. The experimental results indicate that the proposed approach guarantees limited fluctuations of the relative velocity at contact points and ensure smoother velocity changes at dangerous points.

scholarly journals What happens when data are fitted to the wrong equation?

1978 ◽  
Vol 171 (3) ◽  
pp. 513-517 ◽  
Author(s):  
K J Ellis ◽  
R G Duggleby
Keyword(s):  
Data Analysis ◽  
Random Errors ◽  
Mathematical Equation ◽  
Error Structure ◽  
Weighting Factors ◽  
The Difference ◽  
Residual Plots

In many problems of data analysis it is necessary to fit the data to a mathematical equation. Random errors of measurement will be responsible for deviations between the data and the equation, but superimposed on this there may be deviations that result from the equation being an inadequate description of the system from which the data were obtained. Plots of the residual (i.e. the difference between the experimental and calculated values of the dependent variable) against each of the experimental variables have been previously used to detect a misfit between the data and the equation. In the present paper, we show that the shape of the residual plots may be used as a guide in choosing a more appropriate equation. In addition, residual plots give useful information on the error structure of the data, and hence the weighting factors that should be used in the analysis.

Sign in / Sign up

Export Citation Format

Share Document