Modal-based nonlinear optimization algorithm for wavefront measurement with under-sampled data

2020 ◽  
Vol 45 (19) ◽  
pp. 5456
Author(s):  
Lei Zhao ◽  
Jian Bai ◽  
Yan Hao ◽  
Hou Jing ◽  
Chen Wang ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Sampled Data ◽  
Wavefront Measurement ◽  
Nonlinear Optimization Algorithm

Task dependent synthesis of some geometrical parameters of a robot mechanism

Robotica ◽  
1993 ◽  
Vol 11 (2) ◽  
pp. 167-171 ◽  
Author(s):  
Maks Oblak ◽  
Karl Gotlih
Keyword(s):  
Mathematical Model ◽  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Kinematic Chain ◽  
Chain Structure ◽  
Geometrical Parameters ◽  
Task Space ◽  
End Effector ◽  
Optimal Mechanism ◽  
Nonlinear Optimization Algorithm

SUMMARYThis paper deals with the synthesis of a robot mechanism, which has an open kinematic chain structure. The aim of the synthesis is to find optimal mechanism link lengths and the elevation of the robot mechanism base, with respect to the arbitrary chosen task which is described in a task space.A mathematical model, which describes the problem and enables one to use a nonlinear optimization algorithm, was developed. The usefulness of the approach is demonstrated by the example of the Manutec r3 mechanism with a prescribed task for the robot's end-effector.

Nonlinear Optimization Algorithm for Multivariate Optical Element Design

2002 ◽  
Vol 56 (4) ◽  
pp. 477-487 ◽  
Author(s):  
Olusola O. Soyemi ◽  
Frederick G. Haibach ◽  
Paul J. Gemperline ◽  
Michael L. Myrick
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optical Computing ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optical Element ◽  
Optical Elements ◽  
Design Algorithm ◽  
Nonlinear Optimization Algorithm

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

A nonlinear optimization algorithm for WindSat wind vector retrievals

2006 ◽  
Vol 44 (3) ◽  
pp. 597-610 ◽  
Author(s):  
M.H. Bettenhausen ◽  
C.K. Smith ◽  
R.M. Bevilacqua ◽  
Nai-Yu Wang ◽  
P.W. Gaiser ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Wind Vector ◽  
Nonlinear Optimization Algorithm
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