Uncertainty analysis of solder alloy material parameters estimation based on model calibration method

2012 ◽  
Vol 52 (6) ◽  
pp. 1128-1137 ◽  
Author(s):  
Jin Hyuk Gang ◽  
Dawn An ◽  
Jin Won Joo ◽  
Joo Ho Choi
Author(s):  
J. Sebastian Hernandez-Suarez ◽  
A. Pouyan Nejadhashemi ◽  
Kalyanmoy Deb

2007 ◽  
Vol 78 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Christina Tonitto ◽  
Mark B. David ◽  
Laurie E. Drinkwater ◽  
Changsheng Li

2010 ◽  
Vol 22 (7) ◽  
pp. 1571-1576
Author(s):  
杨正华 Yang Zhenghua ◽  
陈伯伦 Chen Bolun ◽  
曹柱荣 Cao Zhurong ◽  
董建军 Dong Jianjun ◽  
侯立飞 Hou Lifei ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2841
Author(s):  
Mohammad Ali Zaiter ◽  
Régis Lherbier ◽  
Ghaleb Faour ◽  
Oussama Bazzi ◽  
Jean-Charles Noyer

This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR’s orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance.


2011 ◽  
Vol 230-232 ◽  
pp. 723-727 ◽  
Author(s):  
Bao Feng Zhang ◽  
Xiu Zhen Tian ◽  
Xiao Ling Zhang

In order to simplify previous camera calibration method, this paper put forward an easy camera calibration method based on plane grid points on the foundation of Heikkila plane model calibration method. Intrinsic and extrinsic parameters of the camera are calibrated with MATLAB, then the rotation matrix and the translation vector are calculated. The experiment results show this method is not only simple in practice, but also can meet the needs of computer vision systems.


2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Yousheng Chen ◽  
Andreas Linderholt ◽  
Thomas J. S. Abrahamsson

Correlation and calibration using test data are natural ingredients in the process of validating computational models. Model calibration for the important subclass of nonlinear systems which consists of structures dominated by linear behavior with the presence of local nonlinear effects is studied in this work. The experimental validation of a nonlinear model calibration method is conducted using a replica of the École Centrale de Lyon (ECL) nonlinear benchmark test setup. The calibration method is based on the selection of uncertain model parameters and the data that form the calibration metric together with an efficient optimization routine. The parameterization is chosen so that the expected covariances of the parameter estimates are made small. To obtain informative data, the excitation force is designed to be multisinusoidal and the resulting steady-state multiharmonic frequency response data are measured. To shorten the optimization time, plausible starting seed candidates are selected using the Latin hypercube sampling method. The candidate parameter set giving the smallest deviation to the test data is used as a starting point for an iterative search for a calibration solution. The model calibration is conducted by minimizing the deviations between the measured steady-state multiharmonic frequency response data and the analytical counterparts that are calculated using the multiharmonic balance method. The resulting calibrated model's output corresponds well with the measured responses.


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