KEYWORDS: Statistical modeling, Monte Carlo methods, Statistical analysis, Measurement uncertainty, Data modeling, Matrices, Linear regression, Engineering, Data acquisition
In response to the problem of solving multiple linear regression measurement model in the case of small sample calibration test data, a method of parameter estimation for regression coefficient of measurement model based on Monte Carlo sampling idea and uncertainty evaluation theory is presented. In detail, the general form of multidimensional linear regression measurement model is introduced, and the principle and evaluation process based on Monte Carlo method (MCM) of solving the estimates and their standard deviations of the regression coefficient in measurement model are put forward. The optimization scheme for Monte Carlo sampling is further given, which can be adopted to ensure the reliability of parameter estimation under small sample condition. Finally, the feasibility and practicability of the proposed method are verified by a simulation example. In the case of small sample test data, this method can not only estimate the estimated values of each parameter in the multidimensional measurement model, but also evaluate the corresponding standard deviation. It can obtain a more scientific, reasonable and accurate measurement model, which provides a reliable model guarantee for practical measurement and evaluation applications.
In order to eliminate the asymmetric projection error, this paper proposes a method to calculate projection point of circle center by use of concentric circles. Firstly, the basic principles and mathematical model are introduced briefly. Next, take arbitrary secants on a circle, the relationship between the secant midpoint and the projection point of the infinity point on the imaging plane is obtained based on the properties of projective geometry. The equations which contain parameters of secant midpoints are established according to the geometric constraints. Then the coordinate of projection point of circle center is derived from the coordinates of secant midpoints. Finally, in order to eliminate the algorithm error in the calculation process, the coordinates of all solved projection points are averaged to get the final coordinates. The simulation results show that the method can get the projection point of circle center accurately and stably, and does not have intrinsic projection error. Besides, the accuracy of the proposed method is 1.71nm higher than that of the direct ellipse fitting method.
This paper mainly focuses on the sphericity evaluation based on the minimum zone sphere (MZS) method in the Cartesian coordinate system. An asymptotic search method is proposed to search for the homocentric centre of MZS model and calculate the sphericity error. The search process of the proposed method consists two parts: geometric area search is implemented to obtain a quasi-MZS centre (close to the MZS centre) and 3+2 and 2+3 mathematical models dominating the minimum zone sphere are solved to obtain the MZS centre. The geometric area search is employed to fast convergence to the quasi-MZS centre by constructing a search sphere model. Some characteristic points distributed on the search sphere are selected to determine the search direction. A threshold is set to terminate the search process and the quasi-MZS centre is determined as a result. The quasi-MZS centre is employed as a reference centre to solve the 3+2 and 2+3 models to determine the MZS centre. According to the minimum conditions, the mathematical models are established to solve the two models. Then the judgment is implemented to ensure all the measured points are enveloped between the two homocentric spheres. As a result, the centre of two homocentric spheres is the MZS centre. The MZS sphericity error can be obtained as well. To verify the performance of the proposed method, simulation experiments and comparison experiments are implemented. The results demonstrated that the proposed method is effective, reliable and meet the requirement of sphericity evaluation.
Thermoelastic damping is one of the key factors affecting the quality factor of vacuum-encapsulated resonant devices. In order to suppress the influence of thermoelastic damping, vertical slots are introduced to the MEMS resonant beams. The heat flux inside the beams are expected to be reduced through local structural optimization, thus improving the quality factor. To verify the feasibility of the proposed method, the software of COMSOL is employed to explore the inhibition effect of structural parameters such as length, width and quantity of the slots on thermoelastic damping. The simulation results show that the thermoelastic damping decreases sharply and the quality factor are improved after the slots are introduced, and the effects are strongly related to the characteristic parameters of the slots.
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