Prediction of modulus of elasticity of UHPC using maximum likelihood estimation method

Structures ◽  
2021 ◽  
Author(s):  
Ali Alsalman ◽  
Rahman Kareem ◽  
Canh N. Dang ◽  
José R. Martí-Vargas ◽  
W. Micah Hale
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Modulus Of Elasticity ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimation Method

scholarly journals A Stochastic Model of Stress Evolution in a Bolted Structure in the Presence of a Joint Elastic Piece: Modeling and Parameter Inference

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mohammed Haiek ◽  
Youness El Ansari ◽  
Nabil Ben Said Amrani ◽  
Driss Sarsri
Keyword(s):  
Numerical Simulation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Stress Evolution ◽  
Parameter Inference ◽  
Maximum Likelihood Estimation Method ◽  
Over Time

In this paper, we propose a stochastic model to describe over time the evolution of stress in a bolted mechanical structure depending on different thicknesses of a joint elastic piece. First, the studied structure and the experiment numerical simulation are presented. Next, we validate statistically our proposed stochastic model, and we use the maximum likelihood estimation method based on Euler–Maruyama scheme to estimate the parameters of this model. Thereafter, we use the estimated model to compare the stresses, the peak times, and extinction times for different thicknesses of the elastic piece. Some numerical simulations are carried out to illustrate different results.

scholarly journals Calibration of MEMS Triaxial Accelerometers Based on the Maximum Likelihood Estimation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yifan Sun ◽  
Xiang Xu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Random Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Least Square ◽  
Triaxial Accelerometer ◽  
Zero Bias ◽  
Estimation Function ◽  
Maximum Likelihood Estimation Method

As a widely used inertial device, a MEMS triaxial accelerometer has zero-bias error, nonorthogonal error, and scale-factor error due to technical defects. Raw readings without calibration might seriously affect the accuracy of inertial navigation system. Therefore, it is necessary to conduct calibration processing before using a MEMS triaxial accelerometer. This paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. The error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. The calibration parameters are obtained by the Newton iteration method, which is more efficient and accurate. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. Moreover, the proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. The proposed method has the potential to be applied to other triaxial inertial sensors.

scholarly journals A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps

2016 ◽  
Vol 11 (5) ◽  
pp. 913-920 ◽  
Author(s):  
P. V. Sudeep ◽  
P. Palanisamy ◽  
Chandrasekharan Kesavadas ◽  
Jan Sijbers ◽  
Arnold J. den Dekker ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Magnetic Resonance ◽  
Maximum Likelihood Estimation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimation Method
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