Business Scenario Evaluation Method Using Monte Carlo Simulation on Qualitative and Quantitative Hybrid Model

2008 ◽  
Vol 128 (4) ◽  
pp. 656-664 ◽  
Author(s):  
Masaki Samejima ◽  
Masanori Akiyoshi ◽  
Koshichiro Mitsukuni ◽  
Norihisa Komoda
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Hybrid Model ◽  
Evaluation Method ◽  
Qualitative And Quantitative ◽  
Scenario Evaluation

Business scenario evaluation using Monte Carlo simulation on qualitative and quantitative hybrid model

2010 ◽  
Vol 170 (3) ◽  
pp. 9-18 ◽  
Author(s):  
Masaki Samejima ◽  
Masanori Akiyoshi ◽  
Koshichiro Mitsukuni ◽  
Norihisa Komoda
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Hybrid Model ◽  
Qualitative And Quantitative ◽  
Scenario Evaluation

Secondary Fluorescence of 3D Heterogeneous Materials Using a Hybrid Model

2020 ◽  
Vol 26 (3) ◽  
pp. 484-496
Author(s):  
Yu Yuan ◽  
Hendrix Demers ◽  
Xianglong Wang ◽  
Raynald Gauvin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Model ◽  
Hybrid Model ◽  
Three Dimensional ◽  
Heterogeneous Materials ◽  
X Ray ◽  
The Monte Carlo Method ◽  
Secondary Fluorescence ◽  
Good Agreement

AbstractIn electron probe microanalysis or scanning electron microscopy, the Monte Carlo method is widely used for modeling electron transport within specimens and calculating X-ray spectra. For an accurate simulation, the calculation of secondary fluorescence (SF) is necessary, especially for samples with complex geometries. In this study, we developed a program, using a hybrid model that combines the Monte Carlo simulation with an analytical model, to perform SF correction for three-dimensional (3D) heterogeneous materials. The Monte Carlo simulation is performed using MC X-ray, a Monte Carlo program, to obtain the 3D primary X-ray distribution, which becomes the input of the analytical model. The voxel-based calculation of MC X-ray enables the model to be applicable to arbitrary samples. We demonstrate the derivation of the analytical model in detail and present the 3D X-ray distributions for both primary and secondary fluorescence to illustrate the capability of our program. Examples for non-diffusion couples and spherical inclusions inside matrices are shown. The results of our program are compared with experimental data from references and with results from other Monte Carlo codes. They are found to be in good agreement.

Hybrid model of evaluation of underground lead—zinc mine capacity expansion project using Monte Carlo simulation and fuzzy numbers

SIMULATION ◽  
2011 ◽  
Vol 87 (8) ◽  
pp. 726-742 ◽  
Author(s):  
Zoran Gligoric ◽  
Cedomir Beljic ◽  
Branko Gluscevic ◽  
Sasa Jovanovic
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Hybrid Model ◽  
Fuzzy Numbers ◽  
Capacity Expansion ◽  
Lead Zinc ◽  
Zinc Mine

An efficient Monte Carlo simulation for new uncertain Heston–CIR hybrid model

2021 ◽  
Author(s):  
Behrouz Fathi-Vajargah ◽  
Mohammad Mirzazadeh ◽  
Sara Ghasemalipour
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Hybrid Model

Analysis of Uncertainty in Standard Metal Tank Volume Verification with Monte Carlo Method

2013 ◽  
Vol 712-715 ◽  
pp. 1974-1978
Author(s):  
Jin Hai Li ◽  
Ru Song Tong ◽  
Suo Sheng Cao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Evaluation Method ◽  
Simulation Method ◽  
Comparison Method ◽  
Monte Carlo Simulation Method ◽  
Analog Simulation ◽  
Volume Comparison ◽  
Evaluation Result

The uncertainty in standard metal tank volume value verification is evaluated by using Monte Carlo Method. For second standard metal tank, verification is conducted with volume comparison method. A mathematical model of second standard metal tank volume measurement is established by using first standard metal tank to measure second standard metal tank. An analog simulation is conducted for verification value by using Monte Carlo simulation method, and hence the uncertainty in volume verification is obtained. Through comparison between evaluation result and traditional uncertainty propagation evaluation method, the result indicates that the difference cannot be ignored and the evaluation result by using Monte Carlo Simulation Method is more reliable.

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