Assessment of individual bruising dynamics by pulsed photothermal radiometry and inverse Monte Carlo analysis

2017 ◽  
Author(s):  
Ana Marin ◽  
Nina Verdel ◽  
Luka Vidovič ◽  
Matija Milanič ◽  
Boris Majaron
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Photothermal Radiometry ◽  
Inverse Monte Carlo ◽  
Pulsed Photothermal Radiometry

scholarly journals Utilizing nuclear data in delayed gamma-ray spectroscopy inverse Monte Carlo analysis

2020 ◽  
Vol 239 ◽  
pp. 17005
Author(s):  
Douglas Chase Rodriguez ◽  
Kamel Abbas ◽  
Jean-Michel Crochemore ◽  
Mitsuo Koizumi ◽  
Stefan Nonneman ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Gamma Ray ◽  
Collaborative Work ◽  
Product Yield ◽  
Nuclear Data ◽  
Monte Carlo Analysis ◽  
Nuclear Material ◽  
Current Status ◽  
Inverse Monte Carlo ◽  
Gamma Ray Spectroscopy

Safeguards verification of uranium and plutonium in high-radioactivity nuclear material is currently performed using destructive analysis techniques. However, the preparation method is a burden on both the safeguards inspectors and facility operators. While nondestructive assay (NDA) techniques would improve the efficiency and time, there are no passive NDA techniques available to directly verify the U and Pu content. As an alternative, the JAEA and JRC are collaboratively developing the Delayed Gamma-ray Spectroscopy (DGS) active-interrogation NDA technique to evaluate the fissile composition from the unique fission product yield distributions. To analyze the data we are developing an Inverse Monte Carlo (IMC) method that simulates the interrogation and evaluates the individual contributions from the mixed nuclear material to the composite spectrum. While the current nuclear data affects the ability to evaluate the composition, the IMC analysis method can be used to determine the systematic uncertainty contributions and has the potential to improve the nuclear data. We will present the current status of the DGS collaborative work as it relates to the development of the DGS IMC analysis.

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

Monte Carlo Analysis of Nonequilibrium Ionized Flows.

1996 ◽  
Author(s):  
Iain D. Boyd ◽  
Xiaoming Liu ◽  
Jitendra Balakrishnan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis

scholarly journals Li-ion batteries for peak shaving, price arbitrage, and photovoltaic self-consumption in commercial buildings: A Monte Carlo Analysis

2021 ◽  
Vol 234 ◽  
pp. 113889
Author(s):  
Pietro Elia Campana ◽  
Luca Cioccolanti ◽  
Baptiste François ◽  
Jakub Jurasz ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Analysis ◽  
Commercial Buildings ◽  
Li Ion Batteries ◽  
Peak Shaving ◽  
Li Ion
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