Monte Carlo Simulation of an Arc Therapy Treatment by Means of a PC Distribution Model

2001 ◽  
pp. 329-332
Author(s):  
A. Leal ◽  
F. Sánchez-Doblado ◽  
M. Perucha ◽  
M. Rincón ◽  
R. Arráns ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Model ◽  
Arc Therapy ◽  
Therapy Treatment

scholarly journals Empirical Bayesian regression model for estimation of small rates

2012 ◽  
Vol 53 ◽  
Author(s):  
Gintautas Jakimauskas ◽  
Leonidas Sakalauskas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Data ◽  
Real Data ◽  
Simulation Method ◽  
Bayesian Regression ◽  
Distribution Model ◽  
Estimation Model ◽  
Hospital Discharges ◽  
Large Populations

The efficiency of adding an auxiliary regression variable to the logit model in estimation of small probabilities in large populations is considered. Let us consider two models of distribution of unknown probabilities: the probabilities have gamma distribution (model (A)), or logits of the probabilities have Gaussian distribution (model (B)). In modification of model (B) we will use additional regression variable for Gaussian mean (model (BR)). We have selected real data from Database of Indicators of Statistics Lithuania – Working-age persons recognized as disabled for the first time by administrative territory, year 2010 (number of populations K = 60). Additionally, we have used average annual population data by administrative territory. The auxiliary regression variable was based on data – Number of hospital discharges by administrative territory, year 2010. We obtained initial parameters using simple iterative procedures for models (A), (B) and (BR). At the second stage we performed various tests using Monte-Carlo simulation (using models (A), (B) and (BR)). The main goal was to select an appropriate model and to propose some recommendations for using gamma and logit (with or without auxiliary regression variable) models for Bayesian estimation. The results show that a Monte Carlo simulation method enables us to determine which estimation model is preferable.

scholarly journals Development of Fire and Explosion Risk Assessment on Coal Reclaim Tunnel Using Monte Carlo Simulation and Risk Matrix Method

2021 ◽  
Author(s):  
Nuhindro Priagung Widodo ◽  
Dimas Agung Permadi ◽  
Ahmad Ihsan ◽  
Ginting Jalu Kusuma
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Group ◽  
Distribution Model ◽  
Risk Matrix ◽  
Moderate Risk ◽  
Controllable Factors ◽  
Fire And Explosion ◽  
Explosion Risk

The comprehensive fire and explosion risk assessment has been studied for Coal Reclaim Tunnel (CRT) facility by applying the Monte Carlo simulation method. In this research, the fire and explosion risk of two existing CRT, namely model A and model B, have been assessed. A set of 30 data for each factor has been used to define the statistical distribution model, sourced from historical data, field measurement, and laboratory experiments. Based on the simulation, CRT model A has a 100% extreme risk group, while model B has two risk groups, high risk=81.73% and moderate risk=18.27%, classified as a not acceptable risk. Several preventive actions were set to reduce the probability and severity level as low as reasonably possible, especially for the controllable factors. Furthermore, the probability and severity levels were re-adjusted by the Monte Carlo simulation. The result shows that both the CRT model have been grouped into a 100% moderate-risk group. For optimal prevention against explosion risk, a sensitivity analysis has been carried out to find the most influential factors for the fire and explosion risk in CRT. Through this research, a method for risk matrix assessment related to the fire and explosion in the CRT facility has been developed.

scholarly journals Dynamic Reliability Evaluation of Road Vehicle Subjected to Turbulent Crosswinds Based on Monte Carlo Simulation

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Wang ◽  
Yongle Li ◽  
Helu Yu ◽  
Haili Liao
Keyword(s):  
Surface Roughness ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Numerical Models ◽  
Random Processes ◽  
Distribution Model ◽  
Vibration System ◽  
Road Vehicle ◽  
Dynamic Reliability

As a vehicle moves on roads, a complex vibration system of the running vehicle is formed under the collective excitations of random crosswinds and road surface roughness, together with the artificial handing by the drivers. Several numerical models in deterministic way to assess the safety of running road vehicles under crosswinds were proposed. Actually, the natural wind is a random process in time domain due to turbulence, and the surface roughness of a road is also a random process but in spatial domain. The nature of a running vehicle therefore is an extension of dynamic reliability excited by random processes. This study tries to explore the dynamic reliability of a road vehicle subjected to turbulent crosswinds. Based on a nonlinear vibration system, the dynamic responses of a road vehicle are simulated to obtain the dynamic reliability. Monte Carlo Simulation with Latin Hypercube Sampling is then applied on the possible random variables including the vehicle weight, road friction coefficient, and driver parameter to look at their effects. Finally, a distribution model of the dynamic reliability and a corresponding index for the wind-induced vehicle accident considering these random processes and variables is proposed and employed to evaluate the safety of the running vehicle.

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