Colour management system: Monte Carlo implementation for camouflage pattern generation

2020 ◽  
Vol 136 (5) ◽  
pp. 407-416
Author(s):  
Wojciech Przybył ◽  
Wojciech Radosz ◽  
Adam Januszko
Keyword(s):  
Monte Carlo ◽  
Management System ◽  
Pattern Generation ◽  
Monte Carlo Implementation

A Weighted Monte Carlo Simulation Approach to Risk Assessment of Information Security Management System

2015 ◽  
Vol 11 (4) ◽  
pp. 63-78 ◽  
Author(s):  
Seyed Mojtaba Hosseini Bamakan ◽  
Mohammad Dehghanimohammadabadi
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Security ◽  
Risk Analysis ◽  
Management System ◽  
Security Management ◽  
International Standards ◽  
Information Security Management ◽  
Information Security Management System

In recent decades, information has become a critical asset to various organizations, hence identifying and preventing the loss of information are becoming competitive advantages for firms. Many international standards have been developed to help organizations to maintain their competitiveness by applying risk assessment and information security management system and keep risk level as low as possible. This study aims to propose a new quantitative risk analysis and assessment methodology which is based on AHP and Monte Carlo simulation. In this method, AHP is used to create favorable weights for Confidentiality, Integrity and Availability (CIA) as security characteristic of any information asset. To deal with the uncertain nature of vulnerabilities and threats, Monte Carlo simulation is utilized to handle the stochastic nature of risk assessment by taking into account multiple judges' opinions. The proposed methodology is suitable for organizations that require risk analysis to implement ISO/IEC 27001 standard.

Geometry entrapment in Walk-on-Subdomains

2019 ◽  
Vol 25 (4) ◽  
pp. 329-340 ◽  
Author(s):  
Preston Hamlin ◽  
W. John Thrasher ◽  
Walid Keyrouz ◽  
Michael Mascagni
Keyword(s):  
Monte Carlo ◽  
Boltzmann Equation ◽  
Monte Carlo Method ◽  
Electrostatic Energy ◽  
Potential Solution ◽  
Long Time ◽  
Poisson Boltzmann ◽  
Poisson Boltzmann Equation ◽  
Monte Carlo Implementation

Abstract One method of computing the electrostatic energy of a biomolecule in a solution uses a continuum representation of the solution via the Poisson–Boltzmann equation. This can be solved in many ways, and we consider a Monte Carlo method of our design that combines the Walk-on-Spheres and Walk-on-Subdomains algorithms. In the course of examining the Monte Carlo implementation of this method, an issue was discovered in the Walk-on-Subdomains portion of the algorithm which caused the algorithm to sometimes take an abnormally long time to complete. As the problem occurs when a walker repeatedly oscillates between two subdomains, it is something that could cause a large increase in runtime for any method that used a similar algorithm. This issue is described in detail and a potential solution is examined.

A Monte Carlo Implementation of the James-Ford-Jivkov Microstructurally Informed Local Approach Applied to Predict Fracture Toughness Across Irradiation Conditions

2016 ◽  
Author(s):  
Michael Ford ◽  
Peter James
Keyword(s):  
Fracture Toughness ◽  
Monte Carlo ◽  
Transition Region ◽  
Crack Size ◽  
Model Parameters ◽  
Pressure Vessel Steel ◽  
Local Approach ◽  
Vessel Steel ◽  
Reactor Pressure Vessel Steel ◽  
Monte Carlo Implementation

The need to predict changes in fracture toughness for materials where the tensile properties change through life, such as with irradiation, whilst accounting for geometric constraint effects, such as crack size, are clearly important. Currently one of the most likely approaches by which to develop such ability are through application of local approach models. These approaches appear to be sufficient in predicting lower shelf toughness under high constraint conditions, but may fail when attempting to predict toughness in the transition region, for low constraint geometries or for different irradiation states, when using the same parameters, making reliable predictions impossible. Cleavage toughness predictions in the transition regime are here made with a stochastic, Monte Carlo implementation of the recently proposed James-Ford-Jivkov model. This implementation is based around the creation of individual initiators following the experimentally observed distribution for specific reactor pressure vessel steel, and determining if these initiators form voids or cause cleavage failure using the model’s improved criterion for particle failure. This implementation has been presented previously in PVP2015-45905, where it was successfully applied across different constraint conditions; in the work presented here it is applied across different irradiation conditions for a second type of steel. The model predicts the fracture toughness in a large part of the transition region, demonstrates an ability to predict the irradiation shift and shows a level of scatter similar to that observed experimentally. All results presented, for a given material, are obtained without changes in the model parameters. This suggests that the model can be used predicatively for assessing toughness changes due to constraint-, irradiation- and temperature-driven plasticity changes.

A Monte Carlo Implementation of the James-Ford-Jivkov Micro-Structurally Informed Local Approach Applied to Predict Fracture Toughness Including Low Constraint Conditions

2015 ◽  
Author(s):  
Michael Ford ◽  
Peter James
Keyword(s):  
Fracture Toughness ◽  
Monte Carlo ◽  
Transition Region ◽  
Crack Size ◽  
Model Parameters ◽  
Local Approach ◽  
Rpv Steel ◽  
Low Constraint ◽  
Constraint Effects ◽  
Monte Carlo Implementation

The need to predict changes in fracture toughness for materials where the tensile properties change through life, such as with irradiation, whilst accounting for geometric constraint effects, such as crack size, are clearly important. Currently one of the most likely approaches by which to develop such ability are through application of local approach models. These approaches appear to be sufficient in predicting lower shelf toughness under high constraint conditions, but may fail when attempting to predict toughness in the transition region or for low constraint geometries when using the same parameters, making predictions impossible. Cleavage toughness predictions in the transition regime that are then extended to low constraint conditions are here made with a stochastic, Monte Carlo implementation of the recently proposed James-Ford-Jivkov model. This implementation is based around the creation of individual initiators following the experimentally observed distribution for specific RPV steel, and determining if these initiators form voids or cause cleavage failure using the model’s improved criterion for particle failure. The model has shown to predict experimentally measured locations of cleavage initiators. Further, initial results from the Monte Carlo implementation of the model predicts the fracture toughness in a large part of the transition region, demonstrates an ability to predict the constraint shift and shows a level of scatter similar to that observed experimentally. All results presented, for a given material, are obtained without changes in the model parameters. This suggests that the model can be used predicatively for assessing toughness changes due to constraint- and temperature-driven plasticity changes.

A quasi-Monte Carlo implementation of the ziggurat method

2018 ◽  
Vol 24 (2) ◽  
pp. 93-99
Author(s):  
Nguyet Nguyen ◽  
Linlin Xu ◽  
Giray Ökten
Keyword(s):  
Monte Carlo ◽  
Random Variable ◽  
Numerical Results ◽  
Quasi Monte Carlo ◽  
Low Discrepancy Sequences ◽  
Gamma Distributions ◽  
Variable Generation ◽  
Monte Carlo Implementation

Abstract The ziggurat method is a fast random variable generation method introduced by Marsaglia and Tsang in a series of papers. We discuss how the ziggurat method can be implemented for low-discrepancy sequences, and present algorithms and numerical results when the method is used to generate samples from the normal and gamma distributions.

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