scholarly journals Optimal Control Methods of Experiment Times in System-of-Systems Combat Computer Simulation

2019 ◽  
Vol 26 ◽  
pp. 03004
Author(s):  
Zhiqiang Zhao ◽  
Feiyue Zhou
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Simulation Experiment ◽  
Random Factor ◽  
Interval Method ◽  
Interval Extension ◽  
Self Adaptation ◽  
Optimal Control Methods

In the process of scheme optimization, in order to eliminate the influence of random factor, it needs to conduct computer simulation of Monte Carlo. Therefore, it is proposed to introduce confidence interval into systemof-systems combat simulation, and confirm whether the Monte Carlo simulation finishes according to data sample generated in simulation process. According to characteristic of data sample, extend correspondingly confidence interval method, and under the condition of obtaining the solution meeting accuracy requirements, reduce simulation experiment times as far as possible. The simulation experiment results show that confidence interval extension method is able to possess self-adaptation control to Monte Carlo simulation.

scholarly journals The W/S Test for Data Having Neutrosophic Numbers: An Application to USA Village Population

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammed Albassam ◽  
Nasrullah Khan ◽  
Muhammad Aslam
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Distributions ◽  
Interval Method ◽  
Power Of Test ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
Village Population

The W/S test under neutrosophic statistics is proposed in this paper. The Monte Carlo simulation under the neutrosophic statistical interval method is proposed and applied to study the sensitivity of various neutrosophic statistical distributions. The power of test curves for neutrosophic distributions is presented. The efficiency of the proposed W/S test under neutrosophic statistics is compared with that of the W/S test under classical statistics. The proposed test is explained with the aid of an example.

Computer Simulation of Self-Assembly of Disc-Shaped Nanoparticles

2013 ◽  
Vol 710 ◽  
pp. 716-719
Author(s):  
Bo Du ◽  
Zi Lu Wang ◽  
Xue Hao He
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulated Annealing ◽  
Formation Mechanism ◽  
Self Assembly ◽  
Nematic Phase ◽  
The Self ◽  
Isotropic Phase ◽  
Shaped Nanoparticles

Understanding how nanoparticles self-assemble into specific structures is important in biology. The self-assembly structures of disc-shaped nanoparticles are investigated using Gay Berne potential. Through the simulated annealing Monte Carlo simulation underNVTcondition, we found that various nanostructures such as nematic phase and isotropic phase are discovered. The formation mechanism of these novel nanostructures is discussed.

Intensity and resolution effects in converging multichannel collimators for SANS by Monte Carlo simulation

2003 ◽  
Vol 36 (5) ◽  
pp. 1262-1265 ◽  
Author(s):  
A. N. Falcão ◽  
F. M. A. Margaça ◽  
F. G. Carvalho
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Neutron Beam ◽  
Incident Neutron ◽  
Beam Direction ◽  
Channel Axis ◽  
Monte Carlo Computer Simulation ◽  
The Individual ◽  
Incident Neutron Beam

A study of the contribution of the individual channels of a converging multichannel collimator to the operation of the device is carried out by means of a Monte Carlo computer simulation. The simulation shows that the coupling between the divergence of the incident neutron beam and the inclination of the individual CMC channel axis relative to the beam direction modulates the channel performance as far as intensity and resolution of the transmitted neutrons are concerned. While this does not impair in any significant way the usefulness of the device, the results are helpful to the designer.

Bias and Precision of Alternate Estimators in Meta-Analysis: Benefits of Blending Schmidt-Hunter and Hedges Approaches

2017 ◽  
Vol 22 (2) ◽  
pp. 490-514 ◽  
Author(s):  
Michael T. Brannick ◽  
Sean M. Potter ◽  
Bryan Benitez ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Root Mean Square Error ◽  
Random Effects ◽  
Meta Analysis ◽  
Size Distributions ◽  
Mean Square ◽  
The Mean

We describe a new estimator (labeled Morris) for meta-analysis. The Morris estimator combines elements of both the Schmidt-Hunter and Hedges estimators. The new estimator is compared to (a) the Schmidt-Hunter estimator, (b) the Schmidt-Hunter estimator with variance correction for the number of studies (“ k correction”), (c) the Hedges random-effects estimator, and (d) the Bonett unit weights estimator in a Monte Carlo simulation. The simulation was designed to represent realistic conditions faced by researchers, including population random-effects distributions, numbers of studies, and skewed sample size distributions. The simulation was used to evaluate the estimators with respect to bias, coverage of the 95% confidence interval of the mean, and root mean square error of estimates of the population mean. We also evaluated the quality of credibility intervals. Overall, the new estimator provides better coverage and slightly better credibility values than other commonly used methods. Thus it has advantages of both commonly used approaches without the apparent disadvantages. The new estimator can be implemented easily with existing software; software used in the study is available online, and an example is included in the appendix in the Supplemental Material available online.

Brittle Intergranular Failure in Grain Aggregates - A Computer Simulation by Means of the Graphics Processing Unit

2009 ◽  
Vol 409 ◽  
pp. 386-389
Author(s):  
Miriam Kupková ◽  
Samuel Kupka
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Binary Variable ◽  
Cubic Lattice ◽  
Increasing Temperature ◽  
Graphics Processing

Within a model considered, each of bonds between contacting grains is treated as a two-state system and represented by a binary variable. Its two values refer to the two possible states of bond – intact or broken. A Monte Carlo simulation of fracture is carried out on a set of binary variables arranged to a cubic lattice. The transition from one configuration of broken bonds to another is governed by a Griffith-like energy associated with each of configurations. The results demonstrate i) the capability of the model to provide a useful information (e.g. the increase in roughness of fracture surface with increasing temperature, that is the transition from “brittle” to “plastic” failure), and ii) the advantage of simulation by using the graphics processing unit (saving of a computational time).

scholarly journals Quantitative Comparison of the Spatial Distribution of Radar and Gauge Rainfall Data

2012 ◽  
Vol 13 (6) ◽  
pp. 1939-1953 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Anh Tran Phuong ◽  
Deg-Hyo Bae
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Spatial Distribution ◽  
Simulation Experiment ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Observation Data ◽  
Storm Events ◽  
Large Area ◽  
Radar Rainfall

Abstract The common statement that a rain gauge network usually provides better observation at specific points while weather radar provides more accurate observation of the spatial distribution of rain field over a large area has never been subjected to quantitative evaluation. The aim of this paper is to evaluate the statement by using some statistical criteria. The Monte Carlo simulation experiment, inverse distance weighting (IDW) interpolation method, and cross-validation technique are used to investigate the relation between the accuracy of the interpolated rainfall and the rain gauge density. The radar reflectivity–rainfall intensity (Z–R) relationship is constructed by the least squares fitting method from observation data of radar and rain gauges. The variation in this relationship and the accuracy of the radar rainfall with rain gauge density are evaluated by using the Monte Carlo simulation experiment. Three storm events are selected as the case studies. The obtained results show that the accuracy of interpolated and radar rainfall increases nonlinearly with increasing gauge density. The higher correlation coefficient (γ) value of radar-rainfall estimation, compared to gauge interpolation, especially in the convective storm, proves that radar observation provides a more accurate spatial structure of the rain field than gauge observation does.

scholarly journals Distribution of defects in lipid membranes. Computer simulation

2014 ◽  
Vol 65 (3-4) ◽  
pp. 257-260 ◽  
Author(s):  
Witold Okulski
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Lipid Membranes ◽  
Lipid Membrane ◽  
Ion Transfer ◽  
Nearest Neighbour ◽  
The Monte Carlo Method ◽  
Temperature Dependences ◽  
Lipid Chain

Based on the Monte-Carlo method, the ten-state Pink model of lipid membrane was completed by the dynamic Kawasaki-type processes of nearest neighbour exchanges of lipid molecules and vacancies in lipid chain lattice. The influence of the number and distribution of vacancies in membrane on its permeability was studied. The snapshots of microconfigurations of the dipalmitoylphosphatidylcholine (DPPC) membrane model obtained from Monte-Carlo simulation were analyzed. Temperature dependences of the area of vacancies in the membrane and regional probability of an ion transfer through interface region were determined.

scholarly journals Comparing the Thiessen’s Method against simpler alternatives using Monte Carlo Simulation

2018 ◽  
pp. 125-138
Author(s):  
Marcelo Guelfi ◽  
Carlos López-Vazquez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Bootstrap Method ◽  
Field Measurements ◽  
Expected Value ◽  
Systematic Analysis ◽  
Sample Mean ◽  
Friedman's Test ◽  
Data Location

Estimating the expected value of a function over geographic areas is problem with a long history. In the beginning of the XX-th century the most common method was just the arithmetic mean of the field measurements ignoring data location. In 1911, Thiessen introduced a new weighting procedure measuring influence through an area and thus indirectly considering closeness between them. In another context, Quenouville created in 1949 the jackknife method which is used to estimate the bias and the standard deviation. In 1979 Efron invented the bootstrap method which, among other things, is useful to estimate the expected value and the confidence interval (CI) from a population. Although the Thiessen’s method has been used for more than 100 years, we were unable to find systematic analysis comparing its efficiency against the simple mean, or even to more recent methods like jackknife or boostrap. In this work we compared four methods to estimate de expected value.  Sample mean, Thiessen, the so called here jackknifed Thiessen and bootstrap. All of them are feasible for routine use in a network of fixed locations. The comparison was made using the Friedman’s Test after a Monte Carlo simulation. Two cases were taken for study: one analytic with three arbitrary functions and the other using experimental data from daily rain measured with a satellite. The results show that Thiessen’s method is the best estimator in almost all the cases with a 95% of confidence interval. Unlike the others, the last two considered methods supply a suitable CI, but the one obtained through jackknifed Thiessen was even more accurate, opening the door for future work.

Genetic Crossing vs Cloning by Computer Simulation

1997 ◽  
Vol 08 (03) ◽  
pp. 605-608 ◽  
Author(s):  
Subinay Dasgupta
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Natural Disaster ◽  
Asexual Reproduction ◽  
String Model ◽  
Genetic Crossing

We perform Monte Carlo simulation using Penna's bit string model, and compare the process of asexual reproduction by cloning with that by genetic crossover. We find them to be comparable as regards survival of a species, and also if a natural disaster is simulated.

Research of Adaptive Synthesis Rule for Conflict Evidences

2012 ◽  
Vol 532-533 ◽  
pp. 1026-1030
Author(s):  
Sheng Wei Li ◽  
Han Li
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Evidence Synthesis ◽  
The Self ◽  
Synthesis Process ◽  
Balance Function ◽  
High Conflict ◽  
Improvement Method ◽  
Self Adaptation

In order to deal with the lack of treatment with high conflict evidence ability in traditional D-S evidence synthesis rules and some defects of improvement method, an adaptive synthesis rule for conflict evidence is presented. Firstly, based on the balance between conjunction and disjunction, the adaptive synthesis rule for conflict evidence is established; Secondly, the probability distribution of the conflict evidences is gathered by Monte Carlo simulation, and then a reasonable fusion balance function between conjunction and disjunction is established; Thirdly, the conflict coefficient is obtained real-time through conjunction; the self-adaptation of evidence synthesis process is realized simultaneously. Finally, the validity of this method is verified through the comparison with other methods.

Sign in / Sign up

Export Citation Format

Share Document