scholarly journals Monte Carlo simulation of climate-weather change process at maritime ferry operating area

2017 ◽  
Vol 1 (21) ◽  
pp. 5-17
Author(s):  
Ewa Kuligowska
Keyword(s):  
Monte Carlo ◽  
Change Process ◽  
Fixed Time ◽  
Limit Values ◽  
Mean Values ◽  
Sojourn Times ◽  
Process Characteristics ◽  
The Monte Carlo Method ◽  
The Mean ◽  
Weather Change

The paper presents a computer simulation technique applied to generating the climate-weather change process at Baltic Sea restricted waters and its characteristics evaluation. The Monte Carlo method is used under the assumption of semi-Markov model of this process. A procedure and an algorithm of climate-weather change process’ realizations generating and its characteristics evaluation are proposed to be applied in C# program preparation. Using this program, the climate-weather change process’ characteristics are predicted for the maritime ferry operating area. Namely, the mean values and standard deviations of the unconditional sojourn times, the limit values of transient probabilities and the mean values of total sojourn times for the fixed time at the climate-weather states are determined.

scholarly journals System Safety Model Related to Climate-Weather Change Process Application to Port and Maritime Transport

2018 ◽  
Vol 46 (1) ◽  
pp. 21-34
Author(s):  
Mateusz Torbicki
Keyword(s):  
Change Process ◽  
Piping System ◽  
Maritime Transport ◽  
System Safety ◽  
Mean Values ◽  
Port Area ◽  
The Mean ◽  
Sea Area ◽  
System Operating ◽  
Weather Change

Abstract The conditional safety functions at the climate-weather particular states and the unconditional safety functions of the port oil piping transportation system area and the maritime ferry, the mean values and the variances of those systems unconditional lifetimes and other safety indicators are determined. Those safety indicators, considering impact of the climate-weather change process, are evaluated for the piping system operating at under water Baltic sea area and for the maritime ferry operating at Gdynia Port area.

scholarly journals The Reliability Model of Artificial Monitoring on the Anomalous Event in Expressway Tunnels and Monte Carlo Simulation

2019 ◽  
Vol 4 (3) ◽  
pp. p172
Author(s):  
Ling WU ◽  
Yueqi HU ◽  
Weihua ZHAO ◽  
Tong ZHU
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Video Surveillance ◽  
Warning System ◽  
Reliability Models ◽  
The Monte Carlo Method ◽  
Anomalous Event ◽  
Actual Operation ◽  
The Mean ◽  
Mean Time

Artificial monitoring remains to be a major way to detect anomalous events in expressway tunnels. To estimate the reliability of artificial monitoring on anomalous events in expressway tunnels, the video surveillance and mobile inspection based reliability models of artificial monitoring on the anomalous event in the expressway tunnel were built, and Monte Carlo method was applied to calculate the probability and mean time to detect the anomalous event at the specific time. The results showed that the Monte Carlo method could simulate video surveillance and mobile inspection, and obtain the probability distribution and mean time of detecting anomalous events. The mean time to spot the anomalous event was in reverse relation with the number of inspectors, the time of mobile inspection, and the reliability probability of the monitoring pre-warning system in tunnels and was in positive relationships with the departure interval. Combined with the actual operation cost, the model serves as a basis for the artificial monitoring package.

Estimation of Random Variable Distribution Parameters by the Monte Carlo Method

2013 ◽  
Vol 20 (2) ◽  
pp. 249-262 ◽  
Author(s):  
Sergiusz Sienkowski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Variable ◽  
Statistical Parameters ◽  
The Monte Carlo Method ◽  
Variable Distribution ◽  
Distribution Parameters ◽  
The Subject ◽  
The Mean ◽  
Typical Measurement

Abstract The paper is concerned with issues of the estimation of random variable distribution parameters by the Monte Carlo method. Such quantities can correspond to statistical parameters computed based on the data obtained in typical measurement situations. The subject of the research is the mean, the mean square and the variance of random variables with uniform, Gaussian, Student, Simpson, trapezoidal, exponential, gamma and arcsine distributions.

Monte Carlo simulation of the effect of counting losses on measured X-ray intensities

2007 ◽  
Vol 40 (5) ◽  
pp. 964-965
Author(s):  
T. Ida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Dead Time ◽  
Statistical Properties ◽  
Time Model ◽  
Mean Values ◽  
Counting Loss ◽  
X Ray ◽  
The Mean

The statistical properties of intensities affected by counting loss based on conventional non-extended and extended dead-time models are examined by a Monte Carlo method. It has been confirmed that the variance of the counted pulses for the non-extended dead-time model with the rate of generated pulsesr and the dead-time τ is given by \sigma_{\rm non}^2 = \mu_{\rm non}/(1+r \tau)^2, while that for the extended dead-time model is given by \sigma_{\rm ext}^2 = \mu_{\rm ext} [1 - 2r\tau \exp(-r \tau)], as proposed by Laundy & Collins [(2003).J. Synchrotron Rad.10, 214–218], for the mean values of counted pulses μnonand μext, respectively. Practical formulae to estimate the statistical errors of the corrected intensities are also presented.

scholarly journals Corrigendum to ``Descriptive Measures of Poisson-Lomax Distribution''

2020 ◽  
Vol 43 (2) ◽  
pp. 345-353
Author(s):  
Khushnoor Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Programming Language ◽  
Numerical Results ◽  
Due Diligence ◽  
Mean Values ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
The Mean

This corrigendum focuses on the correction of numerical results derived from Poisson-Lomax Distribution (PLD) originally proposed by Al-Zahrani & Sagor (2014). Though the mathematical properties and derivations by Al-Zahrani & Sagor (2014) were immaculate but during the execution ofthe R codes using Monte Carlo simulation some anomalies occurred in the calculation of the mean values. The same  anomalies are addressed in thepresent corrigendum. The outcome of the corrigendum will provide basic guidelines for the academia and reviewers of various journals to match thenumerical results with the shape of the probability distribution under study. The results will also emphasize the fact that code writing is a cumbersome process and due diligence be exercised in executing the codes using any programming language. Relevant R codes are appended in Appendix 'A'.

Putting Statistics Into the Statistical Energy Analysis of Automotive Vehicles

1993 ◽  
Author(s):  
Clark J. Radcliffe ◽  
Xian Li Huang
Keyword(s):  
Monte Carlo ◽  
Energy Analysis ◽  
Computational Effort ◽  
Design Tool ◽  
Physical Parameters ◽  
Statistical Energy Analysis ◽  
Mean Values ◽  
Interior Spaces ◽  
The Monte Carlo Method ◽  
Design Values

Abstract Sound and vibration transmission modeling methods are important to the design process for high quality automotive vehicles. Statistical Energy Analysis (SEA) is an emerging design tool for the automotive industry that was initially developed in the 1960’s to estimate root-mean-square sound and vibration levels in structures and interior spaces. Although developed to estimate statistical mean values, automotive design application of SEA needs the additional ability to predict statistical variances of the predicted mean values of sound and vibration. This analytical ability would allow analysis of vehicle sound and vibration response sensitivity to changes in vehicle design specifications and their statistical distributions. This paper will present an algorithm to extend the design application of the SEA method through prediction of the variances of RMS responses of vibro-acoustic automobile structures and interior spaces from variances in SEA automotive model physical parameters. The variance analysis is applied to both a simple, complete illustrative example and a more complex automotive vehicle example. Example variance results are verified through comparison with a Monte Carlo test of 2,000 SEA responses whose physical parameters were given Gaussian distributions with means at design values. Analytical predictions of the response statistics agree with the statistics generated by the Monte Carlo method but only require about 1/300 of the computational effort.

Putting Statistics into the Statistical Energy Analysis of Automotive Vehicles

1997 ◽  
Vol 119 (4) ◽  
pp. 629-634 ◽  
Author(s):  
C. J. Radcliffe ◽  
X. L. Huang
Keyword(s):  
Monte Carlo ◽  
Energy Analysis ◽  
Computational Effort ◽  
Design Tool ◽  
Physical Parameters ◽  
Statistical Energy Analysis ◽  
Mean Values ◽  
Interior Spaces ◽  
The Monte Carlo Method ◽  
Design Values

Sound and vibration transmission modeling methods are important to the design process for high quality automotive vehicles. Statistical Energy Analysis (SEA) is an emerging design tool for the automotive industry that was initially developed in the 1960’s to estimate root-mean-square sound and vibration levels in structures and interior spaces. Although developed to estimate statistical mean values, automotive design application of SEA needs the additional ability to predict statistical variances of the predicted mean values of sound and vibration. This analytical ability would allow analysis of vehicle sound and vibration response sensitivity to changes in vehicle design specifications and their statistical distributions. This paper will present an algorithm to extend the design application of the SEA method through prediction of the variances of RMS. responses of vibro-acoustic automobile structures and interior spaces from variances in SEA automotive model physical parameters. The variance analysis is applied to both a simple, complete illustrative example and a more complex automotive vehicle example. Example variance results are verified through comparison with a Monte Carlo test of 2,000 SEA responses whose physical parameters were given Gaussian distributions with means at design values. Analytical predictions of the response statistics agree with the statistics generated by the Monte Carlo method but only require about 1/300 of the computational effort.

scholarly journals THREE CLASSIFICATIONS ON BRANCHING PROCESSES AND THEIR BEHAVIOR FOR FINDING THE SOLUTION OF NONLINEAR INTEGRAL EQUATIONS

2010 ◽  
Vol 15 (3) ◽  
pp. 371-381
Author(s):  
Behrouz Fathi Vajargah ◽  
Mojtaba Moradi
Keyword(s):  
Integral Equation ◽  
Monte Carlo ◽  
Fixed Point ◽  
Monte Carlo Method ◽  
Integral Equations ◽  
Branching Process ◽  
Branching Processes ◽  
Nonlinear Integral Equations ◽  
The Monte Carlo Method ◽  
The Mean

In this paper, we consider the Monte Carlo method for finding the solution of nonlinear integral equations at a fixed point xo‐ In this method, simulated Galton‐Watson branching process is employed for solving the proposed integral equation. The main goal of this paper is to compare the behavior of three classifications of branching process based on the mean progeny, i.e. the subcritical, critical and supercritical process.

scholarly journals Stochastic Responses of Multi-Degree-of-Freedom Uncertain Hysteretic Systems

2011 ◽  
Vol 18 (1-2) ◽  
pp. 387-396
Author(s):  
Yimin Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moment Method ◽  
Degree Of Freedom ◽  
Random Response ◽  
Hysteretic Model ◽  
Mean Values ◽  
Hysteretic Systems ◽  
The Mean ◽  
Second Moment Method

On the basis of the Bouc-Wen hysteretic model, the effective numerical method for the response of nonlinear multi-degree-of-freedom (MDOF) stochastic hysteretic systems is presented using second moment method. Using this method, the mean values, variances and covariances are computed. The Monte Carlo simulation is applied to validate the method. The results obtained by the two methods are contrasted, and the solutions of the method in this paper agreed very well with the Monte Carlo simulation. It has solved the random response of nonlinear stochastic vibration systems which is caused by the stochastic hysteretic loop itself.

scholarly journals Monte Carlo cubature construction

2020 ◽  
Author(s):  
Satoshi Hayakawa
Keyword(s):  
Monte Carlo ◽  
Data Compression ◽  
Numerical Integration ◽  
Prior Information ◽  
Probability Measures ◽  
Test Functions ◽  
Mean Values ◽  
Cubature Formulas ◽  
The Mean

AbstractIn numerical integration, cubature methods are effective, especially when the integrands can be well-approximated by known test functions, such as polynomials. However, the construction of cubature formulas has not generally been known, and existing examples only represent the particular domains of integrands, such as hypercubes and spheres. In this study, we show that cubature formulas can be constructed for probability measures provided that we have an i.i.d. sampler from the measure and the mean values of given test functions. Moreover, the proposed method also works as a means of data compression, even if sufficient prior information of the measure is not available.

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