Security Simulation of Continuous-Variable Quantum Key Distribution over Air-to-Water Channel Using Monte Carlo Method

Considering the ocean water＇s optical attenuation and the roughness of the sea surface, we analyze the security of continuous-variable （CV） quantum key distribution （QKD） based Mr-to-water channel. The effects of the absorp- tion and scattering on the transmittance of underwater quantum channel and the maximum secure transmission distance are studied. Considering the roughness of the sea surface, we simulate the performance bounds of CV QKD with different wind speeds using the Monte Carlo method. The results show that even if the secret key rate gradually reduces as the wind speed increases, the maximum transmission distance will not be affected obviously. Compared to the works regarding short-distance underwater optical communication, our research represents a significant step towards establishing secure communication between air platform and submarine vehicle.

Dynamic Monte Carlo study on the probability distribution functions of tail-like polymer chain

The configurational properties of tail-like polymer chains with one end attached to a flat surface are studied by using dynamic Monte Carlo technique. We find that the probability distribution of the free end in z direction P(Rz) and the density profile ρ(z) can be scaled approximately by a factor β to be a length independent function for both random walking (RW) and self-avoiding walking (SAW) tail-like chains, where the factor β is related to the mean square end-to-end distance <R2>. The scaled P(Rz) of the SAW chain roughly overlaps that of the RW chain, but the scaled ρ(z) of the SAW chain locates at smaller βz than that of the RW chain.

Monte Carlo method for evaluation of surface emission rate measurement uncertainty

The aim of this study is to evaluate the uncertainty of 2πcx and 2πβsurface emission rates using the windowless multiwire proportional counter method.This study used the Monte Carlo method(MCM)to validate the conventional Guide to the Expression of Uncertainty in Measurement(GUM)method.A dead time mea surement model for the two-source method was established based on the cha racteristics of a single--channel measurement systemn,and the voltage threshold correction factor measurement function was indi rectly obtained by fitting the threshold correction curve.The uncertainty in the surface emission rate was calculated using the GUM method and the law of propagation of uncertainty.The MCM provided clear definitions for each input quantity and its uncertainty distribution,and the simulation training was realized witha complete and complex mathematical model.The results of the surface emission rate uncertainty evaluation for four radioactive plane sources using both methods showed the uncertainty's consistency E_(n)<0.070 for the comparison of each source,and the uncertainty results ofthe GUM were all lower than those of the MCM.However,the MCM has a more objective evaluation process and can serve as a validation tool for GUM results.

Probabilistic Response and Short-Term Extreme Load Estimation of Offshore Monopile Wind Turbine Towers by Probability Density Evolution Method

A new analysis framework based on probability density evolution method(PDEM)and its Chebyshev collocation solution are introduced to predict the dynamic response and short-term extreme load of offshore wind turbine(OWT)towers subjected to random sea state.With regard to the stochastic responses,random function method is employed to generate samples of sea elevation,the probability density evolution equation(PDEE)is solved to calculate time-variant probability density functions of structural responses.For the probabilistic load estimation,a FAST model of NREL 5MW offshore turbine is established to obtain samples of bending moment at the tower base.The equivalent extreme event theory is used to construct a virtual stochastic process(VSP)to assess the short-term extreme load.The results indicate that the proposed approach can predict time-variant probability density functions of the structural responses,and shows good agreement with Monte Carlo simulations.Additionally,the predicted short-term extreme load can capture the fluctuation at the tail of the extreme value distribution,thus is more rational than results from the typical distribution models.Overall,the proposed method shows good adaptation,precision and efficiency for the dynamic response analysis and load estimation of OWT towers.

Non-Homogeneous Poisson Processes Applied to Count Data:A Bayesian Approach Considering Different Prior Distributions

This article discusses the Bayesian approach for count data using non-homogeneous Poisson processes, considering different prior distributions for the model parameters. A Bayesian approach using Markov Chain Monte Carlo (MCMC) simulation methods for this model was first introduced by [1], taking into account software reliability data and considering non-informative prior distributions for the parameters of the model. With the non-informative prior distributions presented by these authors, computational difficulties may occur when using MCMC methods. This article considers different prior distributions for the parameters of the proposed model, and studies the effect of such prior distributions on the convergence and accuracy of the results. In order to illustrate the proposed methodology, two examples are considered: the first one has simulated data, and the second has a set of data for pollution issues at a region in Mexico City.

Bayesian Study Using MCMC of Three-Parameter Frechet Distribution Based on Type-I Censored Data

Type-I censoring mechanism arises when the number of units experiencing the event is random but the total duration of the study is fixed. There are a number of mathematical approaches developed to handle this type of data. The purpose of the research was to estimate the three parameters of the Frechet distribution via the frequentist Maximum Likelihood and the Bayesian Estimators. In this paper, the maximum likelihood method (MLE) is not available of the three parameters in the closed forms;therefore, it was solved by the numerical methods. Similarly, the Bayesian estimators are implemented using Jeffreys and gamma priors with two loss functions, which are: squared error loss function and Linear Exponential Loss Function (LINEX). The parameters of the Frechet distribution via Bayesian cannot be obtained analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the three parameters is obtained via Metropolis-Hastings algorithm. Comparisons of the estimators are obtained using Mean Square Errors (MSE) to determine the best estimator of the three parameters of the Frechet distribution. The results show that the Bayesian estimation under Linear Exponential Loss Function based on Type-I censored data is a better estimator for all the parameter estimates when the value of the loss parameter is positive.

Energy distributions of multiple backscattered photons in materials

Backscattering of gamma photons from a material is of fundamental importance in radiation shielding,industrial and medical applications, radiation dosimetry,and non-destructive testing. In Compton scattering, incident photons undergo multiple scatterings within the material(target) before exiting. Gamma photons continue to soften in energy as the number of scatterings increases in a thick target; in other words, the energy of gamma photons decreases as the scatterings increase in case of a thick target and results in the generation of singly and multiply scattered events. In this work, the energy distribution of backscattered gamma photons with backscattering intensity and energy probabilities were calculated by using the Monte Carlo method for metallic, biological, and shielding materials with various thicknesses of slab geometry. The materials under study were targeted with gamma photons of 0.279, 0.662, 1.250, and 2.100 Me V energies. In addition, the energy distributions of multiply scattered gamma photons were studied for materials with infinite geometry.The results are presented and discussed in detail by comparing with other Monte Carlo calculations.

BAYESIAN PREDICTION FOR THE TWO-PARAMETER EXPONENTIAL DISTRIBUTION BASED ON TYPE Ⅱ DOUBLY CENSORING

The two-parameter exponential distribution is proposed to be an underlying model,and prediction bounds for future observations are obtained by using Bayesian approach.Prediction intervals are derived for unobserved lifetimes in one-sample prediction and two-sample prediction based on type Ⅱ doubly censored samples.A numerical example is given to illustrate the procedures,prediction intervals are investigated via Monte Carlo method,and the accuracy of prediction intervals is presented.

The Computer Simulation System for Range Distribution of various Implantat

In this paper, the integrated software system named PRDII(Project Range Distribution of Ion Implantation), which is based on the transport theory and Monte Carlo method and is developed by ourselves for range distribution of ion implantation, is introduced. The system generally possess versatility., efficiency and practicality. As for versatility, the system can build and utilize the parameter bank of various ions and material bank of various parts, can calculate the range distributions of implanted ions in the target for various conditions, such as for different incident ions, for different compositions and shapes of targets, for different energies.. doses and angles of incidence; As for efficiency, the computations are accelerated greatly by using some numerical simulating formulae instead of numerical iterative solutions in key procedure and using fixed fly path instead of random fly path; As for practicality, the preassumptions of the simulation are more close to the reality and the simulating results are more accurate in that the sputtering and the dynamic changes of target composition during penetration are considered and the advanced Monte Carlo method is used. Compared to other similar software, such as famous TRIM[1] and SASAMAL[2], it is more beautiful for interface, more convenient for operation, It can call, compute.. compare.. comment, copy and print all curves needed, and the simulating results consist with the experiments very well.

A Time-dependent Stochastic Grassland Fire Ignition Probability Model for Hulun Buir Grassland of China

Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.

Inferences for the Generalized Logistic Distribution Based on Record Statistics

Estimation for the parameters of the generalized logistic distribution (GLD) is obtained based on record statistics from a Bayesian and non-Bayesian approach. The Bayes estimators cannot be obtained in explicit forms. So the Markov chain Monte Carlo (MCMC) algorithms are used for computing the Bayes estimates. Point estimation and confidence intervals based on maximum likelihood and the parametric bootstrap methods are proposed for estimating the unknown parameters. A numerical example has been analyzed for illustrative purposes. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Thickness distribution of large scale metallic sheet by EB-PVD on rotating substrate

A two-dimensional kinetic Monte-Carlo(KMC) method was used to approach the thickness distribution of large scale metallic sheet deposited by electron beam physical vapor deposition(EB-PVD) on rotating substrate. The KMC model involves incident atom attachment, adatom diffusion and adatom detachment. The effective deposition rate and effective incident angle along substrate radial were studied as influencing factors of sheet thickness distribution. The KMC simulation results indicate that incident angle is a very important factor to affect the sheet thickness distribution as well as theory deposition mass. The experiments results show that the KMC model can predict the thickness distribution of large scale sheet deposited by EB-PVD on rotating substrate.

Monte-Carlo模拟在土壤重金属生态风险评价中的应用

离散检测数据的重金属生态风险评估往往受采样数据空间覆盖度有限、检测数据随机波动等问题影响,使评价结果具有较大的不确定性.相对而言,基于地方区域的重金属概率统计以概率的形式解释污染等级的占比,能更有效指导风控方案的制定、污染的修复和场地的管控.本文应用Monte-Carlo模拟结合Hakanson潜在生态风险指数法,对云南省内某小流域土壤中的8种重金属(Cd、Hg、As、Pb、Cr、Cu、Ni和Zn)展开调查,分别从单种金属的污染等级、风险等级,以及整个区域的生态风险三个维度阐述当地的污染状况,系统性评估了当地的生态风险.研究表明Cd是主要污染贡献因子,其生态风险应引起重点关注.本研究证实了Monte-Carlo模拟可有效平衡Hakanson潜在生态风险指数法的随机性,将二者结合可更加全面精确地反映重金属的生态风险.

An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods

Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.

基于Monte Carlo method的均衡度确定模型

蒙特卡罗方法(Monte Carlo method),也称统计模拟方法,是一种以概率统计理论为指导的一类非常重要的数值计算方法,是指使用随机数(或更常见的伪随机数)来解决很多计算问题的方法,本文尝试建立警察服务平台的均衡度模型并用蒙特卡罗方法求解,实验结果可以满足一般的应用需求。

New Approach to Simulating the Absorption of Gamma Radiations Using the Monte-Carlo Method: A Computational Study

The following article has been retracted due to the investigation of complaints received against it. Mr. Mohammadali Ghorbani (corresponding author and also the last author) cheated the author’s name: Alireza Heidari. The scientific community takes a very strong view on this matter and we treat all unethical behavior such as plagiarism seriously. This paper published in Vol.3 No.3, 260-265, 2012, has been removed from this site.

Path integral Monte Carlo study of(H_2)_n@C_(70)(n = 1, 2, 3)

The path integral Monte Carlo(PIMC) method is employed to study the thermal properties of C70 with one, two,and three H2 molecules confined in the cage, respectively. The interaction energies and vibrationally averaged spatial distributions under different temperatures are calculated to evaluate the stabilities of(H2)n@C70(n = 1, 2, 3). The results show that(H2)2@C70is more stable than H2@C70. The interaction energy slowly changes in a large temperature range,so temperature has little effect on the stability of the system. For H2@C70and(H2)2@C70, the interaction energies keep negative; however, when three H2 molecules are in the cage, the interaction energy rapidly increases to a positive value.This implies that at most two H2 molecules can be trapped by C70. With an increase of temperature, the peak of the spatial distribution gradually shifts away from the center of the cage, but the maximum distance from the center of H2 molecule to the cage center is much smaller than the average radius of C70.

Stochastic Volatility Modeling based on Doubly Truncated Cauchy Distribution and Bayesian Estimation for Chinese Stock Market

In order to measure the uncertainty of financial asset returns in the stock market, this paper presents a new model, called SV-dt C model, a stochastic volatility(SV) model assuming that the stock return has a doubly truncated Cauchy distribution, which takes into account the high peak and fat tail of the empirical distribution simultaneously. Under the Bayesian framework, a prior and posterior analysis for the parameters is made and Markov Chain Monte Carlo(MCMC) is used for computing the posterior estimates of the model parameters and forecasting in the empirical application of Shanghai Stock Exchange Composite Index(SSECI) with respect to the proposed SV-dt C model and two classic SV-N(SV model with Normal distribution)and SV-T(SV model with Student-t distribution) models. The empirical analysis shows that the proposed SV-dt C model has better performance by model checking, including independence test(Projection correlation test), Kolmogorov-Smirnov test(K-S test) and Q-Q plot. Additionally, deviance information criterion(DIC) also shows that the proposed model has a significant improvement in model fit over the others.

基于Monte-Carlo的滑坡失稳概率计算研究

普通的滑坡稳定性计算,只能对滑坡的安全性做一个直观的计算,而滑坡的失稳概率却无从得知。利用蒙特卡洛（Monte-Carlo）法可确定滑坡的失稳概率,但传统应用蒙特卡洛（Monte-Carlo）计算滑坡失稳概率时,常认为c和φ值服从正态分布,其实在不同情况下c和φ值服从多种分布。基于此,探讨了岩土体参数c和φ服从不同分布形式时的滑坡失稳概率计算。结果表明,不考虑c和φ值的变异系数和偏度系数计算得出的失稳概率比考虑c和φ值的变异系数和偏度系数计算出的失稳概率偏高,且2种失稳概率误差为10%～25%。上述研究对滑坡失稳概率计算有一定的参考价值。

轮盘疲劳可靠性分析的Monte-Carlo数字仿真

低循环多轴疲劳失效是航空发动机低压压气机轮盘的主要失效形式。轮盘是一种价格昂贵的部件,对其进行部件的可靠性试验在经济上难以承受。据此以临界平面法为基础,通过Ansys有限元分析与材料的应变疲劳试验相结合,利用Monte-Carlo数字仿真方法,确定了轮盘寿命的分布形式,建立了轮盘疲劳可靠性模型,并编制了轮盘疲劳可靠性分析软件,对某型轮盘进行了疲劳可靠性分析。与传统的确定性方法相比更加符合轮盘的失效状况,与基于真实轮盘的试验相比则可以有效降低试验费用,缩短试验周期。