用蒙特-卡罗方法计算大气点扩散函数

大气效应的纠正是一个仍未被彻底解决的定量遥感问题，随着遥感器空间分辨率的不断提高，各种斜视星载遥感器的不断涌现，寻找交叉辐射项的修正方法日显迫切，该文应用蒙特卡罗（ＭＣ） 方法求得不同大气条件下的点扩散函数图形，结果显示：（１） 大气状况对点扩散函数的形状、目标像元的贡献率有不可忽略的影响；（２） 一次散射的假定将导致夸大背景像元的贡献率，其绝对误差最大可达７ ％ ，其相对误差最大可达４０ ％ ；（３） 斜视条件下与传感器同侧的像元贡献率最大，对侧像元的贡献率最小，其间的过渡曲线形状决定于传感器斜视角度与气溶胶尺度谱，而与入射辐射波长和大气能见度无关。

马尔可夫链—蒙特卡罗方法与人口学研究

本文概略介绍了用于粒子物理学研究的马尔可夫链-蒙特卡罗方法(MCMC)移植到人口学这个复杂系统研究的思想和方法,同时还介绍了马尔可夫链-蒙特卡罗方法在人口学应用中的重要工具和手段,如随机数、人口分布函数、人口样本的随机抽样和用马尔可夫链-蒙特卡罗方法进行人口仿真研究的简略过程及标题式介绍了用此方法做过的一些工作。

基于现实与虚拟交互的交通流再现实验方法

面向连续与间断交通流实验系统框架,利用现实交通流的观测数据,在实验框架的虚拟环境中建立交通流的非参数模型,通过虚拟框架的贝叶斯学习再现与现实等价的实验交通流.选取更为复杂的信号控制交通流场景对该实验方法进行验证.结果表明,该方法在一定精度内可以近似再现信号控制交通流.

SVD模态显著性检验的Monte Carlo方法及应用

本文介绍了SVD模态显著性检验的Monte Carlo统计检验方法,并在5个热带海洋区域进行了实际应用,指出 Monte Carlo统计检验方法比常规的统计检验方法,具有"去伪存真"的优点。

光子在高散射媒质中传输特性的蒙特-卡罗模拟

为研究光子在高散射媒质中的传输特性,将高散射媒质等效为离散的球形散射体集合。在蒙特-卡罗方法的基础上,提出一种新的椭圆算法来确定高散射媒质中散射点的分布。根据米氏散射理论和光子辐射输运方程,结合模拟解析得到的散射点的分布,不同粒径、不同光程下光子散射阶数的分布以及不同散射阶数的散射光强分布,从而实现光子在高散射媒质中动态特性的数值解析。数值解析结果表明光子在高散射媒质中的传输过程本质上是光子的复散射过程,这种数值解析方法能有效解析高散射媒质中光子从单散射到复散射全光程的动态传输特性。

采用MCMC方法的上海股市随机波动模型

采用贝叶斯统计中的马尔科夫链-蒙特卡罗(MCMC)方法对上海股市的随机波动性进行研究,基于Gibbs抽样的MCMC数值计算过程,对上海股市的随机波动率模型(SV)进行参数估计,并在WinBUGS软件中实现.根据信息判别准则(DIC),对比拟合的SV-N,SV-T,SV-MT模型参数,结果表明:SV-T模型最能反映上海股市波动具有尖峰厚尾的特性,可进一步用于预测样本外的波动率结果.

Extremum response surface method of reliability analysis on two-link flexible robot manipulator

In order to present a new method for analyzing the reliability of a two-link flexible robot manipulator,Lagrange dynamics differential equations of the two-link flexible robot manipulator were established by using the integrated modal method and the multi-body system dynamics method.By using the Monte Carlo method,the random sample values of the dynamic parameters were obtained and Lagrange dynamics differential equations were solved for each random sample value which revealed their displacement,speed and acceleration.On this basis,dynamic stresses and deformations were obtained.By taking the maximum values of the stresses and the deformations as output responses and the random sample values of dynamic parameters as input quantities,extremum response surface functions were established.A number of random samples were then obtained by using the Monte Carlo method and then the reliability was analyzed by using the extremum response surface method.The results show that the extremum response surface method is an efficient and fast reliability analysis method with high-accuracy for the two-link flexible robot manipulator.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Time-variant reliability analysis of three-dimensional slopes based on Support Vector Machine method

In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.

Reliability analysis of supporting pressure in tunnels based on three-dimensional failure mechanism

Based on nonlinear failure criterion,a three-dimensional failure mechanism of the possible collapse of deep tunnel is presented with limit analysis theory.Support pressure is taken into consideration in the virtual work equation performed under the upper bound theorem.It is necessary to point out that the properties of surrounding rock mass plays a vital role in the shape of collapsing rock mass.The first order reliability method and Monte Carlo simulation method are then employed to analyze the stability of presented mechanism.Different rock parameters are considered random variables to value the corresponding reliability index with an increasing applied support pressure.The reliability indexes calculated by two methods are in good agreement.Sensitivity analysis was performed and the influence of coefficient variation of rock parameters was discussed.It is shown that the tensile strength plays a much more important role in reliability index than dimensionless parameter,and that small changes occurring in the coefficient of variation would make great influence of reliability index.Thus,significant attention should be paid to the properties of surrounding rock mass and the applied support pressure to maintain the stability of tunnel can be determined for a given reliability index.

Comparison of GUF and Monte Carlo methods to evaluate task-specific uncertainty in laser tracker measurement

Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.

Study on slope reliability method Response Surface Method based on Morgenstern-Price Method

The combination of Morgenstern-Price Method （MPM） with Response Surface Method （RSM） improves the eonventional RSM by replacing Finite-Element Method （FEM） with MPM, which is a type of reliability analysis method-RSM based on MPM, and makes the computing process simpler. The stability of the slope was re-evaluated by use of Monte Carlo method （MCM） and the result contrasted with the fore is the conclusion. The reliability analysis of the artificial slope in Donggang Power Station was carried on, and obtained the reliable charaeteristics, which provided basis for the project.

Study on High Order Perturbation-based Nonlinear Stochastic Finite Element Method for Dynamic Problems

Several algorithms were proposed relating to the development of a framework of the perturbation-based stochastic finite element method (PSFEM) for large variation nonlinear dynamic problems. For this purpose, algorithms and a framework related to SFEM based on the stochastic virtual work principle were studied. To prove the validity and practicality of the algorithms and framework, numerical examples for nonlinear dynamic problems with large variations were calculated and compared with the Monte-Carlo Simulation method. This comparison shows that the proposed approaches are accurate and effective for the nonlinear dynamic analysis of structures with random parameters.

Stability,detonation properties and pyrolysis mechanisms of polynitrotriprismanes C_6H_(6-n)(NO_2)_n (n=1-6)

To further test whether polynitriprismanes are capable of being potential high energy density materials （HEDMs）, extensive theoretical calculations were carried out to investigate on a series of polynitrotriprismanes （PNNPs）： C6H6-.（NO2）. （n=1-6） Heats of formation （HOFs）, strain energies （SE）, and disproportionation energy （DE） were obtained using B3LYP/6-311＋G（2df, 2p）//B3LYP/6-31G＊ method by designing different isodesmic reactions, respectively. Detonation properties of PNNPs were obtained by the well-known KAMLET-JACOBS equations, using the predicted densities （p） obtained by Monte Carlo method and HOFs. It is found that they increase as the number of nitro groups n varies from 1 to 6, and PNNPs with n〉4 have excellent detonation properties The relative stability and the pyrolysis mechanism of PNNPs were evaluated by the calculated bond dissociation energy （BDE）. The comparison of BDE suggests that rupturing the C--C bond is the trigger for thermolysis of PNNPs. The computed BDE for cleavage of C--C bond （88.5 kJ/mol） further demonstrates that only the hexa-nitrotriprismane can be considered to be the target of HEDMs.

Dynamic Reliability Evaluation of Double-Layer Cylindrical Latticed Shell under Multi-Support Excitations

To overcome the excessive computational cost and/or bad accuracy of traditional approaches,the probabilistic density evolution method(PDEM) is introduced.The dynamic reliability of a double-layer cylindrical latticed shell is evaluated by applying PDEM and Monte Carlo Method(MCM) respectively,and four apparent wave velocities(100 m/s,500 m/s,800 m/s and 1 200 m/s) and five thresholds(0.1 m,0.2 m,0.3 m,0.4 m and 0.5 m) are taken into consideration.Only the difference between threshold and maximal deformation...

Energy-Efficient Distributed Lifetime Optimizing Scheme for Wireless Sensor Networks

In this paper, a sensing model for the coverage analysis of wireless sensor networks is provided. Using this model and Monte Carlo method, the ratio of private range to sensing range required to obtain the desired coverage can be derived considering the scale of deployment area and the number of sensor nodes. Base on the coverage analysis, an energy-efficient distributed node scheduling scheme is proposed to prolong the network lifetime while maintaining the desired sensing coverage, which does not need the geographic or neighbor information of nodes. The proposed scheme can also handle uneven distribution, and it is robust against node failures. Theoretical and simulation results demonstrate its efficiency and usefulness.

One-step random-walk process of nanoparticles in cement-based materials

Efficient modelling approaches capable of predicting the behavior and effects of nanoparticles in cement-based materials are required for conducting relevant experiments.From the microstructural characterization of a cement-nanoparticle system,this paper investigates the potential of cell-based weighted random-walk method to establish statistically significant relationships between chemical bonding and diffusion processes of nanoparticles within cement matrix.LaSr_(0.5)C_(0.5)O_(3)(LSCO)nanoparticles were employed to develop a discrete event system that accounts for the behavior of individual cells where nanoparticles and cement components were expected to interact.The stochastic model is based on annihilation(loss)and creation(gain)of a bond in the cell.The model considers both chemical reactions and transport mechanism of nanoparticles from cementitious cells,along with cement hydration process.This approach may be useful for simulating nanoparticle transport in complex 2D cement-based materials systems.

The Testing Intelligence System Based on Factor Models and Self-Organizing Feature Maps

Presented is a new testing system based on using the factor models and self-organizing feature maps as well as the method of filtering undesirable environment influence. Testing process is described by the factor model with simplex structure, which represents the influences of genetics and environmental factors on the observed parameters - the answers to the questions of the test subjects in one case and for the time, which is spent on responding to each test question to another. The Monte Carlo method is applied to get sufficient samples for training self-organizing feature maps, which are used to estimate model goodness-of-fit measures and, consequently, ability level. A prototype of the system is implemented using the Raven＇s Progressive Matrices （Advanced Progressive Matrices） - an intelligence test of abstract reasoning. Elimination of environment influence results is performed by comparing the observed and predicted answers to the test tasks using the Kalman filter, which is adapted to solve the problem. The testing procedure is optimized by reducing the number of tasks using the distribution of measures to belong to different ability levels after performing each test task provided the required level of conclusion reliability is obtained.

Reduced Expanding Load Method for Simulation-Based Structural System Reliability Analysis

The current situation and difficulties of the structural system reliability analysis are mentioned. Then on the basis of Monte Carlo method and computer simulation, a new analysis method reduced expanding load method (RELM) is presented, which can be used to solve structural reliability problems effectively and conveniently. In this method, the uncertainties of loads, structural material properties and dimensions can be fully considered. If the statistic parameters of stochastic variables are known, by using this method, the probability of failure can be estimated rather accurately. In contrast with traditional approaches,RELM method gives a much better understanding of structural failure frequency and its reliability index β is more meaningful.To illustrate this new idea, a specific example is given.

Three Semi-empirical Analytic Expressions for the Radial Distribution Function of Hard Spheres

Three simple analytic expressions satisfying the limitation condition at low densities for the radial distribution function of hard spheres are developed in terms of a polynomial expansion of nonlinear base functions and the Carnahan-Starling equation of state. The simplicity and precision for these expressions are superior to the well-known Percus Yevick expression. The coefficients contained in these expressions have been determined by fitting the Monte Carlo data for the first coordination shell, and by fitting both the Monte Carlo data and the numerical results of PercusYevick expression for the second coordination shell. One of the expressions has been applied to develop an analytic equation of state for the square-well fluid, and the numerical results are in good agreement with the computer simulation data.