A discrete stress–strength interference model based on universal generating function

Continuous stress–strength interference (SSI) model regards stress and strength as continuous random variables with known probability density function. This, to some extent, results in a limitation of its application. In this paper, stress and strength are treated as discrete random variables, and a discrete SSI model is presented by using the universal generating function (UGF) method. Finally, case studies demonstrate the validity of the discrete model in a variety of circumstances, in which stress and strength can be represented by continuous random variables, discrete random variables, or two groups of experimental data.     

Single- and multiple-pulse noncoherent detection statistics associated with partially developed speckle

Single- and multiple-pulse detection statistics are presented for aperture-averaged direct detection optical receivers operating against partially developed speckle fields. A partially developed speckle field arises when the probability density function of the received intensity does not follow negative exponential statistics. The case of interest here is the target surface that exhibits diffuse as well as specular components in the scattered radiation. An approximate expression is derived for the integrated intensity at the aperture, which leads to single- and multiple-pulse discrete probability density functions for the case of a Poisson signal in Poisson noise with an additive coherent component. In the absence of noise, the single-pulse discrete density function is shown to reduce to a generalized negative binomial distribution. The radar concept of integration loss is discussed in the context of direct detection optical systems where it is shown that, given an appropriate set of system parameters, multiple-pulse processing can be more efficient than single-pulse processing over a finite range of the integration parameter n.     

基于拒绝抽样算法的结构体系可靠度更新

该文着重研究基于观测信息的结构体系可靠度更新模型及其拒绝抽样算法。基于Bayesian理论建立考虑观测信息的结构体系失效概率更新模型,根据观测信息事件类型建立不等式和等式观测信息条件下随机变量的似然函数并推导其后验概率密度函数;基于观测信息域确定随机变量后验样本的拒绝抽样策略,探究拒绝抽样算法的抽样效率,推导更新后结构体系失效概率估计值及其标准差的计算公式;将上述方法应用于刚架结构发生塑性失效时体系可靠度更新计算。研究表明:考虑观测信息的结构体系条件失效概率更新模型可转化为随机变量后验概率密度在失效域上的积分,构造满足观测信息域的先验样本作为随机变量后验样本的抽样策略是可行的,该抽样策略可以处理多随机变量、多观测信息条件下结构体系可靠度更新;与抗力相关随机变量检测值增大及验证荷载值提高均可以降低更新后结构体系的失效概率,与抗力相关的随机变量还需控制其检测误差的标准差,以降低观测信息的不确定性。     

Efficient evaluation of the pdf of a random variable through the kernel density maximum entropy approach

In this paper, a new approach for the evaluation of the probability density function (pdf) of a random variable from the knowledge of its lower moments is presented. At first the classical moment problem (MP) is revisited, which gives the conditions such that the assigned sequence of sample moments represent really a sequence of moments of any distribution. Then an alternative approach is presented, termed as the kernel density maximum entropy (MaxEnt) method by the authors, which approximates the target pdf as a convex linear combination of kernel densities, transforming the original MP into a discrete MP, which is solved through a MaxEnt approach. In this way, simply solving a discrete MaxEnt problem, not requiring the evaluation of numerical integrals, an approximating pdf converging toward the MaxEnt pdf is obtained. The method is first demonstrated by approximating some known analytical pdfs (the chi‐square and the Gumbel pdfs) and then it is applied to some experimental engineering problems, namely for modelling the pdf of concrete strength, the circular frequency and the damping ratio of strong ground motions, the extreme wind speed in Messina's Strait region. All the developed numerical applications show the goodness and efficacy of the proposed procedure. Copyright © 2008 John Wiley & Sons, Ltd.     

Probability Density Functions of Speckle Intensity Produced by Weak Diffusers

By using the discrete cell model for the rough surface of objects, a general expression is derived which gives the probability density function of speckle intensity produced by weak diffusers having an arbitrary phase distribution and an arbitrary cell number, N, of contributions for the formation of speckles. In some special cases, it gives the probability density functions which have been previously derived and are well known in the statistics of speckles. In the case of the small number N = 2, an analytical solution of the probability density function is obtained from that general expression. Some features on the probability density functions of speckles are discussed for the case of small roughness and small number N.     

CPT-based LPI criteria for ground liquefaction risk classification

Abstract

The liquefaction potential index (LPI) has been extensively applied to ground liquefaction hazard assessments. In this paper, the Robertson’s unified cone penetration test (CPT) based method was applied to determine the LPIs for case histories in two well-documented investigation areas: Adapazari in Turkey and Yuanlin City in Taiwan. A statistical method was applied to find LPI threshold values for low, medium, and high ground liquefaction potential at LPIs of 5.6, 12.5, and 21.7, respectively. According to an analysis of the various risk levels by applying Bayes’ theorem of conditional probability, the probabilities of liquefaction are 0, 32.3, 85.2, and 100% at the low, medium, high, and extremely high ground liquefaction risk level, respectively.     

Joint distribution of peaks and valleys in a stochastic process

General expressions and numerical results are presented pertaining to the occurrence of two local extrema of a stochastic process at prescribed time values. The extrema may be either peaks or valleys and the process may be either stationary or nonstationary. General formulas are presented for the rates of occurrence, the joint and conditional probability distributions, and the moments of the extreme values. These formulas are relatively simple multiple-integral expressions, but the integrands involve the joint probability density function for six random variables. The procedures are then applied for the special case of a stationary mean-zero Gaussian process for which the calculations are greatly simplified. Numerical results for three different spectral density functions demonstrate that conditioning on either only the existence or both the existence and the value of one peak can have a very significant effect on both the rate of occurrence and the probability distribution of a second peak.     

Reliability analysis for multidisciplinary systems with the mixture of epistemic and aleatory uncertainties

Epistemic and aleatory uncertain variables always exist in multidisciplinary system simultaneously and can be modeled by probability and evidence theories, respectively. The propagation of uncertainty through coupled subsystem and the strong nonlinearity of the multidisciplinary system make the reliability analysis difficult and computational cost expensive. In this paper, a novel reliability analysis procedure is proposed for multidisciplinary system with epistemic and aleatory uncertain variables. First, the probability density function of the aleatory variables is assumed piecewise uniform distribution based on Bayes method, and approximate most probability point is solved by equivalent normalization method. Then, important sampling method is used to calculate failure probability and its variance and variation coefficient. The effectiveness of the procedure is demonstrated by two numerical examples. Copyright © 2013 John Wiley & Sons, Ltd.     

基于乘法定理和AL模型的风速风向联合概率分布的研究及应用

该文开展了风速风向联合概率分布的研究,以大理地区1971年~2017年47年间的风速日值数据资料为例,选用乘法定理和AL模型两种方法建立该地区风速风向联合概率分布。首先,对各风向以及全风向风速数据的最优概率分布进行研究;其次,分别基于谐波函数和混合von Mises分布对风向的概率密度进行拟合,并进一步基于乘法定理和Angular-Linear （AL）模型推导得出了风速风向联合概率密度函数;最后,对大理地区50年重现期内的极值风速进行预测。研究结果表明:Gumbel分布能更好地描述大理地区的风速分布规律,通过AL模型获得的风速风向联合概率密度函数明显优于基于乘法定理得到的联合概率密度函数;而忽略风向的影响将明显高估大理地区的极值风速。     

Monte Carlo filters for identification of nonlinear structural dynamical systems

The problem of identification of parameters of nonlinear structures using dynamic state estimation techniques is considered. The process equations are derived based on principles of mechanics and are augmented by mathematical models that relate a set of noisy observations to state variables of the system. The set of structural parameters to be identified is declared as an additional set of state variables. Both the process equation and the measurement equations are taken to be nonlinear in the state variables and contaminated by additive and (or) multiplicative Gaussian white noise processes. The problem of determining the posterior probability density function of the state variables conditioned on all available information is considered. The utility of three recursive Monte Carlo simulation-based filters, namely, a probability density function-based Monte Carlo filter, a Bayesian bootstrap filter and a filter based on sequential importance sampling, to solve this problem is explored. The state equations are discretized using certain variations of stochastic Taylor expansions enabling the incorporation of a class of non-smooth functions within the process equations. Illustrative examples on identification of the nonlinear stiffness parameter of a Duffing oscillator and the friction parameter in a Coulomb oscillator are presented. This paper is dedicated to Prof R N Iyengar of the Indian Institute of Science on the occasion of his formal retirement.     

Statistics of intensity in adaptive-optics images and their usefulness for detection and photometry of exoplanets

This paper is an introduction to the problem of modeling the probability density function of adaptive-optics speckle. We show that with the modified Rician distribution one cannot describe the statistics of light on axis. A dual solution is proposed: the modified Rician distribution for off-axis speckle and gamma-based distribution for the core of the point spread function. From these two distributions we derive optimal statistical discriminators between real sources and quasi-static speckles. In the second part of the paper the morphological difference between the two probability density functions is used to constrain a one-dimensional, "blind," iterative deconvolution at the position of an exoplanet. Separation of the probability density functions of signal and speckle yields accurate differential photometry in our simulations of the SPHERE planet finder instrument.     

Probability distributions of peaks and troughs of non-Gaussian random processes

This paper deals with the development of probability density functions applicable for peaks, troughs and peak-to-trough excursions of a non-Gaussian random process where the response of a non-linear system is represented in the form of Volterra's second-order functional series. The density functions of peaks and troughs are derived in closed form and presented separately. It is found that the probability density function applicable to peaks (and troughs) is equivalent to the density function of the envelope of a random process consisting of the sum of a narrow-band Gaussian process and sine wave having the same frequency. Furthermore, for a non-Gaussian random process for which the skewness of the distribution is less than 1.2, the density function of peaks (and troughs) can be approximately presented in the form of a Rayleigh distribution. The parameter of the Rayleigh distribution is given as a function of parameters representing the non-Gaussian characteristics. The results of comparisons between newly derived density functions and histograms of peaks, troughs and peak-to-trough excursions constructed from data with strong non-linear characteristics show that the distributions well represent the histograms for all cases.     

考虑风向风速联合分布的输电塔线体系风振疲劳研究

输电塔线体系属于风敏感结构体系,在结构设计和实际工作中风荷载均起控制作用。然而,在风荷载计算以及工程研究领域中对风向风速的处理至关重要,但因两者之间的相关性导致了建立联合分布模型的困难。为此,该文基于乘法定理以重庆市日极值风速为对象,建立了风速条件概率密度的混合模型,改善了单一概率分布模型的不足。与此同时,根据风向方位记录数据建立了风向角的概率密度函数模型,并结合风速条件概率密度的混合模型,给出了风向风速的离散-连续混合联合分布模型。然后,将风向风速联合分布模型与Miner线性疲劳累积损伤理论相结合,推导出了输电塔的风振疲劳计算方法。最后,采用此建议算法对特高压输电塔线结构体系进行了风振疲劳分析,有效地考虑了风向风速对输电塔体风振疲劳的影响,减少了计算量,提高了结构分析的计算效率。     

Run-Related Probability Functions Applied to Sampling Inspection

Run-related distributions are frequently encountered in sampling inspection in the application of stopping rules for different phases of sampling. In many cases the generating function of these distributions is known; nevertheless, it is too complicated to derive the pdf using the ordinary method, and only the expectation and variance are obtained. We present an approach, based on partial fraction expansion, to derive the exact pdf of several run-related variables from their generating function. Using efficient algorithms for finding roots of polynomials and simple recursive formulas, we complete the method presented by Feller. We derive and present exact probability functions of some run-related variables. An application to sampling inspection of attribute data in batches and for continuous production is suggested.     

An Electronic Probability Density Machine

A relatively simple semiconductor circuit is described which will present an output plot of probability density p(v) vs. the variable v. Experimental data is presented and compared with theoretical expectations for variations of variables such as amplitude, frequency, waveshape, and "sampling window width."     

固定测站的风向风速联合分布研究

风速与风向联合分布在工程规划、结构设计以及风能资源评价等方面应用广泛。风玫瑰图是目前分析风要素常用的手段,不足之处在于该图只能表示特定16个方位各级风力的频率分布情况。基于连续型角度-线性联合分布模型,该文提出了根据风玫瑰图建立联合分布模型的新方法,并绘制了一种新型且在风速及风向上均连续的风频分布图。以辽东湾长兴岛站的风测数据为例,建立风速风向联合分布模型,绘制风速风向联合分布图。结果表明:1)统计模型可靠性高,与原始风向风速分布相关性极强;2)联合分布模型相比风向离散的风速分布模型,可以更好地反映邻近方位之间风速分布的相互影响;3)新的联合分布图给出了全方向的风频理论分布,克服了传统风玫瑰图中仅有16个风向的不足。     

正态云模型的重尾性质证明

正态分布和重尾分布在概率研究中具有非常重要的地位,二者具有完全不同的数学形式和物理意义。正态分布的密度函数以指数函数衰减至0,服从正态分布的随机变量,其绝大多数取值在其期望附近,偏离期望很大的取值很少。而服从重尾分布的随机变量,其尾分布函数具有重尾特性,密度函数以幂指数衰减至0。笔者证明了正态云模型是具有均值的重尾分布,是介于正态分布与重尾分布之间的中间状态,正态云模型的参数超熵He是可以实现正态分布向重尾分布转换的桥梁。     

Monte Carlo structural optimization in discrete variables with annealing algorithm

The paper describes the basic ideas of Monte Carlo annealing algorithms for structural optimization with discrete design parameters. The algorithm generates randomly a set of design parameters, with probability depending on the objective function and given by the Boltzmann–Gibbs distribution. In this model the search for the global minimum is simulated by a relaxation process of the statistical mechanical system with the Hamiltonian proportional to the objective function. The rate of the convergence of the method and its dependence upon the annealing probability are discussed. Numerical implementation of the method for the weight optimization of the ten-bar planar cantilever truss is presented. The results of numerical simulation are compared with those obtained by the dual methods. The principal conjecture is that the method is fairly efficient and has great potential for applicaton in engineering design.     

随机结构动力反应的极值分布

提出了求解随机结构动力反应极值分布的概率密度演化方法。基于随机结构动力反应概率密度演化分析的基本思想，可构造一个与随机结构动力反应极值有关的具有“虚拟时间参数”的随机过程及其导数过程，导出了一维概率密度演化方程。结合结构动力反应的时程分析方法与有限差分方法，可求解该随机过程的一维概率密度函数。当虚拟时间参数为1时，即得到随机结构动力反应的极值分布。这一方法可用来求解一般的随机抽样和随机过程的极值分布。与随机抽样极大值分布的理论结果比较表明，本文建议方法具有良好的精度。在此基础上，分析了八层框架结构随机动力反应极值分布的若干特征。     

非正态变量情况下基于杂交SAPSO-SFLA的多目标稳健协同优化

为解决非正态变量空间中复杂多变的隐式非线性功能函数的可靠性及灵敏度的问题,融合鞍点估计与线抽样法的优点,结合二分法的特点与黄金分割法的求解效率,提出基于黄金分割二分法的鞍点线抽样法,即可沿重要线抽样方向利用黄金分割点的二分法快速找到各样本点对应于功能函数的零点,将结构失效概率转化为一系列线性功能函数失效概率的平均值,求出相关变量的可靠性灵敏度,从而导出失效概率对变量均值与方差的可靠性灵敏度及结构轻量化的多目标优化问题,并阐明了多目标协同优化的思想。同时,针对可靠性灵敏度作为目标函数因误差导致多目标协同优化难以收敛的问题,提出了利用误差的思想与方法;为提高算法的收敛性,对粒子群优化(Particle Swarm Optimization,PSO)算法与混合蛙跳算法(Shuffled Frog-Leaping Algorithm,SFLA)进行改进以后,再将两者进行杂交,提出杂交自适应粒子群优化-混合蛙跳算法(Self-Adaptive PSO-SFLA,SAPSO-SFLA),并用来求解上述多目标优化问题。算例表明:1) 基于黄金分割二分法的鞍点线抽样法在求解复杂非线性功能函数的可靠性及灵敏度时精度高,速度快;2) 与粒子群优化和混合蛙跳算法相比,所提杂交SAPSO-SFLA不仅具有更快的收敛速度,其鲁棒性还能使盾构行星减速器箱体体积减小8.42%。