Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method

The last decade witnessed the development of a large number of non-destructive tests for structural integrity evaluation. This growth is due to attracted interest to reduce time and costs to perform damage monitoring and predictive maintenance. In this way, several methods intended to detect structural damage based on sensitivity and statistical methods were proposed. However, some of these methods present some practical problems in measuring structural dynamic characteristics such as dynamic mode shapes. Some methods based exclusively on structural responses show disadvantages in finding the damage position on structures.     

A new approach for numerical identification of conductivity

This paper studies the parameter estimation problem for a steady state flow in an inhomogeneous medium. For identifying the spatially varying diffusion coefficient from an observed solution to the forward problem, we propose a direct method using local Green's function technique. This could be used when the diffusion coefficient is discontinuous. The convergence order is calculated and numerical simulations are performed.     

Naor’s model with heterogeneous customers and arrival rate uncertainty

This paper examines an extension of Naor’s observable queueing model in which the arrival rate is not known with certainty by either customers or the system manager. Further, customers are heterogeneous in terms of their service valuation and waiting time cost. We analyze the stability of the system and properties of the revenue maximizing fee and socially optimal fee.     

Parameter analysis based on stochastic model for differential evolution algorithm

In this paper, a stochastic model is used to describe and analyze the evolution process of differential evolution (DE) for numerical optimization. With the model, it illustrates how the probability distribution of the whole population is changed by mutation, selection and crossover operations. Based on the theoretical analysis, some guidelines about the parameter setting for DE are provided. In addition, numerical simulations are carried out to verify the conclusions drawn from model analysis.     

考虑替代模型不确定性的结构动力特性全局敏感性分析

结构参数的不确定性必然导致其动力特性具有不确定性,全局敏感性分析是定量各不确定参数动力特性影响大小的有效手段但是全局敏感性分析具有计算花费高的问题,为此采用快速Gauss(高斯)过程模型来降低计算成本此外,该文采用的是全局Gauss过程模型而不是它的均值函数来进行全局敏感性分析,考虑了替代模型不确定性,给出了敏感性指标的分布该方法的可靠性通过具有解析敏感性指标值的测试函数得到验证最后,将该方法用于安庆铁路长江大桥动力特性的敏感性分析     

MOIRA: A decision support system for decision making on aquatic ecosystems contaminated by radioactive fallout

Interventions to restore radionuclide contaminated aquatic ecosystems may reduce individual and collective radiation doses, but may also result in detrimental ecological, social and economic effects. Decision makers must carefully evaluate possible impacts before choosing a countermeasure, hence decision analysis methods constitute an important aid to rank intervention strategies after the contamination of an aquatic ecosystem. We describe MOIRA, a decision support system for the identification of optimal remedial strategies to restore water systems after accidental introduction of radioactive substances. MOIRA includes an evaluation module based on a multi-attribute value model to rank alternatives and a module to perform multiparametric sensitivity analyses, both with respect to weights and values, to allow us to gain insights into the problem. The problem is under certainty since the validation of models used to quantify countermeasure impacts suggests little uncertainty in policy effects.The system is implemented in a PC based decision support system which allows the inclusion of all relevant information.     

参数设计的绝对偏差法

本文在望目特性下分析了参数设计中内表所用的指标,指出了现有一些指标中存在的某些不足之处. 提出了新的内表指标——平均绝对偏差.在实际操作时,又把该指标分解为两个调节指标:正偏差与负偏差.本文以稳压电源的参数设计为例详细地叙述了这个“绝对偏差法”的全过程.在多个例子上的使用结果表明,此方法简单、易行、所寻找的可控因子调节方向常常是有效的.从而可不同程度地减少试验次数,提高分析效率.     

A methodology for sensitivity analysis of models fitted to data using statistical methods

A simple methodology is presented for sensitivity analysis ofmodels that have been fitted to data by statistical methods.Such analysis is a decision support tool that can focus theeffort of a modeller who wishes to further refine a model and/orto collect more data. A formula is given for the calculationof the proportional reduction in the variance of the model ‘output’that would be achievable with perfect knowledge of a subsetof the model parameters. This is a measure of the importanceof the set of parameters, and is shown to be asymptoticallyequal to the squared correlation between the model output andits best predictor based on the omitted parameters. The methodology is illustrated with three examples of OR problems,an age-based equipment replacement model, an ARIMA forecastingmodel and a cancer screening model. The sampling error of thecalculated percentage of variance reduction is studied theoretically,and a simulation study is then used to exemplify the accuracyof the method as a function of sample size.     

跳-扩散模型下期权定价方法及参数校准

该文对跳-扩散模型下期权定价方法及参数校准问题进行研究.首先,推导出了跳-扩散模型在均值修正等价鞅测度下的风险中性特征函数,利用COS方法对跳-扩散模型进行期权定价,分析了COS方法的定价误差,并通过数值实验验证了COS定价方法的有效性;然后,采用相对熵正则化方法对跳-扩散模型进行参数校准,通过数值模拟实验验证了校准方法的准确性和可靠性;最后,利用S&P500市场数据对模型参数进行校准.结果表明:不同到期日期权数据校准结果有很大不同,Merton跳-扩散模型比Black-Scholes模型能更好的模拟市场数据.     

A minmax regret median problem on a tree under uncertain locations of the demand points

This paper deals with the location of a new facility on a tree according to the minimization of the weighted distance to the customers. The weights represent demands of the set of nodes. The exact location of each customer will be assumed unknown but close   to its corresponding node. In this context, an algorithm to find a minmax regret median is proposed and its complexity is shown to be O(nlog(n))$O(nlog\left(n\right))$, where n$n$ is the number of nodes of the tree     

基于熵的不确定性敏感性分析

为确定影响维修保障信息分析结果不确定性的关键输入参数,提出一种基于熵的不确定性敏感性分析方法.首先分析了模型输出结果的不确定性的表现形式、度量方法、产生机制;其次基于条件熵思想给出分析不确定性的敏感性参数以及敏感性分析方法;然后,简单介绍了盲数理论在计算不确定性数据中的应用;最后,结合平均维修时间模型进行了案例研究,说明了基于熵的不确定性敏感性分析方法的可用性,并证明了不确定性敏感性分析与求导不能等同的结论.     

A new method for parameter sensitivity estimation in structural reliability analysis

For the parameter sensitivity estimation with implicit limit state functions in the time-invariant reliability analysis, the common Monte Carlo simulation based approach involves multiple trials for each parameter being varied, which will increase associated computational cost and the cost may become inevitably high especially when many random variables are involved. Another effective approach for this problem is featured as constructing the equivalent limit state function (usually called response surface) and performing the estimation in FORM/SORM. However, as the equivalent limit state function is polynomial in the traditional response surface method, it is not a good approximation especially for some highly non-linear limit state functions. To solve the above two problems, a new method, support vector regression based response surface method, is therefore presented in this paper. The support vector regression algorithm is employed to construct the equivalent limit state function and FORM/SORM is used in the parameter sensitivity estimation, and then two illustrative examples are given. It is shown that the computational cost of the sensitivity estimation can be greatly reduced and the accuracy can be retained, and results of the sensitivity estimation obtained by the proposed method are in satisfactory agreement with those computed by the conventional Monte Carlo methods.     

基于区间的土体参数敏感性分析方法研究

将一种新的工程结构不确定性分析方法——区间分析方法溶入工程参数的敏感性分析之中,获得了一种新的工程参数敏感性分析方法,进一步拓宽了区间分析方法理论的应用领域．给出了土体参数敏感性因子矩阵求解的区间分析过程,依据区间分析给出了参数区间和决策目标区间的确定方法．基于MARC软件进行了二次开发,实现了Duncan-Chang非线性弹性模型以及与Fortran程序的相互调用功能．通过工程算例验证了该方法的合理可行性,并与文献的结果进行了对比．     

On the equivalence of quadratic optimization problems commonly used in portfolio theory

In the paper, we consider three quadratic optimization problems which are frequently applied in portfolio theory, i.e., the Markowitz mean–variance problem as well as the problems based on the mean–variance utility function and the quadratic utility. Conditions are derived under which the solutions of these three optimization procedures coincide and are lying on the efficient frontier, the set of mean–variance optimal portfolios. It is shown that the solutions of the Markowitz optimization problem and the quadratic utility problem are not always mean–variance efficient.