基于Kriging模型的子集模拟方法

研究建筑结构失效的准确判断问题.在实际工程,需对每个随机样本点进行结构分析,一旦建筑工程较大,需要采集的随机样本数量较大,需要较大的计算成本,造成计算的耗时,甚至模型不收敛.传统的基于Kriging模型的工程结构失效判断方法,受到模型不稳定的影响,影响建筑失效判断的准确性.为此提出了基于Kriging模型的子集模拟方法,引入学习函数主动寻找并添加训练样本,对每个中间失效域建立对应的Kriging模型,用Kriging模型的预测值代替结构实际功能函数值进行仿真计算.实验仿真表明,改进模型只需要进行少量的结构分析就能得到与子集模拟方法类似的仿真结果,可以和有限元程序对接,能够大幅度降低仿真方法的计算代价.     

基于Kriging模型的铁路棚架结构失效预测

为研究铁路棚架结构在受到断裂导线的冲击作用下是否失效，提出基于Kriging模型的铁路棚架结构失效预测方法。首先建立棚架结构的有限元模型，用于仿真计算棚架在极限载荷下的应力；然后，提出采用Kriging模型对棚架结构在极限载荷下的破坏区域进行预测；最后，利用K折叠交叉验证方法对所建立的Kriging模型的精度进行验证，并与径向基函数（Radial Basis Function, RBF）模型预测精度进行对比。结果表明，所提出的基于Kriging模型的铁路棚架结构失效预测方法具有更高的预测精度，可应用于指导铁路棚架的结构设计。     

MATLAB在无速度传感器交流调速系统仿真中的应用

介绍了采用先进的科学计算软件MATLAB对异步电机无速度传感器交流调速系统进行计算机模拟的方法。根据模型参考自适应原理，采用直接转矩控制技术，利用MATLAB语言强大的计算和绘图功能，实现了调速系统的仿真。仿真研究表明，该方法能避免传统计算机模拟的复杂编程过程，减少了工作量，有助于提高系统的综合效率。     

计算机磁头飞行过程的仿真研究

该文采用蒙特卡洛直接模拟方法(DSMC)对计算机硬盘中的磁头飞行过程进行动态仿真，并将计算结果与传统的基于线性Boltzmann方程的修正Reynolds方程数值计算结果比较。在DSMC模拟过程中，用大量的仿真粒子来模拟实际系统，基于硬球模型对粒子的三维速度和位置分量进行计算和存储，得到了沿膜厚方向的二维压力分布。两种方法得到的结果非常吻合，该文采用DSMC法对传统方法不能解决的发生线接触的工况进行了计算，给出了磁头飞行过程中发生与磁盘撞击的仿真结果。     

基于自适应Kriging模型的不确定可靠性功能优化算法研究

产品研发中功能失效是一个复杂的系统性工程,失效过程包含大量不确定性因素。为此,构建了自适应Kriging的不确定可靠性功能优化算法,进行产品总体功能失效分析、认知集合可信任度、样本点的生成、自适应Kriging计算及优选功能组合,获取在指定的概率约束下的最优解。以大数定律及极限定理为基础,保证了样本点在重要区域及Kriging模型的收敛条件。以工程机械储能系统为例,说明算法的迭代性、收敛性、准确性及稳定性。结果表明,该算法能够得出准确的敏感度,节省计算时间,提高计算效率。     

基于GO法的航空电子设备可靠性评估

针对传统电子设备在可靠性评估中存在精度和效率较低问题,提出一种基于GO法的可靠性评估方法。该方法引入状态累积概率,建立考虑共有信号处理的GO模型,给出某航空电子设备监测模块基于GO法的可靠性评估实例。与传统Monte—carlo方法进行比较,结果证明该方法更简单、直观,具有更高的计算精度、仿真效率,且更容易解决复杂多态、时序问题。     

蒙特卡洛仿真在移动通信中的应用研究

蒙特卡洛法的实质是利用服从某种分布的随机数来模拟现实系统中可能出现的随机现象。该文首先给出了如何利用蒙特卡洛(Monte-Carlo，简称MC)法来近似地描述研究对象的概率分布的一般方法；然后利用这一工具重点地研究如何模拟移动通信中的高斯(正态)、Reyleigll、Rice和Nakagami-m等四种常见的随机分布。在此基础上，该文详细地介绍了如何利用蒙特卡洛法对AWGN信道上的QPSK通信系统的传输特性进行仿真，同时也给出了16PSK系统的仿真结果。由于MC法是一种随机计算机模拟试验，其仿真精度与仿真次数有关。论文以AWGN信道的QPSK通信系统为例，用MC法仿真QPSK比特误码率BER随仿真次数变化曲线，并与理论值曲线做比较。仿真结果表明：在通信系统中，若仿真次数(或系统发送符号序列数)大于5000，MC仿真精度能基本满足工程上的要求。该文提供的方法对那些缺乏通信仿真软件的通信工作人员有一定的参考作用。     

基于离散点采样的多分辨率的自由形体几何造型

经过二十余年的发展，三维几何模型已经被广泛的应用于工业工程上的计算机模拟和可视化。传统的几何模型通常基于样条计算。基于离散点采样的复杂自由形体几何造型是近年来随着激光扫描仪的发展而新兴的热门研究课题。通过离散点采样而得到的多分辨率的复杂几何造型现在被广泛的应用于高清晰度三维游戏开发、电子商务和电子图书馆。     

旋转体特征量的闭环算法

本文针对一般旋转体类零部件特征量的计算提出了一种新算法。该算法在引入数据封闭环概念和结构计算图概念的基础上，应用函数拟合方法仿真旋转体母线方程，然后直接对特征量进行离散化求积，使计算过程更加清晰连续且符合现代工程计算的理念。与传统工程算法相比，本算法克服了数据允余量大，计算机过程烦琐等特点，有效地提高了计算机效率和计算精度，成为旋转体类产品设计CAD系统的有机组成部分。     

基于三维Savitzky-Golay滤波的蒙特卡罗剂量分布去噪

提出了一种基于三维Savitzky-Golay滤波的蒙特卡罗（MC）剂量分布的去噪方法。该方法首先利用MC方法模拟粒子轨迹数目较少时得到剂量的三维分布，然后对该剂量分布用三维Savitzky-Golay平滑滤波方法进行去噪处理。结果表明：采用三维Savitzky-Golay平滑滤波方法去噪，不仅提高了剂量分布的可视性，降低了MC计算剂量分布的不确定性。而且也相应地提高了MC剂量计算方法的计算效率。     

基于均值改进控制策略的昂贵约束并行代理优化算法

针对具有黑箱特性的昂贵约束优化问题及工程中计算资源利用率不高问题,提出了新的基于均值改进控制策略的并行代理优化算法.该算法为了减少仿真建模计算负担,选取Kriging近似模型对目标函数和约束函数进行近似估计.在Kriging模型基础上,利用均值改进与新增试验样本间的不等关系构建具有距离特性的控制函数.算法的均值改进控制...     

A derivative based sensitivity measure of failure probability in the presence of epistemic and aleatory uncertainties

In order to analyze the effect of the epistemic uncertainty of random variables’ distribution parameters on the safety of the structure system, a novel sensitivity measure of the failure probability is constructed by integrating the derivative of failure probability in the parameter space. Compared with the variance based sensitivity index, the new derivative based sensitivity measure can be evaluated with less computational cost. It is noticed that the ranking of the new derivative based sensitivity measure is the same as that of the variance based one. For the problem of the variance based sensitivity index with large computational cost, the standard Sobol’s method is employed, and the quasi-Monte Carlo method and double-loop point estimate method are then utilized to compute the derivative based sensitivity measure for comparison. Four examples are employed to demonstrate the reasonability of the proposed sensitivity measure and the efficiency of the proposed method.     

An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability

A vital challenge problem of structural reliability analysis is how to estimate the small failure probability with a minimum number of model evaluations. The Adaptive Kriging combined with Importance Sampling method (AK-IS) which is developed from the adaptive Kriging combined with Monte Carlo simulation (AK-MCS) is a viable method to deal with this challenge. The aim of this paper is to reduce the number of model evaluations of the existing AK-IS algorithm. Firstly, we use a contributive weight function to divide the candidate samples of model input variables generated in AK-IS. The candidate samples are used to select the best next sample to update the Kriging model in AK-IS. Secondly, select the best next sample only in the important area obtained according to the contributive weight value to failure probability to update the Kriging model until the stopping condition is satisfied. Thirdly, use the Kriging model constructed in the important area to predict the other area and update the important area by adding the point with the maximum contributive weight value in the area except the important area ceaselessly until the probability of the accurate identification on the limit state function’s signs (positive limit state value or negative limit state value) of all the importance sampling points satisfies a criterion. Finally, the updated Kriging model is used to estimate the failure probability especially for the small failure probability. The proposed method uses a thought from local to global in order to reduce the computational cost of AK-IS and simultaneously guarantees the accuracy of estimation. A non-linear oscillator system, a roof truss structure and a planar ten-bar structure are analyzed by the proposed method. The results demonstrate the efficiency and accuracy of the proposed method in structural reliability analysis especially for small failure probability.     

Extended Gaussian Kriging for computer experiments in engineering design

Metamodeling or surrogate modeling is becoming increasingly popular for product design optimization in manufacture industries. In this paper, an extended Gaussian Kriging method is proposed to improve the prediction performance of widely used ordinary Kriging in engineering design. Unlike the forgoing approaches, the proposed method places a variance-varying Gaussian prior on the unknown regression coefficients in the mean model of Kriging and makes prediction at untried design points based on the principle of Bayesian maximum a posterior. The achieved regression mean model is adaptive, therefore capable of capturing more effectively the overall trend of computer responses and leading to a more accurate metamodel. Particularly, the regression coefficients in the mean model are estimated by a fast numerical algorithm, making extended Gaussian Kriging implemented roughly as efficient as ordinary Kriging. Experiment results on several examples are presented, showing remarkable improvement in prediction using extended Gaussian Kriging over ordinary Kriging and several other metamodeling methods.     

结合Kriging和物理规划的多目标代理优化算法

遗传算法处理高耗时且具有黑箱性的工程优化问题效率不足。为了提高工程优化效率，结合Kriging代理优化和物理规划，提出了基于Kriging和物理规划的多目标代理优化算法。在处理多目标问题时，使用物理规划将多目标问题转换成单目标问题，再使用Kriging代理优化对单目标问题进行求解。通过两个多目标数值算例和一个工程实例对提出的算法进行验证。结果表明，提出的算法能够求出符合偏好设置的Pareto最优解，且算法的效率更高。     

基于互相关算法的飞机导线故障诊断与定位系统设计

以飞机导线短路故障和断路故障为研究对象,目的在于寻求一种测试方法能够提高应用时域反射测量原理进行故障诊断与定位的精度;按照实际机场工程应用背景,设计了以便携式工控机为控制核心、以LabVIEW为驱动软件的飞机导线故障诊断与定位系统;根据信号传播特性,提出了基于互相关算法的故障定位方法,消除了系统误差的影响;计算机仿真和实物测试结果表明该系统具有测试过程方便、准确性高的优点,对提高民航机务维修质量具有促进作用。     

基于人工神经网络的参数灵敏度分析模型*

通过人工神经网络算法与参数灵敏度分析的结合,找到了一种新的工程系统功能模拟和变化分析方法。神经网络可以有效地解决复杂、非线性系统的功能模拟问题,其传递函数的可微性为参数灵敏度矩阵的求解提供了保证,从而方便寻找系统输入属性与输出属性之间的影响因子。同时,该模型具有良好的扩展性,可以更加全面地考虑系统影响因素。经实例仿真分析表明：该方法在工程分析方面,能够快速找到属性之间的关联程度,得到准确、稳定的分析结果,满足工程分析需求。     

特征融合和聚类核函数平滑采样优化的粒子滤波目标跟踪方法

针对复杂场景下的目标跟踪问题,提出了一种改进的粒子滤波目标跟踪方法。利用背景加权后的联合直方图描述目标灰度和梯度特征信息,在粒子滤波算法的框架下,设计了一种自适应特征融合观测模型来适应场景的不断变化;同时针对传统粒子滤波算法存在的粒子退化问题,提出了一种基于聚类核函数平滑采样的方法。理论仿真和实际场景的实验结果表明,该算法适应性更强,精度更高,能有效跟踪复杂场景下的运动目标。     

Reliability analysis of structures using stochastic response surface method and saddlepoint approximation

Stochastic response surface method (SRSM) is a technique used for reliability analysis of complex structural systems having implicit or time consuming limit state functions. The main aspects of the SRSM are the collection of sample points, the approximation of response surface and the estimation of the probability of failure. In this paper, sample points are selected close to the most probable point of failure and the actual limit state surface (LSS). The response surface is fitted using the weighted regression technique, which allows the fitting points to be weighted based on their distance from the LSS. The cumulant generating function (CGF) of the response surface is derived analytically. The saddlepoint approximation (SPA) method is utilized to compute the probability of failure of the structural system. Finally, four numerical examples compare the proposed algorithm with the traditional quadratic polynomial SRSM, Kriging based SRSM and direct MCS.