一种真空荧光屏的可靠性分析

基于真空荧光屏(VFD)寿命服从对数正态分布的条件,本文在逐次定数截尾样本下研究了VFD的平均寿命、失效率和可靠度的点估计及其置信下限.并利用Monte-Carlo方法对估计结果的优劣进行了分析比较,验证了结论的正确性和可行性.     

小样本条件下对数正态分布均值置信区间

介绍了在小样本条件下3种构造对数正态分布均值置信区间的方法,即:PB方法.P值方法和r*方法.然后从4个方面进行对比,得出r*方法优于其它2种方法,并且在实际方面给予应用.     

马槽滩磷矿合理勘探网度的地质统计学研究

本文利用网度与克立格估值方差之间严格定量的数学关系,探讨了马槽滩磷矿的合理勘探网度。各级网度又经抽稀计算矿石储量,检验了其合理性。     

基于随机分布函数的分年龄别生育率组合模型

分年龄别生育率是人口统计学的重要指标,它可以用基于随机分布函数的生育率模型来描述.文中介绍了对数正态分布模型和泊松分布模型,在此基础上构造了生育率组合模型.仿真结果表明,与单一模型相比,组合模型精度更高,其适用范围也更广泛.     

一种结构响应预测策略与新可靠度分析方法

工程结构在服役期不可避免地遭受各种不确定因素的侵害,为客观科学地描述不确定因素的影响,可靠度理论得以产生和发展。但传统的可靠度分析方法囿于精度和效率等原因,难以应用于实际工程结构。近年来基于概率守恒原理,概率密度演化理论提出并得到发展。但对复杂的工程结构,效率低下等问题依然有待解决。鉴于此,本文提出两级剖分概率空间的概念,以粗剖分获得的少量代表样本构成训练集,训练Kriging模型;然后细剖分概率空间,通过训练的Kriging模型预测加密样本响应提高分析效率。基于此,结合以概率密度演化理论为基础的物理综合法,提出了一种新的可靠度分析方法。通过对一解析系统和一幢钢筋混凝土框架结构的响应预测与可靠度分析,证明了新提出方法的精度和效率。     

对数正态总体分布的联合置信域

从置信区间本质意义出发,得到了对数正态总体参数的联合置信域以及置信域的面积公式.     

时间－空间域中多元信息的地质统计学

主要研究时间－空间域中多个变量的地质统计学方法，其内容包括：时间－空间域中区域化变量的性质；时间－空间域中多元信息的互变异函数及互协方差函数；应用时间－空间信息对某区域化变量进行最优估计，以及应用时间－空间域的信息对空间分量及区域化因子进行最优估计。     

Compactly Supported Correlation Functions

This article proposes compactly supported correlation functions, which parameterize the smoothness of the associated stationary and isotropic random field. The constructions are straightforward, and compact support is relevant for various ends: computationally efficient spatial prediction, fast and exact simulation, and appeal among practicioners.     

一种新钻孔孔位设计的最优化方法

本文给出了一种寻求新钻孔孔位的最优化方法。该方法将最优钻孔孔位问题转化为一个可计算的最优化问题,并用数论中的一致分布点列求解之。     

FEREBUS: Highly parallelized engine for kriging training

FFLUX is a novel force field based on quantum topological atoms, combining multipolar electrostatics with IQA intraatomic and interatomic energy terms. The program FEREBUS calculates the hyperparameters of models produced by the machine learning method kriging. Calculation of kriging hyperparameters ( θ and p ) requires the optimization of the concentrated log‐likelihood . FEREBUS uses Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to find the maximum of . PSO and DE are two heuristic algorithms that each use a set of particles or vectors to explore the space in which is defined, searching for the maximum. The log‐likelihood is a computationally expensive function, which needs to be calculated several times during each optimization iteration. The cost scales quickly with the problem dimension and speed becomes critical in model generation. We present the strategy used to parallelize FEREBUS, and the optimization of through PSO and DE. The code is parallelized in two ways. MPI parallelization distributes the particles or vectors among the different processes, whereas the OpenMP implementation takes care of the calculation of , which involves the calculation and inversion of a particular matrix, whose size increases quickly with the dimension of the problem. The run time shows a speed‐up of 61 times going from single core to 90 cores with a saving, in one case, of ～98% of the single core time. In fact, the parallelization scheme presented reduces computational time from 2871 s for a single core calculation, to 41 s for 90 cores calculation. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.