A New Combined Shewhart–Cumulative Sum S Chart for Monitoring Process Standard Deviation

The combined application of a Shewhart chart and cumulative sum (CUSUM) control chart is an effective tool for the detection of all sizes of process shifts as the scheme combines the advantages of a CUSUM at detecting small to moderate shifts and Shewhart for the quick detection of very large shifts. This article proposes new combined Shewhart–CUSUM S charts based on the extreme variations of ranked set sampling technique, for efficient monitoring of changes in the process dispersion. Using Monte Carlo simulations, the combined scheme is designed to minimize the average extra quadratic loss over the entire process shift domain. The results show that the combined Shewhart–CUSUM S charts uniformly outperform several other procedures for detecting increases and decreases in the process variability. Moreover, the proposed scheme can detect changes that are small enough to escape the Shewhart S chart or fairly large to escape detection by the CUSUM S chart. Numerical example is given to illustrate the practical application of the proposed scheme using real industrial data. Copyright © 2015 John Wiley & Sons, Ltd.     

空间站展开机构的失谐动态响应分析

考虑了动力失谐、质量失谐和摩擦失谐三种常见失谐现象,研究了空间站展开机构的失谐动态响应。利用多柔体系统动力学方法,在机械系统动力学自动分析(ADAMS)仿真平台下建立了空间站柔性展开机构的失谐模型,并用重要度抽样方法随机抽取失谐量,然后进行机构动学仿真,得到机构的失谐动态响应并进行统计。仿真研究证明,该方法可用较少的计算时间得到柔性展开机构的失谐动态响应随机分布特性。     

A new methodology for Bayesian history matching using parallel interacting Markov chain Monte Carlo

This paper presents an innovative application of a new class of parallel interacting Markov chains Monte Carlo to solve the Bayesian history matching (BHM) problem. BHM consists of sampling a posterior distribution given by the Bayesian theorem. Markov chain Monte Carlo (MCMC) is well suited for sampling, in principle, any type of distribution; however the number of iteration required by the traditional single-chain MCMC can be prohibitive in BHM applications. Furthermore, history matching is typically a highly nonlinear inverse problem, which leads in very complex posterior distributions, characterized by many separated modes. Therefore, single chain can be trapped into a local mode. Parallel interacting chains is an interesting way to overcome this problem, as shown in this paper. In addition, we presented new approaches to define starting points for the parallel chains. For validation purposes, the proposed methodology is firstly applied in a simple but challenging cross section reservoir model with many modes in the posterior distribution. Afterwards, the application to a realistic case integrated to geostatistical modelling is also presented. The results showed that the combination of parallel interacting chain with the capabilities of distributed computing commonly available nowadays is very promising to solve the BHM problem.     

CATIA V5环境下基于产品数模的三坐标测量机测点自动采样

为了实现CAD与CAIP(计算机辅助测量规划)的集成,提出了一种基于产品三维模型的CMM(三坐标测量机)测量点采样方法。该方法从产品的IGES文件中有效识别型面形状与边界定义,采用均匀采样与依曲率变化二次采样相结合的策略,针对各个型面类型制定专门的信息处理机制,实现测量点的自动采样,并通过转换在参数平面中判断采样点与裁剪区域的隶属关系,以有效剔除无效采样点;通过典型飞机部件外形曲面的采样点生成结果表明,该方法能有效应用于飞机的测量规划中。     

Efficient conditional probability theorem and importance sampling-based methods for global reliability sensitivity analysis

Global reliability sensitivity (GRS) analysis can measure the effect of random inputs on failure probability (FP). To efficiently solve GRS, two conditional probability theorem (CPT)-based methods are proposed by combining adaptive Kriging (AK) with importance sampling (IS) (CPT-AK-IS) and combining AK with Meta-IS (CPT-AK-Meta-IS) respectively. Firstly, differentiation approximation and CPT are used to convert the estimation of conditional probability density function (PDF), which is required by the existing Bayes theorem-based methods, into that of a series of probabilities. Secondly, GRS can be directly estimated by the failure samples of IS, while the existing Bayes theorem methods based on IS need to transform the failure samples of IS into those of original PDF. Both the first and second strategies can reduce the computational complexity of solving GRS. Thirdly, by selecting a suitable differentiation interval with a proposed adaptive strategy, the estimation of a series of probabilities can be accurately completed as a byproduct of one IS based simulation for solving FP without additional computational cost. Finally, by introducing AK into IS and Meta-IS, it can reduce the number of evaluating performance function and the size of candidate sample pool simultaneously. These novelties are sufficiently verified by the presented examples.     

嵌入式冷库环境监控系统的设计

为了保证冷库内货物质量、维持其最佳储藏环境、提高冷库自动化水平,设计了一款冷库智能监控系统;系统以LPC2103为核心,利用传感器、信号调理电路进行数据采集,并结合软件实现对冷库环境参数的控制;分布式采样点的硬件布局克服了冷库环境参数不均匀,波动性大的缺陷,通过软件滤波算法与智能控制方法进一步屏蔽了信号传输的干扰,为控制设备提供了准确的指令;对温度参数进行记录与分析的实验结果表明,系统误差小并能够准确、及时的调节环境,因此系统结构简单,运行稳定,调控方便,能够有效保证储藏货物质量.     

软件无线电的研究现状综述

软件无线电因为它的开放性强,兼容性高,在航空航天,多频段、多用户、多模兼容以及互联系统有着广泛的应用前景;文中简要介绍了软件无线电的背景,着重对其关键技术及研究难点提炼与归纳,最后对软件无线电在军事通信、个人移动通信的应用与发展趋势进行了分析及总结。     

参数稀疏信号非均匀采样性能分析

提出了一种参数稀疏信号的时间交替周期非均匀采样方案。相对于非均匀采样,周期非均匀采样可以在降低系统复杂度的前提下依然保持较高的转换速率。通过仿真证明,非均匀采样样本的超分辨率特性可以提高非线性最小二乘周期图谱估计的重构性能。     

一种大尺度Gauss模糊的快速采样算法

针对大模板Gauss模糊的传统算法计算复杂度较高的缺点,提出了一种基于快速采样的新算法,将计算复杂度从O(N2)降到O(N)级别.利用极大似然估计理论,对传统Gauss模板和采样模板进行分析,给出了采样算法和传统算法的联系与区别.实验表明,新算法在极大的提高算法的速度的同时,可以将两者之间的能量误差控制在1%左右,当采样点等于10N时,人眼基本无法察觉两种算法的差异.