Interval robust design under contaminated and non normal data

Abstract

Robust parameter design (RPD) is an effective tool, which involves experimental design and strategic modeling to determine the optimal operating conditions of a system. The usual assumptions of RPD are that normally distributed experimental data and no contamination due to outliers. And generally the parameter uncertainties in response models are neglected. However, using normal theory modeling methods for a skewed data and ignoring parameter uncertainties can create a chain of degradation in optimization and production phases such that misleading fit, poor estimated optimal operating conditions, and poor quality products. This article presents a new approach based on confidence interval (CI) response modeling for the process mean. The proposed interval robust design makes the system median unbiased for the mean and uses midpoint of the interval as a measure of location performance response. As an alternative robust estimator for the process variance response modeling, using biweight midvariance is proposed which is both resistant and robust of efficiency where normality is not met. The results further show that the proposed interval robust design gives a robust solution to the skewed structure of the data and to contaminated data. The procedure and its advantages are illustrated using two experimental design studies.     

追踪场中宇宙学参量的演化

考虑的宇宙模型为在平坦空间的Friedmann—Robertson—Walker（FRW）宇宙，且宇宙中仅包含具有指数势的标量场与具有正压的暗物质的两种流体；较为详细地数值研究了在宇宙的追踪演化过程中，各个宇宙学参量随标度因子变化的规律，得到的结果不仅与目前观测结果相一致，而且还有助于理解在追踪下各个宇宙学参量的演化行为．     

Numerical performance of block thresholded wavelet estimators

Usually, methods for thresholding wavelet estimators are implemented term by term, with empirical coefficients included or excluded depending on whether their absolute values exceed a level that reflects plausible moderate deviations of the noise. We argue that performance may be improved by pooling coefficients into groups and thresholding them together. This procedure exploits the information that coefficients convey about the sizes of their neighbours. In the present paper we show that in the context of moderate to low signal-to-noise ratios, this block thresholding approach does indeed improve performance, by allowing greater adaptivity and reducing mean squared error. Block thresholded estimators are less biased than term-by-term thresholded ones, and so react more rapidly to sudden changes in the frequency of the underlying signal. They also suffer less from spurious aberrations of Gibbs type, produced by excessive bias. On the other hand, they are more susceptible to spurious features produced by noise, and are more sensitive to selection of the truncation parameter.     

冰箱蒸发器管路特性自动检测系统研制

介绍了冰箱中蒸发器和毛细管等管路器件特性自动测试系统的设计原理。运用管中气流的理论,分析了温度和压力对管路阻力综合参数的影响,提出了管路阻力综合参数和泄漏系数的检测方法。用差压、流量变送器和数字集成温度传感器,通过三星单片机进行数据采集和运算处理,研制了管路器件泄漏和阻力综合参数测试系统,样机运行的测试结果表明,该管路测试系统的设计是成功的。     

对机械专业课程体系改革的几点思考

高等教育的改革首先是教育观念的改革。改革的重要环节应是课程体系的全面重组与整体的优化。将先进的计算机技术引入到机械专业课程体系中来,使现代科技成果计算机技术与传统的机械设计、机械制造技术融为一个有机的整体,是机械专业课程体系改革的中心内容。     

生命在阴阳两个物质世界的存在形式及其转化是自组织行为

运用协同学理论研究生命在阴阳两个物质世界的存在形式及其转化,将我们对生命问题的认识提高到了一个新的层次,即生命本身是一个自组织系统,它的一切行为都是自组织行为。在生命自组织系统中,序参量——潜意识(广义)即阴性物质世界,就是生命程序和密码编制的总设计师。这也从另一个角度解释了阴性物质世界产生和存在的必然性。生命的自组织系统与宇宙的组织系统之间存在着微妙的关系,如无人控制与人工控制、演进与计划、自发与自觉等之间的关系。在实践中不能肯定一个而否定另一个,而是要看到二者具有互补性,是相辅相成的。     

分数阶自相关和FrFT的LFM信号参数估计

基于分数阶自相关和分数阶傅里叶变换的特点,提出了一种LFM信号检测与参数估计方法。相对分数阶傅里叶二维扫描法和匹配傅里叶变换,所提方法将检测与参数估计的二维搜索变为一维搜索,快速实现信号检测和参数估计,在多分量LFM信号情况下借助“Clean”的方法来抑制强分量对弱分量的干扰。计算机仿真表明了该算法在低信噪比多分量LFM信号检测与参数估计中的有效性。     

基于参数识别的钢筋混凝土简支梁桥静力模型修正技术

以动力模型修正的原理及方法为基础，提出用较精确的静力试验数据进行静力模型修正的概念，并详细介绍了利用最小二乘法对钢筋混凝土简支梁桥进行参数识别的静力模型修正方法。经过动力修正的有限元模型只可用于结构的动态分析，而不可用于静态分析，所以用静力试验数据修正的模型做结构破坏试验数值仿真具有广阔的应用前景。     

在经典测试理论框架中重新定义项目难度

在分析经典测试理论中项目难度的易度指数定义P、易度指数补定义q以及△定义对于成绩测试的不合适性和项目反应理论中项目难度与受考能力本质一致性的基础上，提出了经典理论框架中项目难度的两种新定义：固定基准参照点定义和任意基准参照点定义，解决了经典测试理论中测试成绩依赖于特定试卷这一致命缺陷的根源；还提出了项目难度的样本独立性定义，使项目参数的计算不依赖于不受考样本．     

Small confidence sets for the mean of a spherically symmetric distribution

Summary.  Suppose that X has a k -variate spherically symmetric distribution with mean vector θ and identity covariance matrix. We present two spherical confidence sets for θ , both centred at a positive part Stein estimator     . In the first, we obtain the radius by approximating the upper α -point of the sampling distribution of     by the first two non-zero terms of its Taylor series about the origin. We can analyse some of the properties of this confidence set and see that it performs well in terms of coverage probability, volume and conditional behaviour. In the second method, we find the radius by using a parametric bootstrap procedure. Here, even greater improvement in terms of volume over the usual confidence set is possible, at the expense of having a less explicit radius function. A real data example is provided, and extensions to the unknown covariance matrix and elliptically symmetric cases are discussed.