Reliability analysis of a repairable k-out-of-n system with some components being suspended when the system is down

A k-out-of-n system with independent exponential components is investigated. It is assumed that some working components are suspended as soon as the system is down, repair starts immediately when a component fails and repair times are independent and exponentially distributed. Formulas for various reliability indices of the system including mean time between failures, mean working time in a failure–repair cycle, and mean down time in a failure–repair cycle are derived.     

开放标准LSB3.0的深入剖析

LSB标准是最重要的开放标准之一。文中介绍了LSB3.0标准规范;基于LSB3.0的发行版组件;构建LSB3.0认证的应用程序;LSB3.0的标准认证,并以实际的例子详细讲解了如何使用LSB3.0的开发环境和测试工具。最后对LSB标准的前景作了展望。     

Robust estimation and hypothesis testing under short-tailedness and inliers

Estimation and hypothesis testing based on normal samples censored in the middle are developed and shown to be remarkably efficient and robust to symmetric shorttailed distributions and to inliers in a sample. This negates the perception that sample mean and variance are the best robust estimators in such situations (Tiku, 1980; Dunnett, 1982). Professor Emeritus, Department of Mathematics and Statistics, McMaster University, Professor Emeritus, Department of Mathematics and Statistics, McMaster University,     

Conditional phase-type distributions for modelling patient length of stay in hospital

The proportion of elderly in the population is continuing to increase, placing additional demands on highly competitive medical budgets. The management of the care of the elderly within hospitals can be assisted by the accurate modelling of the length of stay of patients in hospital. This paper uses conditional phase‐type distributions for modelling the length of stay of a group of elderly patients in hospital. The model incorporates the use of Bayesian belief networks with Coxian phase‐type distributions, a special type of Markov model that describes the duration of stay in hospital as a process consisting of a sequence of latent phases. The incorporation of the Bayesian belief network in the model permits the inclusion of additional patient information which may provide a better understanding of the system, in particular the incorporation of any potential causal information that may exist in the data.     

On optimal pairwise linear classifiers for normal distributions: the d-dimensional case

We consider the well-studied pattern recognition problem of designing linear classifiers. When dealing with normally distributed classes, it is well known that the optimal Bayes classifier is linear only when the covariance matrices are equal. This was the only known condition for classifier linearity. In a previous work, we presented the theoretical framework for optimal pairwise linear classifiers for two-dimensional normally distributed random vectors. We derived the necessary and sufficient conditions that the distributions have to satisfy so as to yield the optimal linear classifier as a pair of straight lines.In this paper we extend the previous work to d-dimensional normally distributed random vectors. We provide the necessary and sufficient conditions needed so that the optimal Bayes classifier is a pair of hyperplanes. Various scenarios have been considered including one which resolves the multi-dimensional Minsky’s paradox for the perceptron. We have also provided some three-dimensional examples for all the cases, and tested the classification accuracy of the corresponding pairwise-linear classifier. In all the cases, these linear classifiers achieve very good performance. To demonstrate that the current pairwise-linear philosophy yields superior discriminants on real-life data, we have shown how linear classifiers determined using a maximum-likelihood estimate (MLE) applicable for this approach, yield better accuracy than the discriminants obtained by the traditional Fisher's classifier on a real-life data set. The multi-dimensional generalization of the MLE for these classifiers is currently being investigated.     

变压器冲击电压分布计算在纵绝缘设计中的应用

要使电力变压器能够安全、可靠地运行，合理设计变压器内部绝缘结构至关重要，特别是变压器线圈的绝缘必须能可靠地承受住大气过电压、长期工作电压、操作过电压和暂态过电压的作用，分析了变压器线圈在遭受到冲击电压时产生电磁振荡过电压和感应过电压的原因，对电力变压器波过程进行计算与仿真，运用波过程计算软件进行变压器绝缘结构设计，最后指出波过程计算是变压器纵绝缘设计的重要依据。     

Least possible time paths in stochastic, time-varying networks

In this paper, two computationally efficient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The first algorithm determines the least possible time path from each node for each departure time interval, the least possible travel time and a lower bound on the associated probability of the occurrence of this travel time. The second algorithm determines up to k least possible time paths, the associated travel times and the corresponding probabilities of occurrence of the travel times (or a lower bound on this probability). No such efficient algorithms for determining least time paths in stochastic, time-varying networks exist in the literature.     

内部过电压试验数据统计分析方法的研究

应用数理统计的原理和方法，研究开发了内部过电压统计分析软件．它能够快捷而准确地处理大量试验数据，从而得到多项过电压参数的概率分布规律及相关分析结果，并自动绘出多种曲线和计算图表．使过电压统计分析达到了新水平．     

高压输电线路用复合绝缘子电位和电场分布的计算与改善

采用模拟电荷法和有限元法计算高压线路用复合绝缘子的电场分布和电位分布，讨论了改善复合绝缘子电场和电位分布的措施。为验证计算结果，还进行了复合绝缘子的电位分布试验。     

Optimal Design of Blocked and Split-Plot Experiments for Fixed Effects and Variance Component Estimation

In general, modeling data from blocked and split-plot response surface experiments requires the use of generalized least squares and the estimation of two variance components. The literature on the optimal design of blocked and split-plot response surface experiments, however, focuses entirely on the precise estimation of the fixed factor effects and completely ignores the necessity to estimate the variance components as well. To overcome this problem, we propose a new Bayesian optimal design criterion which focuses on both the variance components and the fixed effects. A novel feature of the criterion is that it incorporates prior information about the variance components through log-normal or beta prior distributions. The resulting designs allow for a more powerful statistical inference than traditional optimal designs. In our algorithm for generating optimal blocked and split-plot designs, we implement efficient quadrature approaches for the numerical approximation of the new optimal design criterion. Supplementary materials for this article are available online.