Reliability Analysis of Flexible Manufacturing Cells Based on Triangular Fuzzy Number

In most of flexible production schemes, the flexible manufacturing cells (FMCs) are more economical and feasible. So, an important task is to establish the correct reliability analysis model for the FMCs. However, with the increasing of system complexity, some reliability analysis modes can hardly describe the actual situation. Besides, due to the lack of test-data and field-data during the design stage of FMC' system, the reliability modeling will be more complicated. In order to deal with the deficient data and the uncertainty occurred from analysis and judgment, this article analyzes the reliability of FMCs system through the method of fuzzy fault tree, which is based on triangular fuzzy membership. At last, a practical example is illustrated. The reliability analysis model indicates that it can offer a diagnostic tool for FMCs system and improve the efficiency of operation and production in FMCs system.     

A Clustering Method for Categorical Ordinal Data

Often, categorical ordinal data are clustered using a well-defined similarity measure for this kind of data and then using a clustering algorithm not specifically developed for them. The aim of this article is to introduce a new clustering method suitably planned for ordinal data. Objects are grouped using a multinomial model, a cluster tree and a pruning strategy. Two types of pruning are analyzed through simulations. The proposed method allows to overcome two typical problems of cluster analysis: the choice of the number of groups and the scale invariance.     

An Extended Mixture Distribution Survival Tree for Patient Pathway Prognostication

Mixture distribution survival trees are constructed by approximating different nodes in the tree by distinct types of mixture distributions to improve within node homogeneity. Previously, we proposed a mixture distribution survival tree-based method for determining clinically meaningful patient groups from a given dataset of patients’ length of stay. This article extends this approach to examine the interrelationship between length of stay in hospital, outcome measures, and other covariates. We describe an application of this approach to patient pathway and examine the relationship between length of stay in hospital and/or treatment outcome using five-years’ retrospective data of stroke patients.     

TREE-BASED REGRESSION FOR A CIRCULAR RESPONSE

ABSTRACT

The application of conventional statistical methods to directional data generally produces erroneous results. Various regression models for a circular response have been presented in the literature, however these are unsatisfactory either in the limited relationships that can be modeled, or the limitations on the number or type of covariates admissible. One difficulty with circular regression is devising a meaningful regression function. This problem is exacerbated when trying to incorporate both linear and circular variables as covariates. Due to these complexities, circular regression is ripe for exploration via tree-based methods, in which a formal regression function is not needed, but where insight into the general structure and relationship between predictors and the response may be obtained. A basic framework for regression trees, predicting a circular response from a combination of circular and linear predictors, will be presented.     

Comment on “Issues Involved With the Seasonal Adjustment of Economic Time Series” by William R. Bell and Steven C. Hiilmer

This article investigates the existence of multiple regimes in the U.S. economy during the 1923—1991 period. A technique known as regression tree analysis is applied to search for splits in the data, if any exist, rather than choosing a splitting point a priori as has been done in previous work. Using this technique, strong evidence for the existence of nonlinear behavior of U.S. output is found over this period. Monte Carlo results are presented to assess the significance of the regime changes that are found.     

A nonparametric Bayesian approach to copula estimation

We propose a novel Dirichlet-based Pólya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the flexibility of the D-P tree prior ensures its consistency in copula estimation, thus able to detect more subtle and complex copula structures than earlier nonparametric Bayesian models, such as a Gaussian copula mixture. Furthermore, the continuity of the imposed D-P tree prior leads to a more favourable smoothing effect in copula estimation over classic frequentist methods, especially with small sets of observations. We also apply our method to the copula prediction between the S&P 500 index and the IBM stock prices during the 2007–08 financial crisis, finding that D-P tree-based methods enjoy strong robustness and flexibility over classic methods under such irregular market behaviours.     

铅树生长机理的探讨

本文作者将原铅树实验装置的三部分[即:Cu—Na_2S(Ⅰ),Cu—不含 P_b~(2 )酸性硅胶(Ⅱ),Cu—含 P_b~(2 )酸性硅胶(Ⅲ)]分别组成Ⅰ—Ⅱ,Ⅱ—Ⅲ,Ⅰ—Ⅲ三个原电池,通过测定各电极电势随时间的变化情况,探讨了铅树的生长机理。     

关于防范经销商资信风险的探讨

经销商是生产企业在营销渠道上的一个关键成员,两者既是合作者,同时,在某种程度上也是竞争者.由此产生的风险,应成为生产企业关注的重要问题.本文从经销商的资信风险方面入手,详细分析了经销商的资金风险和信用风险的组成要素及特点,并提出了解决办法--利用分析树将风险量化,为决策提供科学工具.     

The Minimal Spanning Tree Preservation Approaches for DNA Multiple Sequence Alignment and Evolutionary Tree Construction

In this paper, we propose a new criterion, namely the minimal spanning tree preservation approach, for both of the DNA multiple sequence alignment and the construction of evolutionary trees. It is our aim to keep the proximity information among the sequences or species via our approach. The basic idea behind this approach is that those distances among species which are close to one another are more informative than the others.