Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system

This paper presents a genetic algorithm (GA) for parallel redundancy optimization in series-parallel power systems exhibiting multi-state behavior, optimizing the reliability subject to constraints. The components are binary and chosen from a list of products available in the market, and are being characterized by their feeding capacity, reliability, cost and weight. System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curve. In GA, to handle infeasible solutions penalty strategies are used. Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of, both, feasible and infeasible regions. We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution. To evaluate system reliability, a fast procedure, based on universal generating function, is used. An example considering a multi-state series-parallel power system is solved considering both homogeneous and heterogeneous types of redundancy. Also an example considering price discounts is solved. The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically.     

One-dimensional optimal bounded-shape partitions for Schur convex sum objective functions

Consider the problem of partitioning n nonnegative numbers into p parts, where part i can be assigned n_i numbers with n_i lying in a given range. The goal is to maximize a Schur convex function F whose ith argument is the sum of numbers assigned to part i. The shape of a partition is the vector consisting of the sizes of its parts, further, a shape (without referring to a particular partition) is a vector of nonnegative integers (n₁,..., n_p) which sum to n. A partition is called size-consecutive if there is a ranking of the parts which is consistent with their sizes, and all elements in a higher-ranked part exceed all elements in the lower-ranked part. We demonstrate that one can restrict attention to size-consecutive partitions with shapes that are nonmajorized, we study these shapes, bound their numbers and develop algorithms to enumerate them. Our study extends the analysis of a previous paper by Hwang and Rothblum which discussed the above problem assuming the existence of a majorizing shape. This research is partially supported by ROC National Science grant NSC 92-2115-M-009-014.     

同种商品不同月份期货组合风险评价模型研究及应用

以期货合约的每一交易日的对数涨跌率来反映市场风险,借助VaR风险价值法,运用加权核估计技术(WKDE)和指数加权滑动模型(EWMA),建立了基于期货组合中持有头寸不同且可以进行风险对冲的期货组合市场风险非线性叠加评价模型,解决了同种商品、不同月份期货组合每一交易日最大损失的确定问题,并通过实证研究验证了模型的实用性.该模型的特点一是借助WKDE法预测组合中单个合约每一交易日涨跌率最大日亏损值,充分体现了期货合约涨跌率的实际走势,使VaR估计更加精确.二是通过动态迁移相关系数矩阵的计算保证了模型的精确性.采用EWMA模型预测动态变化的方差-协方差矩阵,从实证的角度得到更精准的动态迁移相关系数矩阵.三是考虑了组合中多头和空头不同头寸之间的风险对冲,避免了实际中期货组合风险的线性相加而造成放大风险或减少风险的不准确性,从而能较好地保证了模型的预测精度及准确性.四是通过基于风险非线性叠加建立的期货组合风险评价模型解决了SPAN系统中期货组合风险的线性叠加问题,从而得到更合理的组合风险预测值.     

动态车辆路径问题排队模型分析

分析了一类动态车辆路径问题,其中顾客需求以泊松流形式出现,现场服务时间服从一般分布.提出解决该问题的两种策略:顺序服务策略和中点改进策略,利用排队论、几何概率论等领域的知识分别求出了这两种策略的系统时间,并通过仿真数据实验验证了这两种策略的有效性.     

测试实数编码遗传算法的困难度

分析了遗传算法困难度测试在遗传算法研究中的重要意义. 鉴于当前这方面的研究集 中于二进制编码遗传算法,对实数编码遗传算法的困难度测试进行了分析. 探讨了实数编码遗 传算法困难度分析的适应值与距离相关系数测试法与基于随机游走模型的相关函数测试法, 提出了最佳一阶函数逼近测试法,做了大量实验,并根据实证分析结果对三种方法进行了比较 与评价.     

生产函数与成本函数的关系研究

本文给出了生产函数与成本函数之间的一般关系,并证明齐次生产函数与齐次成本函数之间存在着一种齐次对应关系:齐次生产函数在厂商的成本最小化决策下必然产生齐次的成本函数且齐次成本函数必然产生对应于齐次的生产函数。     

基于iCARE理论的《大学英语》课程定位研究与教改实践

《大学英语》是高校一门重要的公共基础课,厘清课程性质是推进教学改革的基础。在梳理不同时期《大学英语》教学大纲有关规定,借鉴国外高校现代语言课程定位基础上,提出《大学英语》课程定位是EGP,提高"交互密度"是保证学习效率和效果的关键。河北科技大学在多年教改实践基础上,创建了iCARE教学理论,并从六个方面推进《大学英语》课程改革,形成具有校本特色的《大学英语》教学模式,取得良好教改成效。     

高校青年志愿服务的发展路径探析——基于AGIL系统模型

高校青年志愿服务是社会志愿服务中重要的组成部分。高校青年志愿服务事业由四个子系统承担着相应的功能:资源系统发挥适应功能;高校志愿服务组织发挥目标达成功能;共青团发挥整合功能;育人系统发挥模式维持功能。在目前的高校志愿服务工作中,各项功能存在发挥不到位的问题。以AGIL理论分析模型为框架,可以从四个方面促进高校青年志愿服务提供路径支撑:资源系统做好社会化发展、高校志愿服务组织做好项目化管理、共青团做好枢纽化整合、育人系统做好价值化引领,从而发挥有机整体的作用。     

身份与功能交互下的民间信仰景观——以青海西宁湟中县扎麻隆凤凰山为例

改革开放以后,特别是21世纪以来,“文化带动旅游,旅游搞活经济”的模式在全国已经成为常见的经济形态。在这样的大背景下,许多新建的、基于神话和民间信仰打造的旅游景观,与当地居民及海内外香客对民间信仰宗教化的理解,以及国家的宗教政策形成了一种微妙的平衡。作为一处旅游景观,扎麻隆凤凰山的九天圣都筹建于20世纪末,现已成为区域内闻名的民间信仰道场。扎麻隆凤凰山身份与功能的交互与矛盾,以及扎麻隆村权力格局对其影响无疑是值得关注和研究的现象。