Circuit optimization via sequential computer experiments: design of an output buffer

In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit's performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator's deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.     

宏观连续交通流数值模拟

应用交通流宏观连续模型,模拟上下游信号灯作异步周期变化的协调信号灯路段的交通流动,经数值计算比较,选择改进Ｍｕｒｍａｎ格式求解．结果表明：改进Ｍｕｒｍａｎ格式能准确捕捉交通流中无振荡“激波”位置．     

Retirement patterns in Hong Kong: a censored regression analysis

This paper provides an overview of retirement patterns in Hong Kong on the basis of limited data. A censored regression model is used to infer the retirement age from people‘s current retirement status and their current age. This model is equivalent to a restricted probit model, and the interpretation of parameters is straightforward. The results clearly show a negative income effect on the retirement decision. The retirement age seems to be positively related to lifetime earnings but negatively related to the rate of decline of earnings with age. JEL classification: C24, J14, J26 Received May 6, 1996 / Accepted February 5, 1997     

Estimation of critical points in the mixture inverse Gaussian model

The maximum likelihood estimation for the critical points of the failure rate and the mean residual life function are presented in the case of mixture inverse Gaussian model. Several important data sets are analyzed from this point of view. For each of the data sets, Bootstrapping is used to construct confidence intervals of the critical points.     

Dietary and Inhalation Intake of Lead and Estimation of Blood Lead Levels in Adults and Children in Kanpur, India

This research was initiated to study lead levels in various food items in the city of Kanpur, India, to assess the dietary intake of lead and to estimate blood lead (PbB) levels, a biomarker of lead toxicity. For this purpose, sampling of food products, laboratory analysis, and computational exercises were undertaken. Specifically, six food groups (leafy vegetables, nonleafy vegetables, fruits, pulses, cereals, and milk), drinking water, and lead air concentration were considered for estimating lead intake. Results indicated highest lead content in leafy vegetables followed by pulses. Fruits showed low lead content and drinking water lead levels were always within tolerable limits. It was estimated that average daily lead intake through diet was about 114 microg/day for adults and 50 microg/day in children; tolerable limit is 250 microg/day for adults and 90 microg/day for children. The estimated lead intakes were translated into the resultant PbB concentrations for children and adults using a physiologically-based pharmacokinetic (PBPK) model. Monte Carlo simulation of PbB level variations for adults showed that probability of exceeding the tolerable limit of PbB (i.e.,10 microg/dL) was 0.062 for the pre-unleaded and 0.000328 for the post-unleaded gasoline period. The probability of exceeding tolerable limits in PbB level was reduced by a factor of 189 in the post-unleaded scenario. The study also suggested that in spite of the introduction of unleaded gasoline, children continue to be at a high risk (probability of exceeding 10 microg/dL = 0.39) because of a high intake of lead per unit body weight.     

我国财务危机公司投资行为的财务特征分析

在引入一个简化的理性财务危机公司投资模型的基础上,论文分析了企业陷入财务危机后,债权人与债务人之间在期权博弈过程中,企业在投资策略的选择上会偏离正常的投资行为的财务特征。在此基础上,重点分析了我国经济转轨时期财务危机公司严重地过度投资和在债务重组过程中的盲目性、短期性和功利性及其形成的主要原因。企业的过度投资是内部人控制和银行债权弱化的具体体现,虚假的债务重组是将危机转嫁给企业债权人的真正动因。     

股指期货价格非线性均值回复特性实证研究

用ESTAR 模型对香港恒生指数期货933、9312、943 合约及S&P 500 指数期货933、9312、 943 合约价格进行了实证研究,发现恒生指数期货933、9312 合约实际价格呈现非线性均值回 复,而其他各合约实际价格呈现线性均值回复. 结论:由于股票现货没有卖空机制使套利成本 较大,抑制了套利行为,导致期货合约实际价格呈现非线性. 在股票现货没有卖空机制的市场 条件下,单向套利的机会要比成熟的市场更多.     

基于模糊规划的QFD 系统参数确定方法

针对QFD 系统的内在模糊性,运用带有对称三角形模糊系数的模糊规划理论,提出了一 种确定QFD 系统参数的方法. 并通过工程特性目标值的规范化,定义产品开发总成本函数、工 程特性改进成本函数及改进成本系数等概念,建立了一个QFD 规划模型. 仿真结果表明,该模 型能够帮助开发人员在不确定的、模糊条件下有效确定关联函数及自相关函数,优化顾客需求 的满意水平,确定工程特性目标值,使新开发/ 改进的产品顾客满意度赶上或超过目标市场上 的竞争企业,并满足开发预算约束.     

Risk Modeling, Assessment, and Management of Lahar Flow Threat

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.     

Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.