Problems of spatial linkage of a geo-referenced Demographic and Health Survey (DHS) dataset to a population census: A case study of Egypt

GPS coordinates are increasingly available as spatial references on population surveys in the developing world, where high-resolution address and street mapping are absent. This potentially offers opportunities to enhance national census data by spatial linkage with survey sources. The paper explores the use of GPS-referenced Demographic and Health Survey (DHS) data in combination with census data in Egypt and identifies errors in coordinate referencing. The study develops a practical approach to the measurement of spatial uncertainty in this situation and assessment of its impact on data linkage. The analysis specifically addresses the analytical implications at three different spatial scales and is internationally relevant to the handling of GPS-referenced DHS data in GIS.     

Basic study on the measuring of fatigue by the minimum audible pressure

Abstract

Nowadays, human senses of vision and hearing receive many stimulations. In particular, VDT work is commonly performed by people of all ages. Therefore, in order to regulate working hours we need to measure the fatigue due to VDT work. The current measuring system depends on the flicker value (CFF). This report describes a new measuring system which depends on the measurement of the fluctuation in the minimum audible pressure (MAP) while using the increase of threshold value as an index of fatigue, and the results of the related experiment. In Test I, the test sound was set at the pure sound of 1000Hz. In Test 2, an ordinary hearing acuity inspection room was used, using the method of limit measurement and headphones (Telephonies, TDH39, MX41/AR). As a result, it was verified that the most suitable rate of increasing and decreasing audible pressure was by 2 dB or 1 dB.

It was proved from these tests that the minimum audible pressure could be used satisfactorily as a practical index for absolute measurement of hearing sense fatigue.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

This paper presents an intelligent decision support system for evaluating and selecting specific ships under uncertainty. A task-oriented procedure is developed for determining the relative importance of the evaluation and selection criteria with respect to a specific shipping task. A fuzzy multicriteria analysis algorithm is developed for determining the overall performance of each ship across all the selection criteria and their associated sub-criteria. An intelligent decision support system capable of integrating the developments above is proposed for facilitating the ship evaluation and selection process. An example is presented to demonstrate the effectiveness of the proposed intelligent decision support system.     

核电站重要仪表测量通道的不确定度计算方法

对核电厂重要仪表测量通道的不确定度计算的相关国内核安全法规要求、最新国际标准进行了研究,提出一套测量通道不确定度计算的方法。总结完善了计算方法中不确定度通道的类型、国产化仪表设备的不确定度项区分、环境条件区分以及整定值的确定等内容,演示了采用该方法进行仪表测量不确定度计算的过程,并给出了计算结果。通过相关计算,验证了该方法对维护核电厂原有安全基准的有效性。相比较于同类计算方法获得的结果,新计算方法具有明显的优越性。     

铠装热电偶校准用精密热电偶校验炉

为满足最新颁布的铠装热电偶校准规范对校准用校验炉的要求,研发了精密热电偶校验炉.运用内外二级温度控制及插孔安装方式,使校验炉在进行热电偶校准时具备极佳的技术性能.实际应用表明,精密热电偶校验炉完全可以应用于Ⅰ级工业热电偶的校准.     

基于LMI的近空间飞行器H∞控制器的设计

近空间飞行器具有广泛的军事用途.飞行控制系统是保证近空间飞行器飞行安全、可靠、有效完成复杂战术/战略任务的关键系统之一.由于飞行控制系统存在多种不确定性,飞行器本身也易于受到大气紊流等强干扰的作用,设计满足特定性能要求的鲁棒控制器十分重要.本课题基于LMI将近空间飞行器的控制问题转化为线性矩阵不等式系统的可行性问题,以有效推动LMI方法在飞行控制领域中的应用.     

Validation of models with multivariate output

This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading.     

基于带区间值的产生式规则及其模糊推理机制研究

在分析了传统的产生式规则的三种不确定性的基础上，给出了基于模糊数的产生式规则结构，并量化了结构中的各重要参数，描述了知识表示和推理的关系，并在此基础上提出了基于模糊数产生武规则的模糊推理机制。     

一类时滞线性系统的鲁棒非脆弱控制器设计

基于线性矩阵不等式（LMI）方法,研究一类时滞线性系统的鲁棒非脆弱控制器的设计问题.在假定控制器增益扰动范数有界的前提下,对不确定时滞线性系统设计了鲁棒非脆弱状态反馈控制器,同时以一个LMI的形式给出了状态反馈控制器存在的充分条件,而且该LMI是与时滞相关的,因而具有较小的保守性.数值例子证明了该设计方法的有效性和可行性.