低航速下舰船声学故障检测方法研究

舰船在低速航行时,机械振动引起的结构噪声往往成为舰船的主要噪声源,声学故障检测问题可转化为机械设备振动状态监测问题。本文建立了基于PCA的测点级声学故障检测模型,并利用模型试验对检测模型进行了验证和评估。结果表明,通过对SPE和T2统计量的监测,检测模型可以很好地检测出所模拟的故障,验证了文中声学故障检测方法的可行性。     

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以道路网络的路段流量为基础进行OD分布矩阵估计.与以往文献不同的是本文应用了多层前馈神经网络的方法.由于路段流量与相关的OD矩阵分布之间存在连续性关系,这为神经网络模型的逼近特性提供了可行性.本文的方法适用于OD分布矩阵的实时校正.在已知OD分布矩阵的前提下,对两种情境———试验网络和实际Naples农村道路网进行仿真分析.主成分分析法的应用减少了变量个数并有利于改进输入数据.估计误差相对较低,与分析方法相反的是处理的时间几乎是实时的,因此这种方法可用于动态交通管理.本文的神经网络方法在误差和计算时间方面优于传统商业软件得到的OD估计结果.     

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以建设节约型交通为发展目标,以科学性、系统性以及可操作性为原则,在充分考虑投资者、管理者与使用者意见的基础上将车辆运行速度、行车延误、通行能力、交通安全、交通噪声污染,以及车辆尾气排放相结合构成ETC交通效益评价指标体系.通过定性与定量相结合的方法,给出各评价指标的量化值.运用数据包络分析法(DEA)对不同交通流状态下ETC建设项目的交通效益进行超效率分析.最后通过实例验证不仅给出了不同交通量条件下ETC建设项目的交通效益超效率量化值,而且将不同交通量所对应的效益值相对比,从而找出导致效益较低的根本原因,并有针对性地采取改进措施.     

苏州市居民幸福指数调查统计与效率分析

随着GDP的不断增长，以及居民收入的不断提高，居民的生活水平也随之不断改善。苏州地处长江三角洲，经济发展水平居全国前列，高GDP。增长情况下，分析居民的幸福指数意义重大。本文以苏州市为例，首先构建了幸福指数的评价指标体系，利用问卷调查与统计数据定量分析苏州市居民生活幸福指数。进而利用非径向DEA模型对居民生活中的物质投入与精神产出进行效率评价，最后针对计算结果作了原因分析。     

PCA法在武警部队摩托化输送保障能力评估中的应用

首先介绍了PCA方法的原理和计算步骤，然后构建武警部队摩托化输送保障能力评估指标体系，最后利用PCA方法进行评估，并对总体评估对象进行排名，对今后部队摩托化输送保障工作具有一定参考意义。     

PCA8538在电动汽车仪表盘上的应用与调试

提出了一款液晶显示器在电动汽车仪表盘上的应用方案。以LM3S811为控制核心,介绍了PCA8538液晶显示屏在仪表盘信息显示。使用I2C驱动PCA8538,给出了语音电路和矩阵键盘接口电路,最后通过VC设计了调试PCA8538的界面。该仪表盘比传统的电动汽车仪表盘具有更好的显示效果和更多的显示信息。     

基于PCA和DEA的江苏省物流业碳排放效率评价

根据物流行业的特点并结合江苏省的实际情况,从物流业总体情况、交通运输、信息及人力资源等几大方面选取了8个投入指标,并以物流业增加值及货运汽车碳排放量为输出指标,构建出江苏省碳排放效率评价体系。针对传统方法的局限,利用主成分分析法对评价指标进行降维,并结合非期望产出处理等方法,再运用DEA方法对江苏省物流业碳排放的效率进行评价。研究表明,2010年江苏省13个城市中,南京、无锡等6个城市在综合效率、纯技术效率及规模效率上都达到了DEA有效,规模报酬不变,碳排放效率是最好的;而其他几个城市没有达到DEA有效,因此对这些城市的松弛变量分布进行分析并有针对性的提出了改善建议,旨在促进其物流业的可持续发展。     

Seaport performance analysis using robust non-parametric efficiency estimators

Abstract

Seaport services have been developing year on year, playing an increasingly important role in the economy and the transportation markets of each country. This increases interest in measuring their performance. This paper evaluates the performance of a set of European seaports by means of robust non-parametric approaches. Using order-m and bootstrap procedures, the major problems of traditional data envelopment analysis and free disposal hull methods – concerning noise, presence of outliers and the ‘curse of dimensionality’ – are overcome. With this study considerable levels of inefficiency were found in the European seaports sector.     

A multiple criteria approach to two-stage data envelopment analysis

Two-stage data envelopment analysis (DEA) models are commonly used in the evaluation and benchmarking of sustainable operations and processes across multiple research fields. To date, however, little attention has been given to the unrealistic weight distribution and weak discrimination power in the modeling and evaluation of the two-stage sustainable operations when using two-stage DEA models. In order to overcome this methodological weakness, we use the multiple criteria DEA (MCDEA) approach in the evaluation of the two-stage processes. The outcome is a multiple criteria two-stage DEA model which yields more realistic weights for the inputs and outputs and thus has better discrimination power than traditional two-stage DEA models. The developed model is tested and validated by assessing the sustainable design performances of a sample of car product designs.     

A bus route evaluation model based on GIS and super-efficient data envelopment analysis

When compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single factor and comprehensive evaluation models. Finally, the bus system in Shenzhen, China is used as a case study. The comparable significant results validate the capability of the proposed model.