基于主成分分析的DEM粗差检测

为探讨误差的空间分布特性对数宁高程模型(DEM)粗差检测率的影响,建立了独立粗差模型和相关粗差模型,并模拟了不同粗差率(0.2%～3.0%)的数据.将随机分布的粗差加入DEM中,采用基于主成分分析的粗差检测算法进行了试验.结果表明,无论粗差是否空间相关,随粗差率增大,检测率均下降.对于独立分布的粗差,当粗差率小于1.0%时,基本可以定位所有污染数据;而对于空间相关的粗差,当粗差率等于1.0%时,检测率不足50%.可见,粗差的空间相关性及较大的粗差率均会降低基于主成分分析的粗差检测算法的检测率.     

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现行出租车系统仅以对空载车辆在城市中的定位及可能性安排作为其主要调度方式,造成出租车营运效率低、空载率高、乘客等待时间长且满意度不高等诸多问题.本研究采用浮动车(FCD)数据和地理建模仿真平台,使用地理信息系统(GIS)技术来定量分析给定城市出租车系统的完备以及与需求的合适程度,总结出一套基于出租车运营的系统完备度评价体系,从而实现城市出租车系统的良好组织与管理.该体系主要从城市路网、乘客以及驾驶员等多维指标,来量化给定城市出租车系统的运营状态和效率,合理制定运营调度策略.系统基于深圳市实际出租车数据,在ArcGIS 10平台实现且可按预期功能对城市出租车系统从多方位进行评价,为进一步管控奠定良好的基础与指导意义.     

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.