散斑干涉计量图像自动处理系统

着重从光学系统和图像处理系统两部分来论述一种自动处理系统。在光学系统部分,论述散斑计量的基本原理和方法,并给出了测面內和离面位移的计真公式;图像处理部分,主要就散斑条纹图的处理提出了一种新方法,即密度滤波法及相应的条纹中心特征提取方法。实验结果表明:该自动处理系统具有一定的理论和实用价值。     

GPS载波相位的周跳检测方法

讨论了ＧＰＳ载波相位定位中出现的周跳问题及其解决途径，将其分为静态和动态两种情况。在静态环境下根据卫星运动的特点采用最小二乘拟合的方法来检测，在动态环境下利用卡尔曼滤波对载波相位残差进行统计检验，并结合双频相位电离层残差组合进行两级周跳检测，可以提高周跳的检测能力。利用实测的相位数据，上述方法均得到了验证。     

改进实孔径雷达的方位分辨力

实孔径毫米波FMCW成像雷达系统中方位分辨力与距离分辨力不匹配,影响了成像质量。本文从分析“波束效应”的本质原因出发,提出了一种利用卷积反演—广义逆滤波法,对波束进行“伪压缩”是有效地提高成像雷达方位分辨力的方法。文中给出了对雷达实测数据作“伪压缩”处理的结果,可见处理后的方位分辨力提高约4倍。     

非营利机构服务质量传递系统的过滤模型

非营利机构所提供的产品基本都是无形的服务。而在服务质量传递系统中,有诸多因素影响着服务质量传递的效率,并最终使顾客感知服务质量的效果有所损失。通过引入简化模型和基本函数,建立和求解微分方程,构造出服务质量传递系统的动态模型,并对系统的传递过滤和感知过滤两种效应的效果进行了分析,为非营利机构及其他服务提供者准确衡量和把握顾客心理,实施服务补救,改善服务质量传递的效率和效果,提升非营利机构营销绩效奠定了理论基础。     

Combined Density Nowcasting in an Uncertain Economic Environment

We introduce a combined density nowcasting (CDN) approach to dynamic factor models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian sequential Monte Carlo method which rebalances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on U.S. real-time data of 120 monthly variables, indicate that CDN gives more accurate density nowcasts of U.S. GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.     

Continuous-time threshold autoregressions with jumps: Properties,estimation, and application to electricity markets

Continuous-time autoregressive processes have been applied successfully in many fields and are particularly advantageous in the modeling of irregularly spaced or high-frequency time series data. A convenient nonlinear extension of this model are continuous-time threshold autoregressions (CTAR). CTAR allow for greater flexibility in model parameters and can represent a regime switching behavior. However, so far only Gaussian CTAR processes have been defined, so that this model class could not be used for data with jumps, as frequently observed in financial applications. Hence, as a novelty, we construct CTAR processes with jumps in this paper. Existence of a unique weak solution and weak consistency of an Euler approximation scheme is proven. As a closed form expression of the likelihood is not available, we use kernel-based particle filtering for estimation. We fit our model to the Physical Electricity Index and show that it describes the data better than other comparable approaches.