一个评价光谱估计精度的新参数

研究了对多光谱成像系统进行光谱估计的精度评价的问题。在给出光谱估计偏指数定义的基础上,提出了多光谱图像获取系统的光谱估计性能的评价的新方法——系统光谱匹配偏度指数法。该方法用一个指标来定量评价两光谱向量的光谱匹配程度,避免了以往评价参数的复杂性,同时反映了光谱反射率估计曲线实际测量曲线之间的差异程度。采用一典型多光谱相机对Macbeth色卡的24个典型光谱仿真估计所得的24个光谱对数值模拟,验证了光谱反射率估计匹配偏度指数能全面的反映光谱反射率的估计精度的设想;并拿17个多光谱估计系统的仿真估计的光谱数据,计算了它们的系统光谱估计偏度指数,计算结果有很好的一致性。结果还显示,系统光谱估计偏度指数还可用作对多光谱相机光谱估计性能进行等级划分的依据。     

基于目标和点扩展函数联合估计的点源目标图像近视解卷积

在传统的基于波前探测的解卷积方法中,由波前探测得到的点扩展函数被认为是精确的,并用维纳滤波进行复原,但是点扩展函数不可避免地存在误差,所以最终的复原目标图像质量不佳.为了解决该难题,提出了基于目标和点扩展函数联合估计的图像近视解卷积算法.它运用了点扩展函数和目标的先验信息,对点扩展函数和目标进行了规整和进一步约束,从而得到更优的恢复图像质量.对该方法的原理和实现过程进行了阐述,并将其运用于室内点源目标数据中.实验结果证明,与维纳滤波方法相比,该方法使图像恢复的效果得到明显改善.     

Stokes方程的压力梯度局部投影间断有限元法

本文对定常的Stokes方程提出了一种新的间断有限元法,通过将通常的间断Galerkin有限元法与压力梯度局部投影相结合,建立了一个稳定的间断有限元格式,对速度和压力的任意分片多项式空间P_l(K),P_m(K)的间断有限元逼近证明了解的存在唯一性,给出了关于速度和压力的L~2范数的最优误差估计.     

利用频域β-warping变换的浅海目标航向估计方法

对于单一静止的观测平台,针对直航运动的目标,提出了一种利用频域β-warping变换的浅海目标航向估计方法。该方法利用频域β-warping变换从简正波互相关项中提取与目标距离呈线性关系的脉冲时延,进而估计距离特征量,并根据几何关系建立了目标航向、目标方位和距离特征量的数学模型,利用渐近无偏最小二乘方法对目标航向进行估计。数值仿真和实验数据处理结果表明:在浅海水平不变波导远场条件下,该方法可以对直航运动的目标进行可靠地航向估计,航向估计性能与观测时间内的平均方位变化率密切相关。     

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande,MS, Brazil

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.     

Targeted L1-Regularization and Joint Modeling of Neural Networks for Causal Inference

The calculation of the Augmented Inverse Probability Weighting (AIPW) estimator of the Average Treatment Effect (ATE) is carried out in two steps, where in the first step, the treatment and outcome are modeled, and in the second step, the predictions are inserted into the AIPW estimator. The model misspecification in the first step has led researchers to utilize Machine Learning algorithms instead of parametric algorithms. However, the existence of strong confounders and/or Instrumental Variables (IVs) can lead the complex ML algorithms to provide perfect predictions for the treatment model which can violate the positivity assumption and elevate the variance of AIPW estimators. Thus the complexity of ML algorithms must be controlled to avoid perfect predictions for the treatment model while still learning the relationship between the confounders and the treatment and outcome. We use two NN architectures with an L1-regularization on specific NN parameters and investigate how their certain hyperparameters should be tuned in the presence of confounders and IVs to achieve a low bias-variance tradeoff for ATE estimators such as AIPW estimator. Through simulation results, we will provide recommendations as to how NNs can be employed for ATE estimation.     

Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems

The power-delay profile (PDP) estimation of wireless channels is an important step to generate a channel correlation matrix for channel linear minimum mean square error (LMMSE) estimation. Estimated channel frequency response can be used to obtain time dispersion characteristics that can be exploited by adaptive orthogonal frequency division multiplexing (OFDM) systems. In this paper, a joint estimator for PDP and LMMSE channel estimation is proposed. For LMMSE channel estimation, we apply a candidate set of frequency-domain channel correlation functions (CCF) and select the one that best matches the current channel to construct the channel correlation matrix. The initial candidate set is generated based on the traditional CCF calculation method for different scenarios. Then, the result of channel estimation is used as an input for the PDP estimation whereas the estimated PDP is further used to update the candidate channel correlation matrix. The enhancement of LMMSE channel estimation and PDP estimation can be achieved by the iterative joint estimation procedure. Analysis and simulation results show that in different communication scenarios, the PDP estimation error of the proposed method can approach the Cramér–Rao lower bound (CRLB) after a finite number of iterations. Moreover, the mean square error of channel estimation is close to the performance of accurate PDP-assisted LMMSE.     

线性回归分析参数估计的教学研究

概率论与数理统计教学中,线性回归分析参数估计一般都是采用多元函数求极值的方法.为了培养同学开放型思维方式,在实际教学中配合参数估计的极值求法,又给出了它的代数解释和几何解释.     

圆内二维均匀分布的参数估计

研究了圆内二维均匀分布的参数估计,利用次序统计量得到了圆的半径的估计量与置信区间.     

基于高储能密度电容退化数据的可靠性评估

 根据强激光能源模块高储能密度电容器在工作过程中,受到连续冲击而使退化失效具有累积效应的特点,提出了利用产品运行过程中性能参数退化的信息,用复合Poisson过程对退化轨道建模并进行可靠性评估的方法。给出了模型参数的矩估计和电容器的平均退化量、可靠度、平均寿命等可靠性指标评估的Bootstrap仿真过程。并通过实例说明了该评估方法在工程中的应用。基于电容退化信息的可靠性评估方法,可以在极少甚至没有寿命数据的情况下给出客观可信的评估结果,在理论和应用上都具有重要的价值。