电子产品寿命检测中异常数据的检验

讨论在电子产品的寿命数据中,对同时存在的异常大数据和异常小数据的检验方法,给出了一个明确的判别标准,并以一例说明其应用.     

基于线性ν-支持向量回归机的异常数据检测

讨论了线性v-支持向量回归机中参数v的意义,并给出了严格的理论证明。利用v-支持向量回归机中ε-不敏感损失函数及参数v的意义,提出一种回归数据中的异常值检测方法。采用线性模型使得该方法不仅速度快而且能处理大规模数据。数值实验证明其具有可行性和有效性。     

统计数据异常值的识别及R语言实现

从统计理论的角度探讨识别统计数据中的异常值,即Grubbs法、dixon法在识别统计数据中的异常值中具有的重要的作用;介绍了在R语言的环境下,程序包outliers的程序语言,并结合具体实例,说明程序包outliers在识别统计数据中异常值的作用。     

A Recursive Algorithm to Restore Images Based on Robust Estimation of NSHP Autoregressive Models

The objective of this article is to present a new image restoration algorithm. First, each pixel in the image is classified into k categories. Then we assume that the gray levels in each category follow a nonsymmetric half-plane (NSHP) autoregressive model. Robust estimation of the parameters of the model is considered to attenuate the effect of the image contamination on the parameters. In each iteration we will construct a new image using a robustified version of the residuals. The introduction of the classification techniques as a first step of the algorithm reduces considerably the number of parameters to estimate. Hence, the computational time is also reduced because the robust estimations of the parameters are solutions of nonlinear system of equations. Some applications are presented to real synthetic aperture radar (SAR) images to illustrate how our algorithm restores an image in practice.     

应用小波理论处理油(气)田长时压力监测数据

自90年代以来,越来越多的生产井和注入井安装了长时井底压力计.关于长时压力数据分析的主要障碍是数据中的噪音和奇异点、巨大的数据容量、不完备的信息以及缺少动态分析工具.本文以小波理论为基础,经过反复试验,寻找到了适合于处理长时压力数据的小波类型;在Chee kin(2001)的线性回归方法与小波分解的基础上,建立了剔除异常点的多重阀值法、剔除噪音的软阀值方法以及确定瞬变值的多步法;油(气)田实例说明了上述方法的有效性.     

Separation of spin synchronized signals

In the measurements of VLF electric fields with the Pioneer Venus spacecraft in sunlight, spin synchronized signals often dominate over the naturally generated emissions. We present a method to separate natural emissions from the several possible sources of noise. Our major objective by this method is not to remove all spin modulation, but to effectively subtract the background noise caused by the identifiable noise sources. Examination of the data shows that the background spin synchronized noise is quite sensitive to (n), the angle between the sense axis and the solar direction. We model the observed data asy(n)=w(n)t(n)f((n))+x(n), wheref() represents the phase response of the background noise andx(n) is the estimated natural emissions.t(n) andw(n) are the long-term trend component and time- and phase-independent component of the intensity of the background noise, respectively. The method to decomposey(n) is based on the Bayesian approach which has been recently applied to various inversion problems such as nonstationary time series modeling and image reconstruction. In this procedure, the estimated parametersw(n),t(n),f(), andx(n) can be determined automatically. We will describe the Bayesian scheme and its application to the Pioneer Venus VLF electric field data.     

一种无人飞行器监控数据预处理方法

阐述了对无人飞行器进行监控的必要性和重要意义,设计了一种无人飞行器监控数据预处理流程。首先采用空间对准算法将各传感器数据统一到空管系统坐标系下。然后根据无人飞行器的运动特点提出了基于改进“当前”统计模型的自适应卡尔曼目标滤波算法对航迹数据进行平滑滤波,该算法根据滤波新息的变化自适应调整机动频率,仿真表明改进后的算法能够实现对目标更为精确的跟踪。之后对航迹数据中的野值进行了判别和剔除。最后规范统一了各传感器的数据帧格式,实现了对监控数据进行预处理的目的。     

Fuzzy weighted scaled coefficients in semi-parametric model

In general, the regressor variables are stochastic, Duan and Li (1987, J. Econometrics, 35, 25–35), Li and Duan (1989, Ann. Statist., 17, 1009–1052) have been shown that under very general design conditions, the least squares method can still be useful in estimating the scaled regression coefficients of the semi-parametric model Y _i =Q ₁(+X _i ; _i , i+ 1,2,...,n. Here is a constant, is a 1×p row vector, X _i is a p×1 column vector of explanatory variables, _i is an unobserved random error and Q ₁ is an arbitrary unknown function. When the data set (X _i , Y _i ),i=1, 2, ..., n, contains one or several outliers, the least squares method can not provide a consistent estimator of the scaled coefficients . Therefore, we suggest the fuzzy weighted least squares method to estimate the scaled coefficients for the data set with one or several outliers. It will be shown that the proposed fuzzy weighted least squares estimators are % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaamaakaaabaGaamOBaaWcbeaaaaa!3D3C!\[\sqrt n \] and asymptotically normal under very general design condition. Consistent measurement of the precision for the estimator is also given. Moreover, a limited Monte Carlo simulation and an example are used to study the practical performance of the procedures.This research partially supported by the National Science Council, R.O.C.     

近红外光谱分析青贮玉米NDF中判别异常光谱的研究

对近红外光谱分析青贮玉米中性洗涤纤维(NDF)中异常光谱的判别进行了研究。该试验通过将马氏距离阈值分别设定为3(固定值)、2倍马氏距离平均值和马氏距离平均值+2倍马氏距离标准差三种不同值,分别判别和剔出建模过程中的异常光谱,比较不同的阈值设定对模型效果的影响。结果表明,当马氏距离阈值设为3(固定值)时,异常光谱剔出不收敛,可靠性不高。当马氏距离阈值设为2倍马氏距离平均值时,剔出后模型的相关系数、标准差、决定系数及综合得分均低于剔出前,可靠性不高。只有当马氏距离阈值设定为马氏距离平均值+2倍马氏距离标准差时,预测模型的最高相关系数ｒ达到0.97,标准差为2.456,模型预测效果最佳。因此,将马氏距离阈值设定为马氏距离平均值+2倍马氏距离标准差对近红外光谱分析青贮玉米NDF中异常光谱判别的可行性较高,在这种情况下剔出异常光谱后模型效果最佳。     

一种信号分选和特征提取联合处理的雷达信号分析方法研究

研究广度优先搜索邻居的聚类算法和直方图法用于雷达信号分选和特征提取的原理和方法,并将两者相结合用于雷达信号分析使分选和特征提取更加准确.仿真实验证明联合处理法是高效和实用的.