用于彩色图像复原的带有高阶耦合项的TV模型

提出了一种改进的TV(Total Variance)彩色图像复原方法。为消除TV模型的各向异性扩散导致的块效应,采用在TV模型的基础上耦合高阶项的新模型;并将这个新的模型推广到彩色图像,利用多通道的耦合机制实现各单色通道图像复原过程的相互制约。新模型保持了各向异性扩散的特性,图像的边缘得到了保持。实验结果证明, 与其它模型的复原 彩色图像相比,新模型复原的图像的峰值信噪比(PSNR)有了更大的提高,图像的非边缘区看上去更加平滑自然。     

基于颜色和纹理特征的林火烟雾识别

为了实现森林火灾的智能化预警,提出了基于颜色和纹理特征的林火烟雾识别方法.首先使用颜色特征确定烟雾疑似区域,随后采用局部二值模式方差(Local Binary Pattern Variance,LBPV)提取疑似区域纹理的不规则度特征并产生LBP图像.然后利用小波变换分解LBP图像并提取模糊度、复杂度和相关度特征.最后利用支持向量机(Support Vector Machine,SVM)进行烟雾识别.结果证明,颜色结合纹理特征方法可以有效实现林火烟雾的识别,为林火烟雾识别研究提供了一种有效方案.     

MHD角速度传感器随机漂移误差补偿方法研究

为了补偿磁流体动力学(MHD)角速度传感器的随机漂移误差,在对其建立时间序列模型的基础上,采用卡尔曼滤波算法对MHD角速度传感器的漂移数据进行了处理,并用方差和Allan方差分析的方法,对滤波前后的数据进行了分析对比.实验结果表明此方法能有效地提高MHD角速度传感器的测量精度.     

基于自适应Kalman滤波的MEMS陀螺随机误差分析

针对某型MEMS陀螺随机误差较大、精度不高的问题,通过时间序列分析法,建立自回归滑动平均 ARMA(Auto-Regressive and Moving Average)模型,采用ARMA(2,1)模型将预处理后的MEMS陀螺随机误差进行建模.设计基于ARMA模型的经典Kalman滤波器.静态试验和恒定速率试验结果表明在经典Kalman滤波器作用下,静态试验下其均值与均方差下降32.62%和66.31%;恒定速率试验下,其均值有明显的降低,其均方差减小了一个数量级.针对经典Kalman滤波器不能解决振动试验中大振幅时滤波发散问题,提出一种新的自适应Kalman滤波法,通过寻找合适的标定因子s解决滤波发散问题.振动试验结果表明,当振幅为100°时,滤波后的均值和均方差分别下降8.25%和8.36%.     

MEMS传感器随机误差分析及处理

MEMS传感器的精度限制了其应用范围,为减小MEMS传感器随机误差的影响,提高其精度,对其随机误差进行分析和处理具有重要意义。本文首先采用Allan方差法分析了MEMS陀螺仪和加速度计的主要随机误差。然后基于Allan方差分析结果,发现从时频角度,采用小波变换分析传感器的随机误差,可以对零偏,有色噪声,白噪声进行了有效分离。最后通过对小波降噪方法的探讨,发现小波分析具有分离、减小随机误差的优势。     

Supply chain coordination with risk sensitive retailer under target sales rebate

Target sales rebate (TSR) contracts have been shown to be useful in coordinating supply chains with risk-neutral agents. However, there have been few studies on the cases with risk sensitive agents. As a result, based on the classic Markowitz portfolio theory in finance, we carry out in this paper a mean–variance (MV) analysis of supply chains under TSR contracts. We study a supply chain with a single supplier and a single risk averse retailer. We propose TSR contracts for achieving coordination. We demonstrate how TSR contracts can coordinate the supply chain which takes into consideration the degree of risk aversion of the retailer. We find that the supplier can coordinate the channel with flexible TSR contracts. In addition, we extend the supply chain model to include sales effort decision of the retailer. Conditions for TSR contracts to coordinate the supply chain with sales effort of retailer are also derived.     

Automatic eye fixations identification based on analysis of variance and covariance

Eye movement is the simplest and repetitive movement that enables humans to interact with the environment. The common daily activities, such as reading a book or watching television, involve this natural activity, which consists of rapidly shifting our gaze from one region to another. In clinical application, the identification of the main components of eye movement during visual exploration, such as fixations and saccades, is the objective of the analysis of eye movements: however, in patients affected by motor control disorder the identification of fixation is not banal. This work presents a new fixation identification algorithm based on the analysis of variance and covariance: the main idea was to use bivariate statistical analysis to compare variance over x and y to identify fixation. We describe the new algorithm, and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach, we tested the algorithm in a group of healthy subjects and patients affected by motor control disorder.     

One-sided multiple comparisons for treatment means with a control mean

We study comparisons of several treatments with a common control when it is believed a priori that the treatment means, μ_i, are at least as large as the control mean, μ₀. In this setting, which is called a tree ordering, we study multiple comparisons that determine whether μ_i>μ₀ or μ_i=μ₀ for each treatment. The classical procedure by Dunnett (1955) and the step-down and step-up techniques by [Dunnett and Tamhane, 1991] and [Dunnett and Tamhane, 1992] are well known. The results in Marcus and Talpaz (1992) provide multiple comparisons based on the maximum likelihood estimates restricted by the tree ordering. We also study two-stage procedures that consist of the likelihood ratio test of homogeneity with the alternative constrained by the tree ordering followed by two-sample t comparisons with possibly different critical values for the two-sample comparisons. Marcus et al. (1976) discuss the use of closed tests in such situations and propose using a closed version of the restricted likelihood ratio test. We describe step-down versions of the Marcus-Talpaz, the two-stage, and the likelihood ratio procedures, as well as a closed version of the Marcus-Talpaz multiple comparison procedure. Using Monte Carlo techniques, we study the familywise errors and powers of these procedures and make some recommendations concerning techniques that perform well for all tree ordered mean vectors.     

Estimating the error variance after a pre-test for an interval restriction on the coefficients

This paper considers the estimation of the error variance after a pre-test of an interval restriction on the coefficients. We derive the exact finite sample risks of the interval restricted and pre-test estimators of the error variance, and examine the risk properties of the estimators to model misspecification through the omission of relevant regressors. It is found that the pre-test estimator performs better than the interval restricted estimator in terms of the risk properties in a large region of the parameter space; moreover, its risk performance is more robust with respect to the degrees of model misspecification. Furthermore, we propose a bootstrap procedure for estimating the risks of the estimators, to overcome the difficulty of computing the exact risks.     

一种新的指纹图像合成分割法

在研究和分析传统方差法和最大类间方差法的基础上,提出了一种传统方差法和最大类间方差法相结合的分块处理分割算法。该算法首先求出整个指纹图像的方差,然后计算每一子块的方差。若方差小于整个图像的方差则用方差法对图像进行分割,否则用最大类间方差法进行分割。最后再对图像进行平滑处理。实验结果表明,相对于传统分割方法,该方法无需根据经验选取阈值,能较准确地分割出前景和背景,抗噪能力强,对高对比度和低对比度的图像均具有很好的分割效果。