基于小波消噪和优化支持向量机的板形模式识别

提高板形模式识别的精度利于得到高精度的控制效果。针对实测的板形信号中混有噪声信号的问题,利用双变量阈值小波去噪,克服了软硬阈值函数在处理小波系数方面存在的缺点,使得去噪的效果更好。将去噪后的板形信号离散化,作为支持向量机的学习样本,建立识别模型。引入布谷鸟算法优化支持向量机的参数。仿真结果表明,相比于粒子群和遗传算法,布谷鸟优化算法所需匹配的参数少,而获得的最优解更好。     

Monitoring bivariate and trivariate mean vectors with a Shewhart chart

In this article, we propose the use of the mean chart to control multivariate processes. The basic idea is to control the mean vector of bivariate (X, Y) and trivariate (X, Y, Z) processes by alternating the charting statistic of the Shewhart chart. If the mean of X observations was the charting statistic to obtain the current sample point, then the mean of Y observations will be the charting statistic to obtain the next sample point (for the trivariate case, the mean of Z observations will be the charting statistic to obtain the sample point subsequent to the next one). As a Shewhart chart, the signal is given anytime a sample point is plotted beyond the control limits, independent of the charting statistic in use. A fair comparison between the proposed chart and the Hotelling chart is based on an equal number of measurements per sample. The Shewhart chart with alternated charting statistic (ACS) always outperforms the Hotelling chart, except for specific types of disturbances in quality characteristics highly correlated (ρ = 0.7). The ACS chart is substantially easier to operate and faster than the Hotelling chart in signaling changes in the mean vector of bivariate and trivariate processes. Even with fewer measurements per sample, the trivariate ACS chart outperforms the Hotelling chart.     

Bivariate Dispersion Control Charts for Monitoring Non‐Normal Processes

Multivariate control charts are well known to be more sensitive to the occurrence of variation in processes with two or more correlated quality variables than univariate charts. The use of separate univariate control charts to monitor multivariate process can be misleading as it ignores the correlation between the quality characteristics. The application of multivariate control charts allows for the simultaneous monitoring of the quality characteristics by forming a single chart. The charts operate on the assumption that process observations are normally distributed, but in practice this is not always the case. In this study, we examine and present multivariate dispersion control charts for detecting shifts in the covariance matrix of normal and non‐normal bivariate processes. These control charts, referred to as SMAX, QMAX, MDMAX and MADMAX, rely on dispersion estimates, such as the sample standard deviation (S), interquartile range (Q), average absolute deviation from median (MD) and median absolute deviation (MAD), respectively. We compare the performances of these charts to the existing multivariate generalized variance |S| and RMAX charts for bivariate processes using normal and non‐normal parent distributions. The average run length (ARL) measure is used for the evaluation and comparison of the charts. A real life and simulated datasets are used to demonstrate the application of the charts. Copyright © 2016 John Wiley & Sons, Ltd.     

基于二元对称多项式的秘密共享方案

基于二元对称多项式,提出一个新的无可信中心的（t,n）门限秘密共享方案。方案中,利用对称多项式的对称性,为任意对参与者提供验证私钥,有效地预防外部攻击者的欺诈行为;结合离散对数的难解性,对秘密的正确性进行验证,同时确保秘密不会泄露。参与者选取子秘密,通过构造对称多项式,对子秘密加密,得出影子秘密并公开,参与者可以对公开的信息的正确性进行有效验证;不需要分发者的存在,避免了分发者的欺诈。分析结果表明,该方案是安全有效的。     

文胸肩带的选择依据分析

针对内衣企业在文胸肩带配伍时缺乏有效的选择依据,没有形成规范的、切实可行的文胸肩带选取方法的问题,以文胸肩带舒适性理论研究为基础,提取了影响文胸压力舒适性的因素,并实施了文胸肩带拉伸强力实验,采用YG（B）026H型电子织物强力机测试了6款规格的18个肩带样本,并对实验数据进行了SPSS分析.研究表明,在相同开度的条件下,肩带模量和宽度的比值（M/W）、模量拉伸率曲线曲率是选用文胸肩带的重要指标,其中M/W的数值范围应在0.04~0.05较为合适.     

Seismic Data Recovery with Curvelet Bivariate Shrinkage Function Based on Compressed Sensing

基于压缩感知的Curvelet域双变量阈值收缩函数的地震数据恢复算法

张岩^1,2,任伟建²,唐国维¹,赵璨¹

（1.东北石油大学 计算机与信息技术学院, 黑龙江 大庆 163318;

2.东北石油大学 电气信息工程学院,黑龙江 大庆 163318）

创新点说明：

在石油和天然气勘探过程中,欠采样地震数据的恢复问题是一个重要的研究方向,于是国内外学者最近提出了基于压缩感知的Curvelet域迭代阈值收缩恢复算法,然而这些算法通常采用软阈值收缩函数,该思想是建立在假设所有Curvelet域稀疏系数相互独立基础上,而忽略Curvelet域系数之间的依赖关系。本文利用贝叶斯估计对地震数据Curvelet系数的父子依赖关系进行研究,提出双变量阈值收缩函数的恢复算法。首先在Curvelet域对完整地震数据和欠采样数据中噪声信号,分别建立父子系数联合分布模型,然后根据贝叶斯最大后验概率估计得到双变量阈值收缩函数,然后利用Landweber算法进行迭代,最后通过与现有恢复算法对比实验,得到该算法恢复结果可以得到更高的PSNR,并能更好的保持地震数据同相轴的纹理细节信息。

研究目的：

地震勘探是为了获得地下构造的精确成像,由于采集环境原因,如地表障碍物的存在（建筑物、道路、桥、梁）等或地形条件的因素（禁采区和山区、森林、河网地区等）、仪器硬件（地震检波器、空气枪、电缆等）问题引起的采集坏道以及海洋地震数据采集时电缆的羽状漂流等,往往导致地震数据在空间方向上是欠稀疏采样的。从而导致数据中含有空间假频,以至于在地震数据处理过程中不但会引起人为误差,而且会对基于多道技术的地震数据处理方法的结果产生严重的影响,从而在后续的数据处理时产生假象,最终导致错误的解释。本文利用压缩感知、稀疏表示、贝叶斯估计等技术,结合地震数据的特点,建立地震数据高精度恢复算法。保证后续地震数据处理能够更好反映复杂地质构造,为油气勘探提供更有效的指示和帮助。

研究过程及所用的方法：

（1）基于压缩感知的地震数据恢复基本思想

设完整的二维地震数据为 ( ), 是检波器坐标样本数, 是时间轴坐标样本数,设 是向量化算子, 向量化后 , 为( )向量。欠采样地震资料设为 ( ), ,向量化后 , 为( )维向量,则欠采样地震数据可以表示为式(1)：

(1)

是一个二元采样矩阵,从 采样得到 ,相当于从完整的地震资料 中 道抽取 道数据。因为 ,显然式(1)是欠定的方程组,无法根据 求解 ,设地震资料 在变换域 下系数为 ,有 , 是 的反变换,则

(2)

在具备地震数据变换域系数是稀疏的先验知识,和感知矩阵 满足有限等距性质的前提下,为通过 恢复 提供了理论保证。求解欠定方程组(1)的问题转化为 范数优化问题如式(3)。

(3)

求解式(3)是极不稳定而且是NP难的非凸优化问题。在 和 不相关的条件下,可以转为求解一个等价的、更加简单的 范数优化问题如式(4)。

(4)

使问题转化为求解一个凸优化问题,进而可解。

（2）基于Curvelet的地震数据稀疏表示方法

地震数据Curvelet域可以用 表示,其对应的系数 ,由于Curvelet对二维曲线状纹理具有较好的稀疏表示效果,根据地震波的特点,得到的系数 稀疏程度大,有利于地震数据的恢复。

（3）Curvelet域贝叶斯估计双变量阈值收缩函数设计

分别对地震数据与噪声信号Curvelet变换父子系数联合分布建模,得到地震数据近似拉普拉斯分布,噪声信号近似高斯分布的先验,结合贝叶斯最大后验概率估计得到当前系数的阈值函数,利用地震数据Curvelet域最高尺度系数与邻域窗口系数分布特点估计参数,最终确定双变量阈值收缩函数。

（4）稀疏反演恢复算法设计

采用凸投影和阈值操作交替迭代的Landweber算法,每次迭代的阈值操作通过Curvelet域贝叶斯估计双变量阈值收缩函数过滤噪声如式(5)：

(5)

式(5)中 表示第 次迭代,地震缺失数据在Curvelet域尺度为 的系数, 表示地震完整数据在Curvelet域尺度为 的系数贝叶斯最大后验估计值。

结果：

通过合成数据与marmousi模型数据与现有算法比较得到如下结果：

1）恢复数据的PSNR对比：本文算法高于Curvelet域软阈值迭代算法结果,并且高于双树复小波dual-tree DWT (DDWT)与轮廓波Contourlet域双变量阈值收缩函数迭代结果。

2） 恢复数据的主观效果对比：本文算法得到的恢复数据能更好的保持地震数据同相轴的纹理细节信息。

3）恢复时间对比：本文算法恢复所用时间略高于Curvelet域软阈值迭代算法,以及DDWT与Contourlet域双变量阈值收缩迭代算法。

结论：

1） 通过对完整地震数据与噪声信号Curvelet变换父子系数联合分布建模,结合贝叶斯最大后验概率估计得到当前系数的阈值函数,利用地震数据Curvelet域最高尺度系数与邻域窗口系数分布特点确定估计参数,得到的双变量阈值函数可以较好的对当前系数的有效性进行估计。

2）利用Contourlet域双变量阈值收缩函数结合凸投影和阈值操作交替迭代的Landweber算法,可以有效的恢复地震数据。

关键词：地震数据恢复;压缩感知;迭代收缩;双变量阈值收缩函数

     

Stochastic dynamic systems with complex‐valued eigensolutions

A dimensional decomposition method is presented for calculating the probabilistic characteristics of complex‐valued eigenvalues and eigenvectors of linear, stochastic, dynamic systems. The method involves a function decomposition allowing lower‐dimensional approximations of eigensolutions, Lagrange interpolation of lower‐dimensional component functions, and Monte Carlo simulation. Compared with the commonly used perturbation method, neither the assumption of small input variability nor the calculation of the derivatives of eigensolutions is required by the method developed. Results of numerical examples from linear stochastic dynamics indicate that the decomposition method provides excellent estimates of the moments and/or probability densities of eigenvalues and eigenvectors for various cases including large statistical variations of input. Copyright © 2007 John Wiley & Sons, Ltd.     

Selection for female fertility using censored fertility traits and investigation of the relationship with milk production

Bivariate models (censored linear-linear and censored threshold-linear) were used to estimate genetic parameters for production and fertility traits in the Spanish Holstein population. Records on 71,217 lactations from 41,515 cows were used: 30 and 36% of lactations were censored for days open (DO) and number of inseminations to conception (INS), respectively. Heritability estimates for production traits (milk, fat, protein) ranged between 0.18 and 0.25. Heritability of days to first service (DFS) and DO was 0.05; heritability of INS on the liability scale was 0.04. Genetic correlations between fertility traits were 0.41, 0.71, and 0.87 for DFS-INS, DO-INS, and DO-DFS, respectively. Days open had a larger genetic correlation (ranging from 0.63 to 0.76) with production traits than did DFS (0.47 to 0.59) or INS (0.16 to 0.23). Greater antagonism between production and DO may be due to voluntary management decisions for high-yielding cows, resulting in longer lactation lengths. Inseminations to conception appeared to be less correlated with milk production than were the other 2 female fertility traits. Including INS in a total merit index would be expected to increase genetic gain in terms of profit, but profit would decrease if either DO or DO and DFS were included in the index. Thus, INS is the trait to be preferred when selecting for female fertility. The genetic correlation between actual milk yield and 305-d standardized milk yield was 0.96 in the present study, suggesting that some reranking of sires could occur. Because the target of attaining a 12-mo calving interval, as implied by a 305-d standardized lactation length, is changing in the dairy industry, routine genetic evaluation of actual total lactation milk yield should be considered.     

基于四元数小波混合统计模型的图像去噪

图像的去噪和压缩一直是图像处理的经典问题,传统的方法中很难将二者 同时兼顾。四元数小波变换是实小波、四元数理论及二维希尔伯特变换相结合的产物,是一 种新的多尺度分析图像处理工具。图像经四元数小波变换后,其小波系数不仅在尺度内具有 相关性,而且在尺度间也具有一定的相关性。文中提出一种混合统计模型,该模型包括尺度 间的二元非高斯分布模型和尺度内的广义高斯分布模型,然后运用最小均方误差（MMSE） 估计从噪声图中的小波系数恢复原图的系数,从而达到去除图像的噪声的目的。仿真实验表 明,论文方法不仅可以获得信噪比上的提高、视觉上达到明显的去噪效果,而且取得了较高 的压缩比。     

基于二元对称多项式的公平秘密共享方案

基于二元对称多项式,提出一种新的公平[(t,n)]门限秘密共享方案,能够确保：所有参与者都合法且诚实时,均能恢复正确的秘密;存在欺骗者时,所有参与者都无法恢复正确的秘密。该方案利用二元对称多项式不仅为任意两个参与者提供会话密钥;结合离散对数,在确保每个share持有者拥有较少share的情况下,使得Dealer可以选取足够长的秘密序列,从而确保方案的公平性。此外,方案在异步环境下也能实现公平秘密恢复。与Harn方案相比,该方案更加公平和灵活。