集装箱船体波浪载荷应力短期分布的统计分析

基于对波浪载荷所引起的交变应力范围数据统计分析的结论,建议用一类双峰正态混合分布拟合．利用EM算法对混合分布的参数进行估计,借助估计出的参数值,揭示了在一定条件下船体不同部位疲劳载荷的固有关系．船体波浪载荷应力在船首处尤为复杂,双峰对称正态逆高斯混合分布对该处波浪载荷所引起的交变应力范围数据拟合效果更好．实证分析表明,所提供的概率分布参数能够反映船体不同部位的疲劳载荷大小,所得到的关于船体不同部位疲劳载荷固有关系的结论可为船舶运行品质研究提供理论基础．     

基于高斯过程算法的日尺度IMERG降水数据与站点数据的融合研究——以湖北省为例

该文针对卫星降水数据空间分辨率较低的问题,以湖北省为研究区域,考虑经纬度、DEM、亮温数据、IMERG插值数据等辅助变量,融合2016年7月19日IMERG日分辨率卫星降水数据与气象站点数据资料.该文提出了点面融合方法和站点偏差校正估计两个融合方案,并选择了自适应样条多元回归、随机森林、高斯过程回归三种算法.结果表明,点面融合方法优于站点偏差校正估计方法,且高斯过程回归的融合结果优于其他两种算法.基于高斯过程方法的融合结果呈现合理的变化细节,符合降水的空间分布变化模式.融合结果的空间分辨率从约0.1°(约10 km)提高到1 km,且精度相对于原始的IMERG数据得到了较大的提升,该研究对高时间分辨率的多源降水数据的融合具有一定的意义.     

功能梯度材料梁自由振动问题的常微分方程求解器解

建立具有连续分布参数的功能梯度材料Euler梁、Timoshenko梁自由振动的动力学方程,以常微分方程求解器为工具,分析计算这两种梁的自振频率;同时讨论Timoshenko梁的自振频率和振型随梁的参数而变化的规律,给出Timoshenko梁的弯曲振动弹性波和剪切振动弹性波的传播速度,分析弯曲和剪切耦合振动的特点和规律.结果表明:常微分方程求解器解和解析解几乎具有同样的精度;自振频率的大小取决于梁在振动时的弹性波的波速;Timoshenko梁在每个频率下的振动均为弯曲和剪切的耦合振动.     

ARFIMA approximation and forecasting of the limiting aggregate structure of long‐memory process

This article studies Man and Tiao's (2006) low‐order autoregressive fractionally integrated moving‐average (ARFIMA) approximation to Tsai and Chan's (2005b) limiting aggregate structure of the long‐memory process. In matching the autocorrelations, we demonstrate that the approximation works well, especially for larger d values. In computing autocorrelations over long lags for larger d value, using the exact formula one might encounter numerical problems. The use of the ARFIMA(0, d, d?₁) model provides a useful alternative to compute the autocorrelations as a really close approximation. In forecasting future aggregates, we demonstrate the close performance of using the ARFIMA(0, d, d?₁) model and the exact aggregate structure. In practice, this provides a justification for the use of a low‐order ARFIMA model in predicting future aggregates of long‐memory process. Copyright © 2008 John Wiley & Sons, Ltd.     

Semiparametric Preference Learning

Unlike traditional supervised learning problems,preference learning learns from data available in the form of pairwise preference relations between instances.Existing preference learning methods are either parametric or nonparametric in nature.We propose in this paper a semiparametric preference learning model,abbreviated as SPPL,with the aim of combining the strengths of the parametric and nonparametric approaches.SPPL uses multiple Gaussian processes which are linearly coupled to determine the preference relations between instances.SPPL is more powerful than previous models while keeping the computational complexity low （linear in the number of distinct instances）.We devise an efficient algorithm for model learning.Empirical studies have been conducted on two real-world data sets showing that SPPL outperforms related preference learning methods.     

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and patternrecognition for decades.A two stage automatic image annotation method based on Gaussian mixturemodel (GMM) and random walk model (abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization (RPEM) algorithm is employed to estimatethe posterior probabilities of each annotation keyword.Subsequently, a random walk processover the constructed label similarity graph is implemented to further mine the potential correlations ofthe candidate annotations so as to capture the refining results, which plays a crucial role in semanticbased image retrieval.The contributions exhibited in this work are multifold.First, GMM is exploitedto capture the initial semantic annotations, especially the RPEM algorithm is utilized to train themodel that can determine the number of components in GMM automatically.Second, a label similaritygraph is constructed by a weighted linear combination of label similarity and visual similarity ofimages associated with the corresponding labels, which is able to avoid the phenomena of polysemyand synonym efficiently during the image annotation process.Third, the random walk is implementedover the constructed label graph to further refine the candidate set of annotations generated byGMM.Conducted experiments on the standard Corel5k demonstrate that GMM-RW is significantlymore effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.     

基于改进LGDF模型的超声图像自动分割方法

基于局部高斯分布拟合能量(LGDF)模型的图像分割方法,对初始轮廓选取及参数选择较敏感.如果初始轮廓手动选取不当会由于陷入局部极小值而导致分割失败,且分割速度较慢.针对以上不足,提出了一种改进的LGDF模型的超声图像自动分割方法.该方法的正则化项由具有双极值点的势函数构成,在水平集函数进化过程中,可以避免由单极值点势函数造成的水平集函数震荡和扭曲,从而加快了收敛;另外,将局部熵阈值分割的结果作为LGDF模型的初始轮廓,接近真实轮廓,可以克服手动选取初始轮廓的影响.实验结果表明,该方法能自动获取合适的超声图像初始轮廓,并得到较好的分割结果,同时大大提高了分割速度.     

一种动态校正的AGMM-GPR多模型软测量建模方法

工业过程常常是强非线性的,并有多个工况,传统的软测量方法存在预测能力差,不能有效利用误差信息等缺点.为了有效解决这些问题,提出一种基于自适应高斯混合模型-高斯过程回归(AGMM-GPR)的多模型动态校正软测量建模方法.首先,通过贝叶斯信息准则构建自适应高斯混合模型(AGMM),得到优化的子模型个数;然后,利用GPR方法建立各局部模型,当新的数据到来时,将其隶属于各局部模型的后验概率和预测值融合得到多模型输出;最后,为了进一步提高模型的精度,构建自回归积分滑动平均(ARIMA)模型对多模型输出进行动态反馈校正.通过数值仿真和硫回收装置(SRU)中H2S浓度的估计,验证了所提方法具有良好的预测精度和泛化性能.     

信道编码测试平台的建立

为获得信道编译码器的实际性能,建立了基于硬件的信道编码测试平台.给出了平台的设计方案.提出了一种改进的高斯白噪声设计方法.χ2和K-S检验结果表明,产生的样本服从高斯分布,且样本"尖锐"的自相关特性及近似常数的功率谱密度样本具有白噪声特性.在现场可编程门阵列(FPGA)上实现了此测试平台.经实例验证,建立的平台可用作信道编译码器和新型译码算法的测试工具.     

白高斯噪声对肿瘤增长系统的影响

目的研究受白高斯噪声驱动的肿瘤细胞增长系统的定态性质。方法从理论和数值两方面对系统的定态进行分析。结果该系统的定态概率密度随乘性噪声强度D的增长而减小,而加性噪声强度α对定态概率密度几乎没有影响。结论乘性噪声强度D的增长能够引起肿瘤数的减少。