An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.     

Rational Filters for Passive Depth from Defocus

A fundamental problem in depth from defocus is the measurement of relative defocus between images. The performance of previously proposed focus operators are inevitably sensitive to the frequency spectra of local scene textures. As a result, focus operators such as the Laplacian of Gaussian result in poor depth estimates. An alternative is to use large filter banks that densely sample the frequency space. Though this approach can result in better depth accuracy, it sacrifices the computational efficiency that depth from defocus offers over stereo and structure from motion. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. In addition, a depth confidence measure is derived that can be computed from the outputs of the operators. This confidence measure permits further refinement of computed depth maps. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.51.2% of the distance of the object from the imaging optics.     

“迭代-降维-梯度搜索法”求解Dobbins模型参数研究

文章介绍了用“迭代－降维－梯度搜索法”求解Dobbins模型参数的研究成果。该法能解决目前存在的用梯度法搜索参数时误差较大及用“计算机扫描计算－图解－梯度搜索法”寻求参数时计算量较大的问题。     

一种快速自适应谱估计算法

通过对梯度格型快速自适应算法的研究,结合最大熵谱外推目前存在的缺点,将梯度格型算法应用于最大熵谱外推中,并将其与伯格算法进行了比较。     

多目标群体决策的区间层次分析法研究

基于专家个体判断矩阵,应用数理统计及误差理论,探讨了综合判断矩阵的建立途径,给出了用区间层次分析法求解多目标群体决策问题的算法与步骤,最后以算例说明该方法的实用性及有效性。     

基于轮廓特征的图象配准研究

图象配准的一个重要问题是多传器获取的图象配准,这些图象具有不同的灰度特征,用经典技术加区域相关等不能解决,基于轮廓在大多数情况下表达了区域的边缘信息,本文提出了基于轮廓特征的匹配方法,该方法采用区域边缘和其它强边界特征作为匹配对象,算法中参数估计理论,先用闭轮廓的特征在,现用开轮廓的突出部分进行校正。     

非接触激光圆度仪的研制

本文介绍一种非接触式检测大型回转体件圆度误差的激光圆度仪工作原理及总体构成、并通过实验予以验证。该仪器采用激光狭缝扫描原理和光电传感技术, 通过电子学单片机技术进行误差分离及数据处理, 从而实现圆度误差测量     

铲齿成形铣刀重磨误差的分析和重磨前角的优化

本文先提出了γｆ＞０的铲齿成形铣刀因重磨而引起的廓形高度误差的计算方法,分析了重磨误差的规律。在此基础上又进一步提出了重磨前角的修正和优化。     

薄透镜成象快速调节技术

本文结合象差理论,分析了薄透镜成象的几个先路图,介绍了相应的快速调节技术．     

配电匹配潮流技术及其在配电状态估计中的应用

针对配电系统的实时量测冗余度低,提出了用配电匹配潮流技术来求解配电技术状态估计的方法,并推导了最优估计意义下的配电匹配潮流方程及其系统化的求解方法,理论分析和算例结果表明,利用配电匹配潮流进行状态估计高效可行,符合当前配电系统在线应用的实际要求。