基于最优化思想的磁流变抛光驻留时间算法

基于最优化思想研究磁流变抛光驻留时间算法。将驻留时间反卷积运算变换成矩阵运算,以实际加工要求为约束条件,建立关于驻留时间的最优化数学模型,利用最小二乘逼近和最佳一致逼近数学解法器对优化模型进行数值求解。仿真结果显示:该算法收敛幅度大,计算效率较高,所求解满足数控加工要求。在自行研制的磁流变抛光机床上进行抛光实验,对有效口径为50 mm的圆形平面工件,经过4.7min抛光,PV值从0.191λ降至0.087λ,收敛54.5%,RMS值从0.041λ降至0.010λ,收敛75.6%。     

大口径光学元件磁流变加工驻留时间求解算法

为了解决大口径光学元件磁流变高精度加工问题,基于矩阵运算模型,提出了SBB(Subspace Barzilai and Borwein)最小非负二乘与自适应Tikhonov正则化相结合的驻留时间快速求解方法。同时,在一次收敛中采用双去除函数优化螺旋线轨迹下光学元件的加工,保证中心区域与全口径面形精度一致。仿真表明该算法与常用Lawson-Hanson最小非负二乘法相比,计算精度一致且求解效率大幅提高。对Φ600mm以彗差为主的光学表面模拟加工,峰谷(PV)值和均方根(RMS)值从初始的2.712λ与0.461λ中心区域全局一致收敛到0.306λ和0.0199λ(λ=632.8nm)。因此,提出的算法能够在有效保证面形收敛精度的同时快速获得稳定可靠的驻留时间分布,为磁流变抛光应用于大口径光学元件提供有力支持。     

一种改进的高效稳定内嵌边界算法

在气固多相流数值计算中,内嵌边界方法用于处理固体颗粒和流体之间的相互作用问题。本文将程玉民等改进的移动最小二乘法应用于内嵌边界方法中,克服了移动最小二乘逼近法需要求解逆矩阵的弊端,提高了计算效率和精度。通过直接数值模拟具有精确解的Taylor-Green涡、均匀来流绕过固定圆柱和NACA-0012机翼,验证了本算法可以取得很好的模拟结果。     

基于选择采样的高效迭代相位提取算法

在光学精密测量中,相移干涉法应用广泛。常用的相移器件容易出现相移误差,采用等步距相位提取算法会产生测量误差。基于最小二乘的迭代相位提取算法可以有效消除该类相位提取误差,提高测量精度,但是其迭代过程运行时间长,效率低。提出了一种基于选择采样的迭代相位提取算法,先对干涉图像进行等间隔抽样,降低计算量;再根据对比度滤除干涉图像中低质量像素点,防止误差增大,进行最小二乘迭代求解相位。仿真实验对算法进行了分析和验证,在抽样间隔为2时的选择采样方法与所有像素点全部代入计算相比,运行时间从6.687 s降为0.725 s,均方根误差仅为0.032 9。实验结果证明:选择采样的迭代相位提取算法运算时间短、误差小,非常适合高速相移干涉测量应用。     

光电探测系统半参数模型运动学标定

针对光电探测系统高指向精度要求,提出了补偿最小二乘法与双三次样条插值相结合的半参数模型标定算法来减小系统指向误差。根据光电探测系统结构组成,运用多体系统理论建立系统指向误差参数模型。引入非参数分量,建立半参数模型,应用补偿最小二乘法对半参数模型进行标定得到修正模型。搭建实验平台测得两组数据,分别用于运动学标定与验证修正结果。实验结果表明:系统方位向、俯仰向指向误差均值从92.1185″、75.9358″降低到2.7100″、2.7755″,指向误差标准差从21.6522、15.1744降低到10.8645、10.7305。表明提出的标定算法能有效提高光电探测系统指向精度和空间指向稳定性,且能同时考虑系统误差的线性与非线性关系。     

计算机控制光学表面成形中大规模驻留时间求解

采用基于稀疏矩阵的大规模非负最小二乘法,对大口径、微浮雕结构光学元件加工中的驻留时间进行了分析与求解,并对该算法开展了正则化研究。仿真结果表明:与传统非负最小二乘法相比,基于稀疏矩阵的大规模非负最小二乘法精度高、效率快。采用该算法仿真加工平均振幅为1.177 6倍波长的大口径、微浮雕结构光学元件,误差面形均方根收敛至0.067倍波长。     

含噪混沌时间序列预测误差分析

对于含噪混沌时间序列预测问题, 传统方法存在较大的经验性, 对预测误差的构成分析不足, 因而忽略了混沌动态重建与预测模型建立之间的差异性. 本文将实际预测误差分解为预测器偏差和输入扰动误差, 并对整体最小二乘和正则化两种全局预测方法进行分析比较, 进而说明整体最小二乘适用于混沌动态的重建, 对预测器偏差影响较大, 而正则化方法能够改善预测器敏感性, 对输入扰动误差影响较大. 通过两个仿真实例, 展示了混沌动态重建与预测模型建立之间的差异, 在对比最小二乘和正则化方法的同时验证了实际预测误差受预测器偏差和输入扰动误差共同作用. 并指出, 在实际操作时应在二者间寻求平衡, 以便使模型预测精度达到最优. 关键词： 混沌时间序列预测 噪声 整体最小二乘 正则化     

苹果可溶性固形物近红外光谱检测的偏最小二乘回归变量筛选研究

为了提高苹果可溶性固形物含量近红外光谱校正模型的预测能力和稳健性,分别采用反向区间偏最小二乘法、遗传算法和连续投影算法,筛选苹果可溶性固形物的近红外光谱变量,并建立了偏最小二乘回归模型。利用遗传算法筛选的141个变量建立的校正模型,预测效果最好,与全谱建立的校正模型比较,预测相关系数,从0.93提高到0.96,预测均方根误差,从0.30°Brix降低到0.23°Brix。实验结果表明遗传算法结合偏最小二乘回归方法,有效地提高了苹果可溶性固形物近红外光谱检测模型的预测精度。     

基于可见-近红外光谱变量选择的荒漠土壤全磷含量估测研究

以新疆艾比湖湿地保护区采集的300个荒漠土壤样品为研究对象,利用ASD Field Spec○R 3 HR光谱仪获取的土壤可见-近红外光谱数据以及化学分析获取的土壤全磷数据为数据源,将原始光谱数据经过卷积平滑、标准正态变量变换以及一阶微分预处理后,采用蚁群-遗传结合区间偏最小二乘法提取荒漠土壤全磷含量特征波长,构建土壤全磷含量偏最小二乘回归预测模型;并与全谱偏最小二乘、蚁群-区间偏最小二乘、遗传-偏最小二乘模型进行比较。结果表明：经蚁群-区间偏最小二乘法筛选后,荒漠土壤全磷特征波段为500~700,1 101~1 300,1 501~1 700,1 901~2 100 nm;进一步采用遗传-区间偏最小二乘法进行变量选择,得到共线性最小的13个有效波长,分别为：1 621,546,1 259,573,1 572,1 527,564,1 186,1 988,1 541,2 024,1 118和1 191 nm。建模方法比较显示,采用蚁群-遗传结合区间偏最小二乘法选择的特征变量,建立的模型精度最高,其次是遗传算法、蚁群算法和全光谱。蚁群-遗传结合区间偏最小二乘法建立的土壤全磷含量的模型,效验证均方根误差RMSECV以及预测集均方根误差RMSEP分别为0.122和0.108 mg·g-1,效验证相关系数R_c以及预测集的相关系数R_p分别为0.535 7,0.555 9。因此,经过卷积平滑、标准正态变量变换以及一阶微分预处理,并利用蚁群-遗传结合区间偏最小二乘法建立的模型不仅简单,而且具有较高的预测精度和较好的稳健性,可以估算荒漠土壤全磷含量。     

高精度六面体垂直度误差测量的一种新方法

介绍了一种利用波面干涉仪和精度为0.2″的端齿盘组合测量六面体相邻两面垂直度误差的新方法,建立了基于最小二乘算法的评定垂直度误差分布的数学模型,介绍了测量装置及测量实验步骤。该方法不但测量精度高,而且能够获得两个面之间垂直度误差分布数据,可用于六面体面体形位误差的高精度修形加工。针对精度为2″的标准直角棱镜的测量实验表明该方法测量结果可信,精度优于0.5″。可推广到其他多面体类零件形位误差的高精度测量。     

Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.     

用于混沌时间序列预测的组合核函数最小二乘支持向量机

针对混沌时间序列的预测问题,考虑到单一核函数的最小二乘支持向量机无法明显提高预测精度,提出了一种组合核函数的最小二乘支持向量机预测模型,模型中采用多项式函数与径向基函数组合构建核函数.同时,还对遗传算法进行了改进,使之具有更快的收敛速度和更高的精度,改进的遗传算法适用于解决预测模型中的参数优化问题.通过典型的Lorenz时间序列、Mackey-Glass时间序列、太阳黑子数时间序列以及具有混沌特性的网络流量时间序列对该模型进行了验证.仿真结果表明所提出的模型是有效的.     

计算机控制光学表面成形驻留时间算法研究

光学非球面的使用可以提高光学系统的性能、简化系统结构、减小系统体积,减轻系统重量,计算机控制光学表面成形（ＣＣＯＳ）方法是加工光学非球面的重要方法。介绍了ＣＣＯＳ原理,给出了ＣＣＯＳ中关键技术驻留时间计算的三种算法———基于加工仿真的算法、基于傅立叶变换的算法和基于滑动平均与傅立叶变换的算法。基于加工仿真的驻留时间算法需要做大量的卷积计算,计算时间较长,残余误差较大;基于傅立叶变换的驻留时间算法与基于滑动平均和傅立叶变换的驻留时间算法计算时间短,计算后的残余误差小。磨头单位去除函数形状与磨头大小对误差的去除也有明显的影响,小的磨头和陡峭的单位去除函数对于去除局部误差更为有利,但是加工时间较长     

自适应光学波前计算的并行性研究

在自适应光学系统中,波前计算是指出由波前斜率计算,波前复原运算以及波前控制算法等组成的一类处理任务本文研究了从波前计算算法表达层到结构层的优化映射的实现方法,根据现有的并行计算理论提出了适用于波前计算的两种基于单指令流多数据流（ＳＩＭＤ）结构的并行算法,倍增算法和分组算法。在此基础上,以乘－累加（ＭＡＣ）时间和输入／输出（Ｉ／Ｏ）时间作为衡量算法性能的两个指标,在通用数字信号处理ＤＳＰ芯片构筑的单     

Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

The paper concerns active control of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance (second order moment) of error signal, and hence, becomes unstable for the impulsive noise. In order to improve the robustness of adaptive algorithms for processes having distributions with heavy tails (i.e. signals with outliers), either (1) a robust optimization criterion may be used to derive the adaptive algorithm or (2) the large amplitude samples may be ignored or replaced by an appropriate threshold value. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm (approach 1). The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm (approach 2). In this paper we propose two solutions to improve the robustness of the FxLMP algorithm. In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm. As another solution to improve the performance of FxLMP algorithm, a modified normalized step size is proposed. The computer simulations are carried out, which demonstrate the effectiveness of the proposed algorithms.     

应用近红外高光谱成像技术预测甘蔗可溶性固形物含量

为了探究应用近红外高光谱成像技术对甘蔗内部可溶性固形物（SSC）预测的可行性,试验样本选择三种不同品种中的240个甘蔗节作为研究对象。通过高光谱成像系统获取甘蔗节的近红外光谱信息和图像信息,并分别探讨了光谱信息和图像纹理信息对甘蔗可溶性固形物预测的可行性。采用最小二乘回归（PLSR）,最小二乘支持向量机（LS-SVM）及主成分回归（PCR）建模方法构建甘蔗可溶性固形物的预测模型。比较了连续投影算法（SPA）、无信息变量消除算法（UVE）及区间偏最小二乘（iPLS）特征提取方法对预测结果的影响。实验结果表明：基于甘蔗的光谱信息能实现可溶性固形物的预测,其中偏最小二乘回归模型的建模集和预测集的相关系数分别为0.879和0.843,均方根误差分别为0.644和0.742。通过UVE算法提取105个有效波长所建立的PLSR模型的建模集及预测集相关系数分别为0.860和0.813,均方根误差分别为0.693和0.810。     

Decentralized acoustic source localization in a distributed sensor network

The purpose of this work is to investigate acoustic source localization algorithms suitable for use with distributed sensor networks. Traditional sensor networks employ a central controller; however centralized data processing in large-scale sensor networks is not always desirable because of the excessive communication and computational complexity it requires. Therefore, fully distributed localization algorithms are considered. The most important aspect of these algorithms is that they be scalable for use in large sensor networks. The scalability is achieved by forming sensor nodes into groups which collaborate to locate sources. Source locations are determined from time of arrival (TOA) information of the acoustic wave front. Two source location solution methods are used: least squares (LS) and Tikhonov regularized inversion (RI). Experimental results validate the accuracy of a distributed localization approach, and the effectiveness of the LS and RI methods are compared. Additionally, important parameters of the distributed localization algorithm are considered.     

A computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory

Although emission spectral tomography (EST) combines emission spectral measurement with optical computed tomography (OCT), it is difficult to gain transient emission data from a large number of views,therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory (multi-criterion simulated annealing reconstruction technique, MCSART) is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements

Digital image correlation (DIC) method using iterative least squares algorithm (ILS) for displacement field measurement and pointwise least squares algorithm (PLS) for strain field measurement is proposed in this paper. A more general and practical intensity change model is employed with consideration of the linear intensity change of the deformed image, followed by an iterative least squares algorithm for calculating displacement field with sub-pixel accuracy. The concept of correlation function is not used in the ILS method, even though we prove that the algorithm is actually equivalent to the optimization of the sum of squared difference correlation function using improved Newton–Raphson method. Besides, different from the conventional strain estimation approaches based on smoothing the displacement fields first and followed by differentiation of the smoothed displacement fields, a simple yet effective PLS algorithm is proposed for extracting strain fields from the computed displacement fields. The effectiveness and accuracy of the proposed techniques is verified through numerical simulation experiments. A practical application of the algorithms to residual plastic deformation field measurement of GH4169 alloy subjected to tensile fatigue is also presented.