超声层析成像的迭代重建

利用变形Born迭代方法,建立了超声衍射重建算法。在迭代过程中,为了解决超声逆散射问题中的非线性性,需要反复地求解前向散射方程和逆散射方程,以达到全场和未知函数的近似,较好地重建物体内部的断层图象。由于逆散射方程是一个不适定性的方程组,要用正则化方法处理方程的不适定性问题,使迭代方法收敛于问题的真实解,才能成功地应用于较高对比度物体的图象重建问题。用Picard准则对不适定问题进行了分析,给出了通过简单图形．确定模型受噪声污染情况以及正则化方法适用范围的方法。在重建实验中。对建立的图像重建算法进行了实验仿真。达到了较好的效果。     

由 Mie 散射光强反演颗粒粒度分布的一种改进正则化法

基于Mie散射的激光粒度仪广泛地应用在颗粒粒度测量中,其中由光强分布演算出粒度分布的计算方法一直是关注的热点。此反演问题属于第一类Fredholm算子方程,具有不适定性,难以得出准确的稳定解,需要用高效的数值算法。本文提出一种应用于该类仪器颗粒粒度分布反演问题的改进正则化法,采用广义交叉验证法(GCV)来选择正则参数,并引入松弛技术,将迭代值加工成一种松弛值以改善精度,得出了稳定的正则拟解(近似解)。经标准颗粒的验证和计算机模拟证实,此算法是可行和有效的。     

迭代总体最小二乘正则化的近场声全息方法研究

为有效解决近场声全息(Near-field Acoustic Holograph,NAH)技术在水下振动声源识别方法中面临不适定性问题,将平面NAH技术的声源识别过程转化为线性系统的求解过程,探明声源识别中不适定性问题产生的根源,考虑全息面测量声压和传递矩阵均存在误差,提出牛顿迭代的总体最小二乘(NTLS)正则化方法稳定NAH重建过程。新方法以TLS正则化算法为基础建立目标函数,将目标函数改化为具有凸函数的性质;然后采用具有二阶收敛速度牛顿迭代法求解;基于L曲线法特性,提出自适应迭代方法确定正则化参数。开展了NAH仿真和试验研究,提出构建良态传递矩阵的策略,最终验证了NTLS正则化的NAH技术在水下振动声源识别和定位中具有较好的精度。     

基于精确描述的超声散射声场的建立

从超声波在连续介质中的传播特性出发,在建立声波在均匀介质中传播的波动方程的基础上,对声波在非均匀介质中传播的问题进行了研究。利用格林函数,建立了关于精确散射场和全场的波动方程。为了便于利用计算机等处理器对方程进行求解,将矩量法引入到方程的离散化问题中,从而为波动方程的求解提供了理论基础。试验验证结果表明：基于离散化的散射场方程和全场动方程,在一定对比度的条件下,采用迭代方法能够实现超声散射层析成像。     

非线性时域识别方程的不适定性与正则化方法研究

研究了非线性时域识别方程的不适定性及其正则化求解方法。雅可比矩阵的性态能够反映非线性识别方程的性态，因此雅可比矩阵的条件数是非线性识别方程的不适定性的度量。阻尼最小二乘法只是一种强迫正定的计算方法，其识别结果仍然对测试噪声很敏感，解决该问题的有效途径是将阻尼最小二乘法与正则化方法两者结合使用。算例表明，将先验的参数预估值引入Tikhonov镇定泛函可以得到稳定的参数解，且识别误差与原始数据的测试噪声基本保持在同一水平。     

边界无网格技术在噪声源识别中的应用

根据非共形面声全息对声辐射传播建模的高精确度要求,将边界无网格法引入声辐射传播建模过程中,实现对三维空间任意封闭曲面上Kirchhoff-Helmholtz边界积分方程高精度离散和求解计算。进一步研究非共形面声全息逆向重构问题的基本原理及其不适定性,采用了Landweber迭代正则化和L曲线正则化参数选取方法,从而确立有效的声场逆向重构求解方法。最后,还进行减速电机噪声源表面振动重构实验,验证研究的基于边界无网格的非共形面声全息的相关理论和方法可行。     

共轭梯度最小二乘迭代正则化算法在冲击载荷识别中的应用

结构动载荷识别反问题是典型的病态问题,需要应用正则方法克服其病态特性而获得稳定的解。与直接正则化算法Tikhonov方法相比,共轭梯度最小二乘(Conjugate Gradient Least Squares,CGLS)迭代算法在载荷识别反问题的正则化过程有无须对传递矩阵求逆、无须明确正则化参数的优点。提出共轭梯度最小二乘迭代正则化算法和启发式迭代收敛终止准则,用于三自由度仿真模型和壳结构试验模型的冲击载荷识别,并与经典的Landweber迭代正则化算法和直接正则化算法Tikhonov方法比较。仿真和实验结果表明:CGLS迭代正则化算法在识别精度、收敛速度、计算效率和抗噪性方面有明显优势。     

考虑不确定性因素的有限元模型修正方法研究

研究了考虑试验模态参数不确定性的有限元模型修正方法。首先,假设试验模态参数为均值和随机变量之和的形式,将不确定性模型修正问题转化为均值和协方差的修正问题。其次,采用摄动法推导了结构参数均值和协方差关于试验模态参数的修正迭代方程。然后,引入Tikhonov正则化方法对病态矩阵进行处理,解决了传统不确定模型修正过程中常见的病态矩阵求逆不适定问题。最后,从数值仿真和试验两个角度对此方法的有效性进行了验证。     

线性不适定问题中选取Tikhonov正则化参数的线性模型函数方法

如何选取正则化参数是不适定问题Tikhonov正则化的一个重要问题。基于吸收的Morozov偏差原理,研究了正则化参数选取的线性模型函数方法。在从Hermite插值角度导出线性模型函数后,讨论了选取正则化参数的两种线性模型函数算法(基本算法与改进算法)及其收敛性。为克服基本算法的局部收敛性,提出了一种新的线性模型函数松弛算法。并且,提出了两种具有全局收敛性的组合算法,即线性与线性模型函数算法、双曲型与线性模型函数算法。数值实验说明了所提算法的有效性。     

基于l1正则化无迹卡尔曼滤波的结构损伤方法

采用传统卡尔曼滤波类算法对结构进行损伤识别时,损伤识别反问题的不适定性使得识别结果易受噪声干扰,甚至算法不收敛。为此,该文提出了一种结合l1范数正则化的无迹卡尔曼滤波损伤识别算法。根据结构出现局部损伤时其损伤参数分布具有稀疏性的特点,通过伪测量方法,将l1范数正则化引入到无迹卡尔曼滤波框架中,在改善反问题求解不适定性的同时,能有效地提高结构局部损伤识别能力。梁、桁架结构的数值分析与实验研究表明,该文方法可以对损伤的位置与程度进行准确识别,且具有良好的鲁棒性。     

脉冲风洞测力系统建模与载荷辨识方法研究

将载荷辨识技术应用于脉冲燃烧风洞模型测力。用子结构综合法建立了测力试验系统的动力学模型,在时域内将动力学方程进行离散,建立起天平测量信号与模型气动载荷历程之间的线性关系,作为载荷辨识的模型。采用Tikhonov正则化和子空间投影法相结合的混合正则化方法,将高维的、不适定的载荷辨识问题转化为低维的适定问题,以利于快速求解。提出了一种新方法来确定合适的投影子空间维数,然后应用L曲线准则来寻找低维正则化问题的最优正则化参数。最后通过算例验证了系统建模方法的精度和载荷辨识算法的有效性与稳定性。     

Parameter identification with weightless regularization

Although the regularization increased the popularity of parameter identification due to its capability of deriving a stable solution, the significant problem is that the solution depends upon the regularization parameters chosen. This paper presents a technique for deriving solutions without the use of the parameters and, further, an optimization method, which can work efficiently for problems of concern. Numerical examples show that the technique can efficiently search for appropriate solutions. Copyright © 2001 John Wiley & Sons, Ltd.     

一种基于变尺度积分滑动平均的载荷识别方法

首先,基于积分滑动平均思想构造了变尺度加权积分函数,提出了最小二乘意义下加权积分滑动平均最佳近似响应函数模型。其次,利用形函数方法构造最佳近似载荷模型,组合近似载荷及响应得到实际情况下的Duhamel积分方程,对Duhamel积分离散化得到用于载荷识别的离散线性系统方程。再次,使用正则化方法进行载荷识别。利用正则化方法中最小二乘解构造以正则化参数为自变量的函数,提出了一种选取最优正则化参数的新方法。最后,数值仿真及试验验证将该文提出方法与传统方法进行了比较,结果说明新方法能够得到精度较好的近似稳定解,并且具有较好的抗噪性。     

Quasi-optimal Tikhonov penalization and parameterization coarseness in space-dependent function estimation

The determination of space-dependent functions from boundary measurements or inner pointwise measurements is ill-posed inverse problems that require regularization tools to be stabilized. Among numerous regularization strategies, the Tikhonov penalization is one of the most used in the field of space-dependent function estimation. Its efficient use relies on the Tikhonov parameter value for which search is time consuming although necessary, especially in the field of nonlinear inversion. Other strategies, such as appropriate parameterization, have recently proven to be very efficient to cope with the ill-posedness of such problems. This paper shows that the optimal Tikhonov parameter is almost independent of the mesh used to project the functions to be retrieved. As a consequence, this value should be seeked using a coarse mesh even though reconstructions could further be done on finer meshes. This conclusion is validated by numerical means.     

Adaptive error estimation technique of the Trefftz method for solving the over-specified boundary value problem

In this paper, numerical solutions are investigated based on the Trefftz method for an over-specified boundary value problem contaminated with artificial noise. The main difficulty of the inverse problem is that divergent results occur when the boundary condition on over-specified boundary is contaminated by artificial random errors. The mechanism of the unreasonable result stems from its ill-posed influence matrix. The accompanied ill-posed problem is remedied by using the Tikhonov regularization technique and the linear regularization method, respectively. This remedy will regularize the influence matrix. The optimal parameter λ of the Tikhonov technique and the linear regularization method can be determined by adopting the adaptive error estimation technique. From this study, convergent numerical solutions of the Trefftz method adopting the optimal parameter can be obtained. To show the accuracy of the numerical solutions, we take the examples as numerical examination. The numerical examination verifies the validity of the adaptive error estimation technique. The comparison of the Tikhonov regularization technique and the linear regularization method was also discussed in the examples.     

An inverse boundary problem for one-dimensional heat equation with a multilayer domain

In this paper, we propose a regularization method for determining a moving boundary from Cauchy data in one-dimensional heat equation with a multilayer domain. The numerical scheme is based on the use of the method of fundamental solutions and a discrete Tikhonov regularization technique. The generalized cross validation rule for the choice of a regularization parameter is applied to obtain a stable numerical approximation to the moving boundary. Numerical experiments for five examples show that our proposed method is effective and stable.     

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.     

The method of fundamental solutions for a biharmonic inverse boundary determination problem

In this paper, a nonlinear inverse boundary value problem associated to the biharmonic equation is investigated. This problem consists of determining an unknown boundary portion of a solution domain by using additional data on the remaining known part of the boundary. The method of fundamental solutions (MFS), in combination with the Tikhonov zeroth order regularization technique, are employed. It is shown that the MFS regularization numerical technique produces a stable and accurate numerical solution for an optimal choice of the regularization parameter. A. Zeb on study leave visiting the University of Leeds.     

The method of fundamental solution for the inverse source problem for the space-fractional diffusion equation

In this article, a meshless numerical method for solving the inverse source problem of the space-fractional diffusion equation is proposed. The numerical solution is approximated using the fundamental solution of the space-fractional diffusion equation as a basis function. Since the resulting matrix equation is extremely ill-conditioned, a regularized solution is obtained by adopting the Tikhonov regularization scheme, in which the choice of the regularization parameter is based on generalized cross-validation criterion. Two typical numerical examples are given to verify the efficiency and accuracy of the proposed method.