基于最小二乘法的均匀扩声技术研究

针对目前室外及超大型室内空间的扩声应用中声场分布不均匀的问题,本文提出了一种基于最小二乘法声场重建方法的扩声技术。该技术通过对目标声场的逼近来计算线性扬声器阵列各通道的输入参数,实现扩声区域内声压级的均匀分布,同时约束非扩声区域的声能量以获得较好指向性。本文通过仿真研究算法各参数的改变对控制结果的影响,探讨不同扩声区域和目标所对应参数的选取方法。仿真和实验比较了该算法以及未经控制的声场、相移法波束控制的效果,证明该方法可以获得更好的声场均匀度。     

迭代重加权时域平面波叠加法的非稳态声场重建

提出了迭代重加权时域平面波叠加法,并应用于非稳态声场重建。该方法首先通过时域离散卷积得到全息面时域声压与虚源面时域波数谱离散状态公式;然后根据稀疏表示和迭代重加权,得到残差范数与加权解范数最小化问题表达式;最后将每一对波数下虚源面时域波数谱与时域传播核卷积叠加得到重建面上的时域声压。为了验证该方法的有效性,给出了激励板声源的仿真验证,并在半消声室内进行了实验验证。讨论了一些参数对该方法的影响,并通过与基于Tikhonov正则化的时域平面波叠加法比较,突出该方法的重建优势。仿真和实验结果表明,该方法对非稳态声场重建具有很好的效果,并且重建精度高。     

基于L1范数和正交梯度算子的超分辨率重建

针对超分辨率图像重建的病态问题,设计了一种新的自适应超分辨率图像序列重建算法。该算法在L1范数重建框架下,利用金字塔算法与Lucas-Kanade算法相结合的方法实现图像配准,获得亚像素的运动估计;通过引入移位算子给出了基于正交梯度算子的正则项的实现方法,并从自适应的角度选择正则化参数,最后通过最速下降法求解模型的目标泛函最小值。结果表明:对于模拟实验和真实序列实验,该方法相比于样条插值算法、Tikhonov正则化算法、双边全变差重建算法都有一定的优势,能够取得更好的复原效果,并且由于正则项较为简单,重建所需时间相对减少。     

L-曲线准则在正则化图像复原中的应用

对因大气湍流引起的退化图像复原问题,采用Tikhonov正则化方法将其归结为一个适定的线性方程组的求解问题。当图像边界满足周期性条件时,利用二维离散傅里叶变换及其逆变换即可求得复原图像。在求解方程组中,利用L-曲线准则确定出能够平衡正则化函数和偏差函数的正则化参数,以得到较为理想的复原结果。仿真结果表明,当降质图像的噪声方差不是很大时,该方法能够得到较好的复原效果。     

联合波叠加法的全息理论与实验研究

当空间声场中同时存在多个相干声源时，运用常规近场声全息方法无法重建每个相干声源表面的声学信息，当然也无法预测每个声源单独产生的空间声场，相干声场的全息重建与预测已成为全息技术推广应用过程中亟待解决的问题.在提出联合波叠加法并将其应用于空间声场变换的基础上，对其进行了实验研究.通过对实际相干声场的全息重建与预测，验证了常规波叠加法在相干声场重建中的局限性、联合波叠加法在相干声场全息重建与预测过程的可行性和准确性，还研究了Tikhonov正则化方法在抑制声学逆问题的非适定性中的有效性和滤波系数的选择原则的可行性，以提高全息重建与预测的精度. 关键词： 近场声全息 联合波叠加 相干声场 Tikhonov正则化     

平面近场声全息中正则化参数的确定

近场声全息的逆向重建过程属于线性病态逆问题,必须进行正则化处理。本文对三种基于Tikhonov正则化的参数选择方法,即离差原理法、广义交叉验证法、L曲线法,在不同全息距离、声源频率和信噪比的条件下进行了比较,结果表明,它们在远距离及低噪声环境下难以获得合适的正则化参数。采用等效噪声方差的方法,对其中较为稳定的离差原理进行了改进,使其在较远全息距离及低噪声环境下仍能获得合适的正则化参数。相应的仿真实验表明,改进后的离差原理法在很宽的信噪比(＞6 dB)和较远的全息距离(~10 cm)均能稳定地找到合适的正则化参数。此外,由于该方法无须对全息声压进行平滑处理,其有效重建孔径和全息孔径相等。      

声学CT复杂温度场重建研究

为提高声学CT复杂温度场重建能力,提出一种利用Markov径向基函数逼近和Tikhonov正则化的温度场重建算法,简称MTR算法。该算法首先用Markov径向基函数的线性组合,逼近介质中的复杂声速场分布,然后利用介质中多路径声波传播时间和Tikhonov正则化法,求解声速场分布,进而利用声速与温度的关系获得温度分布。对单热点、三热点和五热点温度场模型进行了仿真重建,结果表明MTR算法热点定位精度高,重建误差小。开发了声学CT温度场重建实验系统,用电加热器在内装1200 kg大豆的实验粮仓中形成热点,MTR重建结果能正确反映热点位置,热点温度重建误差1.3%。可见,MTR算法复杂温度场重建能力强,可望用于实际储粮温度分布监测。      

细菌觅食优化算法在光散射法颗粒粒度反演中的应用

针对函数约束算法中传统的智能算法反演时存在鲁棒性差和易陷入局部最优的缺点,提出了将正则化理论与细菌觅食优化算法相结合应用在颗粒粒度的测量中。引入Tikhonov平滑泛函来构建算法的目标函数,采用L曲线法确定正则化参数;再利用细菌觅食优化算法通过趋向、聚群、复制和迁徙等四种智能行为,迭代计算来搜寻函数的最优解。实验仿真结果表明:利用细菌觅食优化算法实现了在不同程度的随机噪声下的服从J-SB分布的单峰分布的均匀球形颗粒粒度分布反演,其反演结果更稳定,反演精度高,对于实现稳定、快速、准确的颗粒粒度在线测量具有重要的意义。     

分部优化正则化在近场声全息技术中的应用

在基于等效源法近场声全息技术的基础上,对稳定重建过程不适定性的正则化技术进行深入研究.为改善正则化效果,综合考虑Tikhonov正则化和截断奇异值法,提出一种新的分部优化正则化技术.该技术兼取了两种方法的优点,比Tikhonov正则化稳定,避免了过滤波等正则化失效的情况;比截断奇异值法精度高,包含了更多的细节信息.通过对简支板的数值仿真,分别和Tikhonov正则化和截断奇异值法进行了比较,说明了本文所提方法稳定性较好和精度较高的优点.最后通过实验研究进一步证明了本文方法的有效性和正确性.     

炉膛三维温度场重建中Tikhonov正则化和截断奇异值分解算法比较

在煤粉锅炉诊断中火焰辐射能图像扮演着越来越重要的角色, 通过电荷耦合器件(CCD)获得的辐射能图像可以重建出炉内火焰三维温度场, CCD 用于获取视场角内的辐射能图像. 温度场重建的矩阵方程是一个严重病态的方程, 本文使用两种算法(Tikhonov正则化算法和截断奇异值分解(TSVD)算法)来重建温度场. 应用广义交叉检验算法来选取正确的正则化参数. 数值模拟的环境为一个10 m×10 m×10 m的三维炉膛, 系统被划分为10×10×10的1000个网格, 每个网格单元都是边长为1 m的立方体. 在正问题求解所得到的CCD接受信号基础上加上不同随机误差以模拟测量时的CCD接受信号. 研究两种算法重建后的温度重建误差、两者的重建时间, 以及最高温度的重建效果. 初步的研究结果显示, 一般情况下基于Tikhonov算法重建的温度场比基于TSVD算法重建的温度场误差要小, 计算所需时间短, 最高温度重建更准确.     

Method of fundamental solutions with optimal regularization techniques for the Cauchy problem of the Laplace equation with singular points

The purpose of this study is to propose a high-accuracy and fast numerical method for the Cauchy problem of the Laplace equation. Our problem is directly discretized by the method of fundamental solutions (MFS). The Tikhonov regularization method stabilizes a numerical solution of the problem for given Cauchy data with high noises. The accuracy of the numerical solution depends on a regularization parameter of the Tikhonov regularization technique and some parameters of the MFS. The L-curve determines a suitable regularization parameter for obtaining an accurate solution. Numerical experiments show that such a suitable regularization parameter coincides with the optimal one. Moreover, a better choice of the parameters of the MFS is numerically observed. It is noteworthy that a problem whose solution has singular points can successfully be solved. It is concluded that the numerical method proposed in this paper is effective for a problem with an irregular domain, singular points, and the Cauchy data with high noises.     

A V-curve criterion for the parameter optimization of the Tikhonov regularization inversion algorithm for particle sizing

The regularization parameter plays an important role in applying the Tikhonov regularization method to recover the particle size distribution from dynamic light scattering experiments. The so-called V-curve, which is a plot of the product of the residual norm and the norm of the recovered distribution versus all valid regularization parameters, can be used to estimate the result of inversion. Numerical simulation demonstrated that the resultant V-curve can be applied to optimize the regularization parameter. The regularization parameter is optimized corresponding to the minimum value of the V-curve. Simulation and experimental results show that stable distributions can be retrieved using the Tikhonov regularization with optimum parameter for unimodal particle size distributions.     

An adaptive greedy technique for inverse boundary determination problem

In this paper, the method of fundamental solutions (MFS) is employed for determining an unknown portion of the boundary from the Cauchy data specified on parts of the boundary. We propose a new numerical method with adaptive placement of source points in the MFS to solve the inverse boundary determination problem. Since the MFS source points placement here is not trivial due to the unknown boundary, we employ an adaptive technique to choose a sub-optimal arrangement of source points on various fictitious boundaries. Afterwards, the standard Tikhonov regularization method is used to solve ill-conditional matrix equation, while the regularization parameter is chosen by the L-curve criterion. The numerical studies of both open and closed fictitious boundaries are considered. It is shown that the proposed method is effective and stable even for data with relatively high noise levels.     

Least squares approach in wavenumber domain for sound field recording and reproduction using multiple parallel linear arrays

A novel signal processing method is proposed for sound field recording and reproduction using multiple parallel linear microphone and loudspeaker arrays. In sound field recording and reproduction, the problem is how to calculate the transfer filters that transform the signals recorded by microphones into the driving signals of the loudspeakers. The proposed method is based on the spatial Fourier transform in the horizontal angle combined with the least squares (LS) approach in the elevation angle. In the proposed method, the signals recorded by each linear microphone array and those that drive each loudspeaker array are decomposed into the wavenumber domain by the spatial Fourier transform in the horizontal direction. The transfer filters are then calculated by the LS approach in the wavenumber domain. As a result, the size of the matrix of each transfer function in the wavenumber domain is much smaller than that of the conventional LS approach in the temporal frequency domain (LSTF), and well-conditioned stable transfer filters can be obtained with low computational cost without regularization. Computer simulation results show that the proposed method reconstructed a sound field around the control points as accurately as the conventional LSTF.     

Sound source reconstruction using inverse boundary element calculations

Whereas standard boundary element calculations focus on the forward problem of computing the radiated acoustic field from a vibrating structure, the aim in this work is to reverse the process, i.e., to determine vibration from acoustic field data. This inverse problem is brought on a form suited for solution by means of an inverse boundary element method. Since the numerical treatment of the inverse source reconstruction results in a discrete ill-posed problem, regularization is imposed to avoid unstable solutions dominated by errors. In the present work the emphasis is on Tikhonov regularization and parameter-choice methods not requiring an error-norm estimate for choosing the right amount of regularization. Several parameter-choice strategies have been presented lately, but it still remains to be seen how well these can handle industrial applications with real measurement data. In the present work it is demonstrated that the L-curve criterion is robust with respect to the errors in a real measurement situation. In particular, it is shown that the L-curve criterion is superior to the more conventional generalized cross-validation (GCV) approach for the present tire noise studies.