基于颗粒粒度信息分布特征的动态光散射加权反演

宽分布和双峰分布颗粒的准确反演是动态光散射技术至今未能有效解决的难题,尤其峰值位置比小于2:1且含有大粒径颗粒(350 nm)的双峰分布.造成这一难题的主要原因包括:1)单角度测量数据的粒度信息含量不足;2)常规反演方法对测量数据的噪声抑制以及粒度信息利用缺乏针对性.对测量数据(即光强自相关函数)的研究发现,数据噪声主要分布在长延迟时段,而粒度信息集中分布在衰减延迟时段.基于此,本文提出了采用粒度信息分布为底数、调节参数为指数的权重系数对自相关函数进行加权反演的约束正则化方法.由于采用了与粒度信息分布一致的权重系数,该方法既充分利用了衰减延迟时段的粒度信息,又有效地抑制了长延迟时段的数据噪声.不同噪声水平下,宽分布和双峰分布颗粒体系的反演结果表明,与常规反演方法相比,这一方法可以获得更为准确的宽分布和近双峰分布的反演结果.     

Multiangle dynamic light scattering analysis using a modified Chahine method

Multiangle dynamic light scattering (MDLS) is used to determine particle size distributions (PSDs). The angular intensity weighting coefficients used in the inversion of the PSD data affect dramatically the PSD recovered. Noise in the weighting factors gives rise to poor PSD results. A modified Chahine method, which is insensitive to the noise of the weighting coefficients, is developed for estimating the PSD from MDLS. The method was evaluated through simulated examples that involved unimodal and bimodal PSDs of different shapes and employed for estimating two bimodal PSDs obtained by mixing two standard polystyrene latexes. For comparison, all examples were also analyzed using a nonnegatively-constrained Tikhonov regularization technique typically used for inverting ill-conditioned linear problems. The PSDs estimated by the proposed modified Chahine method were more accurate than those obtained by the Tikhonov technique.     

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。     

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.     

二维有耗色散介质的时域逆散射方法

为了重建二维有耗色散介质的电参数分布,基于Debye模型,应用泛函分析和变分法,提出一种时域逆散射新方法.该方法首先以最小二乘准则构造目标函数,将逆问题表示为约束最小化问题,接着应用罚函数法转化为无约束最小化问题,然后基于变分计算导出闭式的Lagrange函数关于特征参数的Fréchet导数,最后借助梯度算法和时域有限差分法迭代反演Debye模型参数.为了对抗噪声污染和逆问题的病态特性,采用了一阶Tikhonov正则化方法.数值应用中,利用Polak-Ribière-Polyak非线性共轭梯度法,对二维乳     

初始模型对含噪动态光散射数据正则化反演结果的影响

分别采用最小模型矩阵、最平坦模型矩阵、最光滑模型矩阵作为初始化模型,对加入5种不同水平随机噪声的90nm窄单峰、90nm宽单峰和250nm窄单峰、250nm宽单峰颗粒体系的模拟分布进行了正则化反演,并对反演结果进行比较。结果表明:当噪声水平为0时,正则化初始模型的选择对反演结果没有明显影响。随着噪声水平的增加,采用三种初始化模型反演得到的峰值误差和粒度分布误差都随之变大,但采用最平坦模型和最光滑模型反演得到的峰值和粒度分布误差明显小于采用最小初始模型的反演误差。当噪声水平大于0.01时,选择最平坦初始模型获得的粒度分布结果优于采用最光滑初始模型和最小初始模型获得的结果,而采用最光滑初始模型反演得到的峰值优于最平坦初始模型和最小初始模型的反演峰值。因此,采用正则化算法处理含噪动态光散射数据时,为得到最优的粒度分布信息,宜采用最平坦初始模型,若需要获取最准确的峰值信息,则应选择最光滑初始模型。     

一种基于约束共轭梯度的闪光照相图像重建算法

 针对闪光照相系统成像信噪比低的特点,提出了正则化预优约束共轭梯度算法（RPCCG）。RPCCG算法在闪光照相重建方程中引入Tikhonov正则化准则,利用预优约束共轭梯度法迭代求图像重建的最优解。数值试验表明,采用最小二乘+平滑准则的RPCCG算法是一种具有较高的抗噪能力的有效闪光照相图像重建算法,具有良好的收敛性和稳定性以及较高的重建精度。     

Sparsity-constrained SENSE reconstruction: An efficient implementation using a fast composite splitting algorithm

Parallel imaging and compressed sensing have been arguably the most successful and widely used techniques for fast magnetic resonance imaging (MRI). Recent studies have shown that the combination of these two techniques is useful for solving the inverse problem of recovering the image from highly under-sampled k-space data. In sparsity-enforced sensitivity encoding (SENSE) reconstruction, the optimization problem involves data fidelity (L2-norm) constraint and a number of L1-norm regularization terms (i.e. total variation or TV, and L1 norm). This makes the optimization problem difficult to solve due to the non-smooth nature of the regularization terms. In this paper, to effectively solve the sparsity-regularized SENSE reconstruction, we utilize a new optimization method, called fast composite splitting algorithm (FCSA), which was developed for compressed sensing MRI. By using a combination of variable splitting and operator splitting techniques, the FCSA algorithm decouples the large optimization problem into TV and L1 sub-problems, which are then, solved efficiently using existing fast methods. The operator splitting separates the smooth terms from the non-smooth terms, so that both terms are treated in an efficient manner. The final solution to the SENSE reconstruction is obtained by weighted solutions to the sub-problems through an iterative optimization procedure. The FCSA-based parallel MRI technique is tested on MR brain image reconstructions at various acceleration rates and with different sampling trajectories. The results indicate that, for sparsity-regularized SENSE reconstruction, the FCSA-based method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction method.     

一种求解约束优化问题的改进粒子群算法及其应用

针对复杂约束优化问题,提出一种改进的粒子群方法。该粒子群算法对于不满足约束条件的粒子实行全概率接收,但令其目标函数值同为一个很小的常数,以保持粒子的多样性并使最优解在可行域内。另外,在PSO算法的基础上,使惯性权值按对数规律单调递减,同时引进选择遗传算子,以增强其全局寻优性能。数值实验表明:与PSO算法和一些其它优化算法相比,改进算法具有较强的寻优能力和寻优效率。工程应用表明,改进算法具有一定的优越性。     

Characterization of spherical core–shell particles by static light scattering. Estimation of the core- and particle-size distributions

A numerical method is proposed for the characterization of core–shell spherical particles from static light scattering (SLS) measurements. The method is able to estimate the core size distribution (CSD) and the particle size distribution (PSD), through the following two-step procedure: (i) the estimation of the bivariate core–particle size distribution (C–PSD), by solving a linear ill-conditioned inverse problem through a generalized Tikhonov regularization strategy, and (ii) the calculation of the CSD and the PSD from the estimated C–PSD. First, the method was evaluated on the basis of several simulated examples, with polystyrene–poly(methyl methacrylate) core–shell particles of different CSDs and PSDs. Then, two samples of hematite–Yttrium basic carbonate core–shell particles were successfully characterized. In all analyzed examples, acceptable estimates of the PSD and the average diameter of the CSD were obtained. Based on the single-scattering Mie theory, the proposed method is an effective tool for characterizing core–shell colloidal particles larger than their Rayleigh limits without requiring any a-priori assumption on the shapes of the size distributions. Under such conditions, the PSDs can always be adequately estimated, while acceptable CSD estimates are obtained when the core/shell particles exhibit either a high optical contrast, or a moderate optical contrast but with a high ‘average core diameter’/‘average particle diameter’ ratio.     

Visualized numerical assessment for near infrared diffuse optical tomography with contrast-and-size detail analysis

The purpose of this study is to propose an objective contrast-and-size detail (CSD) analysis for near infrared diffuse optical tomography (NIR DOT), of which the concept is derived from the subjective contrast detail (CD) analysis. We define a measure for numerical CSD analysis based on the resolution estimation of contrast and size. Following that, the contrast-and-size map of resolution can be calculated and displayed for each corresponding image in the map; furthermore, a CSD resolution curve can be plotted by calculating the average value of the projection corresponding to the physical quantity/axis (size or contrast). To provide some worked examples about the proposed CSD analysis evaluating the imaging performance of different reconstruction methods, Tikhonov regularization and edge-preserving regularization with different weighting functions were employed. Results suggested that using edge-preserving regularization with the generalized Lorentzian weighting function is the most attractive for the estimation of absorption-coefficient images.     

Spatial resolution limits for the reconstruction of acoustic source strength by inverse methods

One method for deducing the strength of an acoustic source distribution from measurement of the radiated field involves the inversion of the matrix of frequency response functions relating the field measurement points to the strengths of a number of point sources used to represent the source distribution. In practice, the frequency response function matrix to be inverted may very often be ill-conditioned. This ill-conditioning will also often result in an ill-posed problem and thus regularization algorithms are used to produce reasonable solutions. For this purpose, Tikhonov regularization has been applied, and generalized cross-validation (GCV) has been introduced as an effective method for determining the proper amount of regularization without prior knowledge of either the source distribution or the contaminating errors. In the present work, the emphasis is placed on the relationship between the spatial resolution of the reconstructed source distribution and the small singular values of the frequency response function matrix to be inverted. However, the use of Tikhonov regularization often suppresses the effect of small singular values and these are in turn often associated with high spatial frequencies of the source distribution. Thus, the process of regularization produces a useful estimate of the acoustic source strength distribution but with a limited spatial resolution. Furthermore, in the field of Fourier acoustics, the spatial resolution of the reconstructed source distribution is usually limited by the wavelength of the radiation. This paper expresses the relationship between estimation accuracy, spatial resolution, noise-level and source/sensor geometry, when a range of inverse sound radiation problems are regularised using Tikhonov regularization with GCV. The results presented form the basis of guidelines that enable the reconstruction of acoustic source strength with a resolution that is finer than the intrinsic half-wavelength limit.     

The application of regularization technique based on partial optimization in the nearfield acoustic holography

The regularization technique for stabilizing the reconstruction based on the nearfield acoustic holography(NAH) was investigated on the basis of the equivalent source method.In order to obtain higher regularization effect,a regularization method based on the idea of partial optimization was proposed,which inherits the advantages of the Tikhonov and another regularization method—truncated singular value decomposition(TSVD).Through the numerical simulation,it is proved that the proposed method is stabler than the Tikhonov,and more precise than the TSVD.Finally the validity and the feasibility of the proposed method are demonstrated by an experiment carried out in a semi-anechoic room with two speakers.     

基于自适应正则化的超分辨率重建方法

传统正则化超分辨重建得到的图像往往存在过度平滑或伪信息残留的问题,结合超分辨重建模型对重建图像伪信息的产生进行了分析,针对传统方法的不足提出了基于图像区域信息自适应的正则化方法,通过图像的区域信息将图像划分为平滑区与非平滑区域,对不同区域选用不同的先验模型进行约束。同时考虑人眼的视觉感知特性,结合区域信息实现正则化参数的自适应选取。实验结果表明该方法在抑制重建图像伪信息的同时能有效保护细节,效果要优于传统方法与单一的先验模型约束,对于红外与可见光图像重建效果的提升提供了一定的理论参考。     

Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction

For fluorescence molecular tomography(FMT),image quality could be improved by incorporating a sparsity constraint.The L1 norm regularization method has been proven better than the L2 norm,like Tikhonov regularization.However,the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy.By studying the reason,a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that reduces the iterations significantly and achieves good image quality.It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less oversparsity and is more applicable for heterogeneous-target reconstruction,compared with several regularization methods implemented in this Letter.     

Identifying the inhomogeneous properties of an orthotropic elastic layer

A scheme is proposed for reconstructing inhomogeneously thick elasticity modules of an orthotropic layer from acoustic sounding data. The problem of reconstruction has been reduced to stepwise reconstruction of the functions characterizing the elasticity modules, based on iteration regularization and A.N. Tikhonov’s regularization method, which utilize analysis of the averaged characteristics. A computer experiment is performed for different inhomogeneity layers, the effective frequency sounding regions for identification are revealed, and various aspects of numerical realization are discussed.     

光偏折层析的Tikhonov正则化重建技术

从不适定问题的求解思想出发,建立了使用Tikhonov正则化方法计算流场参量分布的光偏折层析重建技术。理论上详细讨论了偏折信息的转化、投影方程组的正则化、以及由共轭梯度法实现求解等三个主要步骤。用数值模拟考察该方法对非对称温度分布的重建效果,分别计算了对应不同采样数的欠定与超定投影方程组。结果表明,10次迭代后重建分布的平均误差和峰值误差分别为2.1%和5.2%。使用正则化方法对采集的多方向叠栅偏折投影进行计算,重建出双峰温度场。结果表明,使用正则化方法的双峰值相对误差略高,迭代次数减少,可提高重建效率。     

Application of multi-phase particle swarm optimization technique to retrieve the particle size distribution

The multi-phase particle swarm optimization (MPPSO) technique is applied to retrieve the particle size distribution (PSD) under dependent model.Based on the Mie theory and the Lambert-Beer theory, three PSDs, i.e., the Rosin-Rammer (R-R) distribution, the normal distribution, and the logarithmic normal distribution, are estimated by MPPSO algorithm.The results confirm the potential of the proposed approach and show its effectiveness.It may provide a new technique to improve the accuracy and reliability of the PSD inverse calculation.     

Global field interpolation for particle methods

A common problem in particle simulations is effective field interpolation. Field interpolation is any method for creating an accurate representation of a given continuous field by a linear combination of overlapping basis functions. This paper presents two techniques for field interpolation, based on a radial basis function (RBF) formulation using Gaussians. The application in mind is vortex methods, where one needs to determine the circulation (or strength) of individual vortex particles with known position and scale to represent a given vorticity field. This process is required both to initially discretize a given vorticity field, and to replace a configuration of particles with another for the purposes of maintaining spatial accuracy. The first technique presented is formulated as an RBF collocation problem, and obtains a solution accurately and with excellent algorithmic efficiency by means of a preconditioned iterative method. The preconditioner is a sparse approximation, based on localization, to the dense coefficient matrix of the RBF system. The second technique uses approximate solutions to the reverse heat equation, recognizing that the standard regularization used in vortex methods (estimating particle strengths using the local value of vorticity multiplied by particle area/volume) corresponds to a Gaussian blurring of the original field. A single time step is used, thus avoiding amplification of high frequencies, and accurate solutions are produced using explicit finite difference methods. Computational experiments were performed in two dimensions, demonstrating the accuracy and convergence of the proposed techniques. Application in three dimensions is straightforward, as radial basis function interpolation is neutral to dimension, but will require more computational effort.     

对利用动态光散射法测量颗粒粒径和液体黏度的改进

光散射技术通过测量悬浮液中布朗运动颗粒的平移扩散系数,得到颗粒流体力学直径或液体黏度.本文由单参数模型入手,建立了低颗粒浓度下,单颗粒平移扩散系数与颗粒集体平移扩散系数和颗粒浓度之间的线性依存关系并将其引入光散射法中,从而对现有的测量方法进行了改进.改进后的测量方法可实现纳米尺度球型颗粒标称直径的测量和液体黏度的绝对法测量.以聚苯乙烯颗粒+水和二氧化硅颗粒+乙醇两个分散系为参考样本,通过实验,验证了改进后方法的可行性.此外,还针对上述两个分散系,实验探讨了温度和颗粒浓度对颗粒集体平移扩散系数的影响规律,发现聚苯乙烯颗粒+水分散系中,颗粒间相互作用表现为引力;二氧化硅颗粒+乙醇分散系中,颗粒间相互作用表现为斥力.讨论了颗粒集体平移扩散系数随颗粒浓度变化规律与第二渗透维里系数的关系.