Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels

Partial volume effects are often experienced in diffusion-weighted MRI of biologic tissue. This is when the signal attenuation reflects a mixture of diffusion processes, originating from different tissue compartments, residing in the same voxel. Decomposing the mixture requires elaborated models that account for multiple compartments, yet the fitting problem for those models is usually ill posed. We suggest a novel approach for stabilizing the fitting problem of the multiple-tensors model by a variational framework that adds biologically oriented assumption of neighborhood alignments. The framework is designed to address fiber ambiguity caused by a number of neuronal fiber compartments residing in the same voxel. The method requires diffusion data acquired by common, clinically feasible MRI sequences, and is able to derive familiar tensor quantities for each compartment. Neighborhood alignment is performed by adding piece-wise smooth regularization constraints to an energy function. Minimization with the gradient descent method produces a set of diffusion-reaction partial differential equations that describe a tensor-preserving flow towards a best approximation of the data while maintaining the constraints. We analyze fiber compartment separation capabilities on a synthetic model of crossing fibers and on brain areas known to have crossing fibers. We compare the results with diffusion tensor imaging analysis and discuss applications for the framework.     

Intercomparison of Inversion Algorithms for Particle‐Sizing Using Mie Scattering

The applicability of different inversion algorithms to retrieve a size distribution of particles in air from light scattering is examined. The investigation is focused on an optical measurement setup with an elliptical mirror as the main optical element. In order to evaluate the capabilities of the individual inversion methods, light scattering by spherical particles is simulated in the size ranges of 0.1 – 10 μm and 0.05 – 1 μm. The distribution of the particle diameters is modeled with three different parametric functions, i.e., RRSB, logarithmic‐normal and a more specific distribution from an ultrasonic nebulizer. Different kinds of noise, e.g., additive and/or multiplicative, are applied in different levels to the simulated scattering measurement to include real physical measurement conditions. The convergence properties of the scattering simulation are investigated with respect to the number of size classes, and thus, information concerning the size resolution required to simulate a measurement for a given particle size distribution is obtained. Further parameters of interest are the minimum angular resolution of the measurements, the number of size classes of the retrieved particle size distribution and the measured polarization of the scattered light.     

反馈调节的局部正则化鲁棒光流估计

提出了一种引入反馈调节机制的新局部鲁棒光流场计算思想.该思想结合局部法和全局法的特点,首先划分整场为有公共边界的子块,定义距离来判断每个点的子块归属,然后在每个子块内进行光流约束和平滑约束正则化的优化计算.将光流约束中的光强残差作为控制参量,衡量光流准确度的峰值信噪比作为输出,经反馈处理,调节控制参量使光流估计准确度趋向最优.实验结果表明,这种新的结合反馈调节机制局部光流场计算法能使光流准确度获得提升.     

On the numerical solution of ill‐conditioned linear systems by regularization and iteration

We propose to reduce the (spectral) condition number of a given linear system by adding a suitable diagonal matrix to the system matrix, in particular by shifting its spectrum. Iterative procedures are then adopted to recover the solution of the original system. The case of real symmetric positive definite matrices is considered in particular, and several numerical examples are given. This approach has some close relations with Riley's method and with Tikhonov regularization. Moreover, we identify approximately the aforementioned procedure with a true action of preconditioning.     

基于L1正则化的随机梁式结构静力损伤识别方法

提出了一种结合摄动法和L1正则化方法的随机梁式结构静力损伤识别方法。考虑初始模型误差和测量误差的影响,建立了关于随机损伤指数的控制方程,并将摄动法和L1正则化方法相结合,对随机损伤指数的控制方程进行求解,进而从概率的角度对结构的损伤进行识别。损伤试验结果表明,和传统的最小二乘求解法相比,本文方法能够更为准确地识别多处局部损伤的位置及大小,对实际结构损伤检测具有较好的参考价值。     

约束Fractional Tikhonov正则化的模迭代方法

反问题是现在数学物理研究中的一个热点问题,而反问题求解面临的一个本质性困难是不适定性。求解不适定问题的普遍方法是:用与原不适定问题相“邻近”的适定问题的解去逼近原问题的解,这种方法称为正则化方法.如何建立有效的正则化方法是反问题领域中不适定问题研究的重要内容.当前,最为流行的正则化方法有基于变分原理的Tikhonov正则化及其改进方法,此类方法是求解不适定问题的较为有效的方法,在各类反问题的研究中被广泛采用,并得到深入研究.     

Analytical solution of stochastic differential equation by multilayer perceptron neural network approximation of Fokker–Planck equation

The Fokker–Planck equation is a useful tool to analyze the transient probability density function of the states of a stochastic differential equation. In this paper, a multilayer perceptron neural network is utilized to approximate the solution of the Fokker–Planck equation. To use unconstrained optimization in neural network training, a special form of the trial solution is considered to satisfy the initial and boundary conditions. The weights of the neural network are calculated by Levenberg–Marquardt training algorithm with Bayesian regularization. Three practical examples demonstrate the efficiency of the proposed method.     

一类正则化参数自由的线性约束凸优化问题的预测-校正算法

我们对文章的结构做这样的安排:第二节给出本文需要的预备知识;第三节简述单个目标函数问题(1.1)的己有算法和求解可能遇到的困难,第四节给出解决问题的预测-校正方法;第五节和第六节对问题(1.2)分别陈述己有方法的固有困难和我们提出的解决方案.最后,在第七节中,我们为提出的方法给出统一的算法框架,证明这类算法的收敛性和遍历意义下的收敛速率,同时给出我们的一些结论.     

低阶煤热萃取物与瘦煤共炭化焦炭的微观结构及显微强度研究

以众唯瘦煤作为主炼焦煤,大同长焰煤萃取物作为添加剂,进行共炭化处理制备坩埚焦。利用偏光显微镜法定量研究焦炭光学显微组分,获得焦炭的各向异性指数(DRAS);采用XRD及分峰拟合的方法研究了焦炭的微晶粒径(L_c)、芳香缩合度(I_a)、石墨化度(g);利用Raman光谱结合分峰拟合的方法研究了焦炭中理想石墨微晶含量(I_g)。对所得焦炭的光学显微组分进行定量分析发现:大同长焰煤热解萃取产物的添加对共炭化焦炭的光学显微组分有显著的影响,利用偏光显微镜法计算出焦炭的DRAS与XRD和Raman计算的焦炭微晶参数呈现很好一致性。并且,焦炭的显微强度与其微观结构关联性极大。