基于弹性交互作用和非线性扩散的水平集方法

首先介绍了一种基于弹性交互作用和非线性扩散的水平集分割方法。引入非线性扩散获得尺度概念,代替通常水平集演化方程中计算耗时的曲率计算,由基于物体边缘和移动曲线的弹性交互作用生成移动曲线的速度场,扩展了图像力的作用范围,不必预先人为选定初始轮廓,而是由速度场得到初始轮廓,并用水平集方法进行迭代计算,自动控制曲线演化过程中拓扑变化,可以很快得到物体轮廓。基于弹性交互作用的速度场和非线性扩散代替计算耗时的曲率计算,能够明显缩短算法的计算时间,并使用快速傅立叶变换（FFT）计算速度场,进一步提高计算效率。实验结果表明该方法有较好的分割效果和较高的分割效率。     

基于水平集的遗传算法优化的改进

现有的遗传算法大多数没有给出收敛性准则,且存在早熟收敛和收敛速度较慢的难题,为此提出一类新型遗传算法．该算法首先从被优化函数的因变量出发,引入了水平集的新概念,对每一代种群进行分类,把与目标相关的所有信息有机地结合在一起,从而提高了算法的优化速度;其次通过对变异算子进行改进,提高了种群的多样性,有效地避免了遗传算法的早熟收敛;同时还证明了变异算子能提高种群多样性以及新算法能收敛于全局最优解,最后给出了算法的收敛准则．实验表明,该算法正确有效,搜索效率与精度均优于其他方法．     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.     

曲面扩散的变分水平集方法

基于变分水平集方法提出了一种通用的曲面扩散变分模型,其数据项为演化曲面与原曲面的水平集函数Heaviside函数差的平方,规则项为基于整体曲率的通用函数,通过图像扩散模型中的总变差与该模型中的总曲率类比设计该规则项,以实现曲面扩散的任务。为了避免水平集函数的重新初始化,在本文的能量泛函中增加了水平集函数为符号距离函数的惩罚项。所得到的演化方程为4阶偏微分方程,对其对流项采用经典迎风差分格式离散,对其中的扩散项采用中心差分格式。最后通过数值算例验证了模型用于曲面光滑、边缘保持与边缘增强的可行性。     

奇异摄动反应扩散方程数值模拟的粒子群优化算法

针对Shishkin网格方法在数值求解奇异摄动反应扩散方程时,网格过度点参数的选取具有不确定性的缺陷,提出了一种用粒子群优化(PSO)算法估计Shishkin网格参数的方法。首先基于有限差分方法,构造了以误差范数最小为目标的无约束优化问题,并用PSO算法进行了求解。该方法克服了人为选择参数的缺陷。实验结果表明：与单纯形算法相比,PSO算法在优化Shishkin网格参数时能够收敛到全局最优解;而且在最优网格参数下,奇异摄动反应扩散方程的数值结果在边界层的精度也得到了明显提高,进一步说明了所提方法的有效性和可行性。     

一种整合单通道各向异性扩散信息的水平集图像分割方法*

在对Chan-Vese提出的基于简化Mumford-Shah模型(C-V模型)改进的基础上,针对彩色图像、多光谱图像等多通道图像,提出了一种多通道C-V模型水平集图像分割方法.首先将多通道图像分解到各单通道,使用一种新的各向异性扩散方法对各通道进行平滑滤波,然后使用能够整合各通道各向异性扩散信息的多通道C-V模型进行分割.普通彩色图像与多光谱图像数据的实验结果表明,该方法分割质量明显优于传统的C-V模型分割.     

非结构四面体网格上扩散方程的有限体积差分方法

基于二维扩散方程的有限体积方法,构造了三维扩散方程在非结构网格上有限体积差分方法,方法具有高精度和保持通量守恒特性.采取单元中心作为计算节点来减少向量和单元体积的计算量.利用通量守恒条件确定界面中心的函数值,保证了方法的守恒特性.用Lagrange因子插值法更好地适应了非结构网格.采取Bi—CGSTAB方法求解线性代数方程组.计算例子验证方法有效.     

次扩散方程的分数阶参数优化算法

本文考虑一个次扩散方程的反问题,即根据某个时刻解在空间区域上的积分反向优化方程中的分数阶参数.本文说明了当初值为0时,以上问题可能有多个解.基于时空有限元法,我们提出了一个参数优化算法,并利用离散Laplace变换技巧证明了该算法的全局收敛性.另外针对球形区域,利用-△算子的谱分解,我们还提出了一个快速算法.最后两个数值算例验证了算法的有效性.     

基于非线性扩散滤波的水平集模型MRI分割

基于曲线演化的图像分割模型在分割目标时需要在目标附近人为地构造一条曲线作为初始曲线,在此基础上进行演化得到目标边界.当初始曲线离目标边界较远时,影响模型分割的效率;当初始曲线离目标边界很近时,意味着需要过多的人为操作,这使得其时间效率较低且易出错.为此,在非线性扩散滤波的基础上,给出一种半自动初始曲线构造方法,该方法首先利用AOS算法对图像进行非线性扩散滤波,再利用区域信息快速地得到离目标边界很近的初始曲线.然后构造一种新的基于区域信息的速度函数,由水平集模型对其演化,得到了较好的结果.MRI分割实验表明了方法的有效性.     

含曲率的水平集方程在非结构四边形网格上的数值离散方法

在非结构四边形网格上,含曲率的水平集方程采用伽辽金等参有限元方法空间离散,时间离散采用半隐格式．离散形成的线性方程组的系数矩阵是对称的稀疏矩阵,采用共轭梯度法求解．数值算例表明,在笛卡儿网格和随机网格上,含曲率的水平集方程离散格式可达到近似二阶精度．重新初始化方程的离散格式精度可达到近似一阶精度．给出了非结构四边形网格上不光滑界面以曲率收缩的运动过程．在不采用重新初始化的情况下,收缩过程未出现不稳定现象．     

Structure topology optimization: fully coupled level set method via FEMLAB

This paper presents a procedure which can easily implement the 2D compliance minimization structure topology optimization by the level set method using the FEMLAB package. Instead of a finite difference solver for the level set equation, as is usually the case, a finite element solver for the reaction–diffusion equation is used to evolve the material boundaries. All of the optimization procedures are implemented in a user-friendly manner. A FEMLAB code can be downloaded from the homepage www.imtek.de/simulation and is free for educational purposes.     

Topology optimization of multi-material negative Poisson’s ratio metamaterials using a reconciled level set method

Metamaterials are defined as a family of rationally designed artificial materials which can provide extraordinary effective properties compared with their nature counterparts. This paper proposes a level set based method for topology optimization of both single and multiple-material Negative Poisson’s Ratio (NPR) metamaterials. For multi-material topology optimization, the conventional level set method is advanced with a new approach exploiting the reconciled level set (RLS) method. The proposed method simplifies the multi-material topology optimization by evolving each individual material with a single level set function and reconciling the result level set field with the Merriman–Bence–Osher (MBO) operator. The NPR metamaterial design problem is recast as a variational problem, where the effective elastic properties of the spatially periodic microstructure are formulated as the strain energy functionals under uniform displacement boundary conditions. The adjoint variable method is utilized to derive the shape sensitivities by combining the general linear elastic equation with a weak imposition of Dirichlet boundary conditions. The design velocity field is constructed using the steepest descent method and integrated with the level set method. Both single and multiple-material mechanical metamaterials are achieved in 2D and 3D with different Poisson’s ratios and volumes. Benchmark designs are fabricated with multi-material 3D printing at high resolution. The effective auxetic properties of the achieved designs are verified through finite element simulations and characterized using experimental tests as well.     

Topology optimization of shell structures using adaptive inner-front (AIF) level set method

A new topology optimization using adaptive inner-front level set method is presented. In the conventional level set-based topology optimization, the optimum topology strongly depends on the initial level set due to the incapability of inner-front creation during the optimization process. In the present work, in this regard, an algorithm for inner-front creation is proposed in which the sizes, the positions, and the number of new inner-fronts during the optimization process can be globally and consistently identified. In the algorithm, the criterion of inner-front creation for compliance minimization problems of linear elastic structures is chosen as the strain energy density along with volumetric constraint. To facilitate the inner-front creation process, the inner-front creation map is constructed and used to define new level set function. In the implementation of inner-front creation algorithm, to suppress the numerical oscillation of solutions due to the sharp edges in the level set function, domain regularization is carried out by solving the edge smoothing partial differential equation (smoothing PDE). To update the level set function during the optimization process, the least-squares finite element method (LSFEM) is adopted. Through the LSFEM, a symmetric positive definite system matrix is constructed, and non-diffused and non-oscillatory solution for the hyperbolic PDE such as level set equation can be obtained. As applications, three-dimensional topology optimization of shell structures is treated. From the numerical examples, it is shown that the present method brings in much needed flexibility in topologies during the level set-based topology optimization process.     

基于各向异性改进的水平集超声图像去噪算法

针对各向异性扩散可能出现的阶梯效应以及扩散门限难以准确确定、水平集函数只能根据图像梯度区分图像边缘及同质区域的问题,将各向异性扩散中的边缘增强项引入到水平集方程中,同时自适应地估计扩散门限,在去除噪声的同时保持和增强边缘。该方法结合了水平集函数和各向异性扩散的优点,理论分析和实验结果均表明了该算法的去噪效果更好。     

Topological shape optimization of geometrically nonlinear structures using level set method

Using the level set method, a topological shape optimization method is developed for geometrically nonlinear structures in total Lagrangian formulation. The structural boundaries are implicitly represented by the level set function, obtainable from “Hamilton-Jacobi type” equation with “up-wind scheme,” embedded into a fixed initial domain. The method minimizes the compliance through the variations of implicit boundary, satisfying an allowable volume requirement. The required velocity field to solve the Hamilton-Jacobi equation is determined by the descent direction of Lagrangian derived from an optimality condition. Since the homogeneous material property and implicit boundary are utilized, the convergence difficulty is significantly relieved.     

隐式曲面上图像扩散的变分水平集方法

用零水平集函数表达曲面,应用曲面上图像梯度的切投影表达其内蕴梯度,把基于梯度的图像扩散变分模型从平面图像拓展到了隐式曲面图像.首先基于变分水平集方法推导了隐式曲面上图像非线性扩散的通用模型,作为特例,研究了曲面上Charbonnier模型、广义,Ⅳ模型、PM模型的图像噪声去除能力,并通过数值实验验证了这些模型向前、向后扩散及在边缘保持、边缘增强等方面的能力.根据图像修复与图像扩散的联系,将上述模型推广到了图像修复,并通过数值实验进行验证.