An adaptive sampling method for Kriging surrogate model with multiple outputs

The sample distribution has a vital influence on the quality of a Kriging surrogate model, which may further influence the required cost or convergence of the surrogate model-based design and optimization problems. Adaptive sampling methods utilize the information from existing samples to reasonably allocate the sequential samples, which can generally build a more accurate Kriging surrogate model under the same computational budget. However, most of the existing adaptive sampling methods for the Kriging surrogate model are only available for single-output problems, and there are few studies on problems with multiple responses. In this paper, an adaptive sampling method based on Delaunay triangulation and technique for order preference by similarity to ideal solution (TOPSIS) is proposed for Kriging surrogate model with multiple outputs (mKMDT). In the proposed mKMDT, Delaunay triangulation is used to partition the design space into multiple triangle regions, whose area denotes the dispersion of the sample points. The prediction error at each triangle’s centroid represents the local approximation error. Specifically, three different strategies are developed when allocating weights to the area and the prediction error of each triangle with the entropy method and the TOPSIS method. The performance of the proposed method is illustrated through numerical examples with different numbers of outputs and a collision problem between the missile and the adapter. Results show that the proposed method can construct an accuracy surrogate model with few samples, which is useful for practical engineering design problems with multiple outputs.

     

基于Delaunay三角网的克里金并行算法优化

当采样点数据量较大时, 可以采用Delaunay三角剖分建立三角网来使用局部邻域采样点进行克里金插值. 但是该算法需要对每个插值点拟合半变异函数, 插值点规模大时造成巨大开销. 为此, 本文提出了一种以三角形为单位拟合半变异函数的克里金插值方法, 采用CPU-GPU负载均衡将部分计算优化, 充分考虑不均匀样本对克里金插值效果的影响. 结果表明, 本文算法能够保证不均匀样本集的插值效果, 提升了计算性能且能够保证较高的精度.     

图像和视频的低多边形渲染

低多边形是近来艺术设计界的热门风格.为了提高图像和视频低多边形风格化的质量,提出一种基于边缘特征和超像素分割的图像和视频低多边形渲染方法.首先提取相邻超像素的交点以及对特征边和超像素边界的差集的均匀采样点作为三角网格顶点,并执行Delaunay三角剖分来生成初始三角网格;然后采用带约束的二次误差度量方法对生成的网格进行...     

点与三角形位置关系对三角网格拓扑的影响

逐点添加、局部优化的Watson算法和局部变换法是生成大规模离散点集Delaunay三角网格的常用方法。点与三角形位置关系判别和三角形外接圆包含点的测试分别是局部变换法和Watson算法正确生成Delaunay三角网格的重要环节。计算误差会导致点与三角形位置关系以及三角形外接圆包含点的错误判别,从而生成几何拓扑关系不正确的三角网格。采用相对位置坐标可以提高面积坐标和外接圆圆心、半径的计算精度。以等高线地图采集的地形数据为例,用改进的算法生成了包含393252个离散点的Delaunay三角网格。     

A local adaptive sampling method for reliability-based design optimization using Kriging model

Reliability-based design optimization (RBDO) in practical applications is hindered by its huge computational cost during structure reliability evaluating process. Kriging-model-based RBDO is an effective method to overcome this difficulty. However, the accuracy of Kriging model depends directly on how to select the sample points. In this paper, the local adaptive sampling (LAS) is proposed to enhance the efficiency of constructing Kriging models for RBDO problems. In LAS, after initialization, new samples for probabilistic constraints are mainly selected within the local region around the current design point from each optimization iteration, and in the local sampling region, sample points are first considered to be located on the limit state constraint boundaries. The size of the LAS region is adaptively defined according to the nonlinearity of the performance functions. The computation capability of the proposed method is demonstrated using three mathematical RBDO problems and a honeycomb crash-worthiness design application. The comparison results show that the proposed method is very efficient.     

复杂地质体中多值面的网格生成算法

针对现有的网格生成算法无法处理在自然界中大量存在的多值面地质现象，基于分割－归并方法，提出一种分裂－重构算法。在生成初始约束Delaunay三角形网格之后，遵循连续折线的正负区测试准则，对网格中的局部顶点进行分裂，重构相关的三角形的点、边以及三角形的拓扑关系。实验表明，该算法能够有效地生成多值面的网格。     

Delaunay三角剖分在离群点检测中的应用

在传统的基于[K]近邻的算法中,需要为算法设置邻居参数[k]的值,只有具备相关的先验知识才能确定合适的参数值。为了减少参数对于离群点检测的影响,提出了一种无需参数的基于Delaunay三角剖分的离群点检测算法。Delaunay三角剖分是数值分析以及图形学中的重要基础理论,它的构建无需任何参数,在三角剖分图中的每个数据对象与它空间上相邻的点都存在边直接相连,因此可以形成一种有效的邻居关系。算法首先通过Delaunay三角剖分形成每个点的空间邻居集合,然后根据每个点与它们空间邻居之间的分布特征,计算它们的离群程度,根据离群程度的大小判断该点是否为离群点。通过实验与相关的算法比较,算法具有更好的效果。     

壳空间剖分的隐式曲面三角化

提出了一种曲率自适应的壳空间剖分隐式曲面三角形化新方法.新方法首先采用粒子系统对隐式曲面进行采样,通过高斯曲率约束粒子的生成,使生成的网格模型在曲率大的区域具有较多的小三角形,在曲率小的区域具有较少的大三角形,从而使网格模型更好地逼近隐式曲面.新方法在每个采样粒子处沿曲面法线正负方向延伸适当距离得到两个附加点,对所有附加点进行四面体化形成对隐式曲面逼近的壳空间四面体网格,在每个壳空间四面体中抽取三角形,所有抽取的三角形拼合得到隐式曲面的三角网格表示.与以往方法相比,新的三角网格化方法更具有鲁棒性,可一次性获得高质量的三角形网格.最后给出了对常用隐式曲面进行三角化的实例比较,显示了新方法的有效性.     

基于快速Delaunay三角化的散乱点曲面重建算法

针对现有三维重建算法速度较慢的问题,提出了一种基于快速Delaunay三角化的散乱数据点的三维重建算法。首先,提出一种新的平面Delaunay三角化插入点目标三角形定位算法,利用插入点的方向搜索线与三角形是否相交以及交点个数加速目标三角形定位,不用额外判断点是否在三角形内;其次,自动检测曲面漏洞,利用凸壳的边界拼接方法进行漏洞弥补。实验结果表明,本算法不仅能较好地重建出三维模型,而且有较高的效率。     

基于Delaunay三角剖分的半径补偿新算法

合理的半径补偿算法能有效提高逆向工程的最终精度.在分析了现有半径补偿算法及其相应优缺点的基础上,针对三角网格法,通过Delaunay三角剖分思想的引入,提出了一种基于Delaunay三角剖分的半径补偿新算法,并对其中三角剖分的优化准则、边界点的处理等关键技术进行了详细的阐述,最后以增压器叶轮为例,实现了叶轮叶面测量数据的半径补偿.     

Topology, accuracy, and quality of isosurface meshes using dynamic particles

This paper describes a method for constructing isosurface triangulations of sampled, volumetric, three-dimensional scalar fields. The resulting meshes consist of triangles that are of consistently high quality, making them well suited for accurate interpolation of scalar and vector-valued quantities, as required for numerous applications in visualization and numerical simulation. The proposed method does not rely on a local construction or adjustment of triangles as is done, for instance, in advancing wavefront or adaptive refinement methods. Instead, a system of dynamic particles optimally samples an implicit function such that the particles' relative positions can produce a topologically correct Delaunay triangulation. Thus, the proposed method relies on a global placement of triangle vertices. The main contributions of the paper are the integration of dynamic particles systems with surface sampling theory and PDE-based methods for controlling the local variability of particle densities, as well as detailing a practical method that accommodates Delaunay sampling requirements to generate sparse sets of points for the production of high-quality tessellations.     

Extended Gaussian Kriging for computer experiments in engineering design

Metamodeling or surrogate modeling is becoming increasingly popular for product design optimization in manufacture industries. In this paper, an extended Gaussian Kriging method is proposed to improve the prediction performance of widely used ordinary Kriging in engineering design. Unlike the forgoing approaches, the proposed method places a variance-varying Gaussian prior on the unknown regression coefficients in the mean model of Kriging and makes prediction at untried design points based on the principle of Bayesian maximum a posterior. The achieved regression mean model is adaptive, therefore capable of capturing more effectively the overall trend of computer responses and leading to a more accurate metamodel. Particularly, the regression coefficients in the mean model are estimated by a fast numerical algorithm, making extended Gaussian Kriging implemented roughly as efficient as ordinary Kriging. Experiment results on several examples are presented, showing remarkable improvement in prediction using extended Gaussian Kriging over ordinary Kriging and several other metamodeling methods.     

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.     

A new combinatorial approach to surface reconstruction with sharp features

This paper presents a new combinatorial approach to surface reconstruction with sharp features. Different from other postprocessing methods, the proposed method provides a systematic way to identify and reconstruct sharp features from unorganized sample points in one integrated reconstruction process. In addition, unlike other approximation methods, the reconstructed triangulated surface is guaranteed to pass through the original sample points. In this paper, the sample points in the sharp regions are defined as characteristic vertices (c-vertices), and their associated poles (c-poles) are used as a "sculptor" to extract triangles from a Delaunay structure for the sharp features. But, for smooth surface regions, an efficient region-growing scheme is used for triangle extraction and connection. Since only the c-poles associated with the sharp regions are used to participate in the Delaunay computation with the sample points, the proposed algorithm is adaptive in the sense that, given a sampled object with less sharp features, the triangulation becomes more efficient. To validate the proposed algorithm, some detailed illustrations are given. Experimental results show that it is robust and highly efficient.     

平面简单闭合曲线离散采样与重建算法

提出一种鲁棒的平面简单闭合曲线离散采样与重建算法。算法分为采样过程和重 建过程两部分。采样部分首先对平面闭合曲线均匀取点,然后计算各点到曲线所围平面区域中 轴的最近距离,最后根据所求距离确定采样间隔,获取采样点集;重建部分首先构建采样点集 的Delaunay 三角剖分,然后从得到的三角形中选择边构建初始化图形,最后通过修改该图形获 得重建图形。实验表明算法得到的采样点较少且能反映曲线的局部几何特性,重建图形能够较 好地表示原闭合曲线的形状及走向。     

球面凸类图形Delaunay三角剖分再分算法及其收敛性分析

在计算曲面Ricci Flow时,会因为三角网格中存在过小的角而出现不收敛的情况。针对这种不收敛的问题,提出一种提高最小角角度的球面凸类图形Delaunay三角剖分再分算法。首先,给出球面凸类图形Delaunay三角剖分再分算法。它的核心操作有两个：1）如果某条Delaunay劣弧被"侵占",通过添加Delaunay劣弧中点分割Delaunay劣弧;2）如果存在"瘦"球面三角形,通过添加球面三角形外接球面小圆圆心分解球面三角形。然后,利用局部特征尺度探索出所提算法的收敛条件并给出输出顶点的一个上界公式。根据实验输出的网格验证,所提算法网格生成的球面三角形没有狭小的角,适合用来计算Ricci Flow。     

三维散乱点云快速曲面重建算法

提出了一种基于Delaunay三角剖分的三维散乱点云快速曲面重建算法。算法首先计算点云的Delaunay三角剖分, 从Delaunay四面体提取初始三角网格, 根据Voronoi体元的特征构造优先队列并生成种子三角网格, 然后通过区域生长的方式进行流形提取。实验结果表明, 该算法可以高效、稳定地重构具有复杂拓扑结构、非封闭曲面甚至是非均匀采样的点云数据。与传统的基于Delaunay的方法比较, 该算法仅需要进行一次Delaunay三角剖分, 无须极点的计算, 因此算法的重构速度快。     

多尺度形态滤波及弹性匹配技术在类星体谱线识别中的应用

本文提出了一种类星体谱线证认方法。首先针对特征为极值点的信号，研究了多尺度膨胀（腐蚀）关于极值点数的两种重要特性及其应用。其一是单调率特性，根据它自动选择滤波器尺度，有效地滤除脉冲噪声；另一种是单调性，它是＂从粗到精＂策略来重新恢复极值特征位置的理论基础。根据这些性质，对光谱进行多尺度膨胀（腐蚀）和特征恢复，以滤除脉冲噪声而不影响谱线特征。然后研究弹性匹配技术应用于谱线证认，并指出了匹配方法中参量的物理意义。该方法对其他一些应用领域也行之有效     

Algorithm for constrained delaunay triangulation

A direct algorithm for computing constrained Delaunay triangulation in 2-D is presented. The algorithm inserts points along the constrained edges (break lines) to maintain the Delaunay criterion. Since many different insertions are possible, the algorithm computes only those that are on the Delaunay circles of each intersected triangle. A shelling procedure is applied to put triangles together in such a way that completeness and correctness are guaranteed.     

Multi-objective optimization for design under uncertainty problems through surrogate modeling in augmented input space

Multi-objective design under uncertainty problems that adopt probabilistic quantities as performance objectives and consider their estimation through stochastic simulation are examined in this paper, focusing on development of a surrogate modeling framework to reduce computational burden for the numerical optimization. The surrogate model is formulated to approximate the system response with respect to both the design variables and the uncertain model parameters, so that it can simultaneously support both the uncertainty propagation and the identification of the Pareto optimal solutions. Kriging is chosen as the metamodel, and its probabilistic nature (its ability to offer a local estimate of the prediction error) is leveraged within different aspects of the framework. To reduce the number of simulations for the expensive system model, an iterative approach is established with adaptive characteristics for controlling the metamodel accuracy. At each iteration, a new metamodel is developed utilizing all available training points. A new Pareto front is then identified utilizing this surrogate model and is compared, for assessing stopping criteria, to the front that was identified in the previous iteration. This comparison utilizes explicitly the potential error associated with the metamodel predictions. If stopping criteria are not achieved, a set of refinement experiments (new training points) is identified and process proceeds to the next iteration. A hybrid design of experiments is considered for this refinement, with a dual goal of global coverage and local exploitation of regions of interest, separately identified for the design variables and the uncertain model parameters.