基于DLA模型的花式纱线真实感模拟算法

主要研究了分形生长理论的基本模型——扩散有限凝聚（DLA）模型,分析并实现了DLA模型的计算机模拟方法,并对模型演变进行了讨论,得到了形式多样的凝聚效果。并且将DLA模型结合纱线和织物模拟相关技术和结论,用双侧线性DLA模型算法生成纱线毛羽模版,与基础纱线进行纹理合成,对纱线毛羽效果进行了较为真实的模拟。用这种方法能够较为逼真的反应出花式纱线不规则、随机多变且局部自相似的毛羽特征。     

基于DLA模型的人体血管生长模拟方法

针对现有研究血管曲线结构生成方法均是从主干到支干的形式,与血管自然生长不符,导致构建的人体血管模型与实际生长结构存在一定差异的局限性的问题,提出一种模拟人体血管生长的分形算法,即基于受限扩散凝聚(DLA)模型的人体血管生长模拟算法.首先结合分形算法与约束构建优化方法的特点,建立兼具布朗运动与定向运动的血管曲线自然生长方法;然后运用分形算法中的确定型林氏无关系统(D0L)生成血管曲线结构,以支干生成主干的方式模拟大量自然状态下凝聚而成的分形图形,使构建的血管模型逼真自然血管的生长结构特性.对模拟血管的生长实验与分析结果表明,该算法具有可行性且精度得到保证.     

基于DLA的虚拟植物根系模拟方法研究

传统的根系研究模型注重于根系外观形态的模拟,而将根系的生理生态结合起来的模拟方法则较少。对于农业信息技术而言,根系生理生态的研究模型能更好地服务于农林业领域。文中在研究了DLA模型的特点及其应用的基础上,分析了基本DLA模型的不足并提出了一种改进的DLA模型,用于植物须根系的向水性模拟。该模型结合参数可控方法体现了植物须根系向水性的动态生长过程,一定程度上符合实际植物的生长机理,并在VC＋＋6.0上给出了模拟效果图。模拟结果显示,改进的模型能够逼真、细腻地模拟须根系的向水性生长形态。最后指出了改进DLA算法关于根系建模进一步的研究方向。     

团簇-团簇聚集模型及模拟实现

概述了分形及其应用,介绍了团簇一团簇聚集(Cluster-Cluster Aggregation)模型及其维数,给出了该模型的一种模拟分形生长算法,在VC 6．0下实现了CCA分形生长的模拟及维数计算,并对结果进行了分析和讨论。本文方法能在普通微机上较好地实现CCA分形生长模拟,在小规模粒子时,能动态观察分形生长的过程,实时性较好。     

基于分形和肤色模型的自然态人脸检测方法研究

在对当前人脸检测问题研究过程中常用方法及其特点进行论述基础上,分析了利用分形特征实现人脸检测的可行性,提出了基于分形和肤色模型的自然态人脸检测方法.通过大量实验可知,该算法既克服了传统的基于启发式(知识)模型的方法和基于统计模型方法适应变化能力差、检测速度慢的缺点,又克服了单纯的肤色模型算法对肤色相近区域误检率高的缺点,利用人脸图像所特有分形特征,可以实现自然态环境下,多角度、多姿态人脸检测.     

分形方法在心电仿真中的应用

在基于细胞电生理的心电活动仿真中,传导系统非常重要,但由于Purkinje纤维分布的复杂性使得心室传导系统的构建非常困难。根据Purkinje纤维分布特点,用分形理论对心室传导系统进行了描述,并采用分形树模型,基于文法规则,提出了分形树的生成规则,通过分形树的曲面生长算法实现心室Purkinje传导系统的构建。该模型应用于三维心室电活动仿真中,取得了较好效果。     

基于面向对象的分形植物模拟类库研究与实现

在研究分形L系统植物树木模拟算法基础之上,分析了面向对象技术在分形模拟中的应用,提出了一种基于面向对象的分形植物生成方法.此方法利用面向对象技术的优势,克服了传统分形图形具有的无序性、图形复用率低等缺陷,实现了图形生成快、易移植等特点,扩大了分形图形的应用领域.     

支持森林场景动态生成的树木生长模拟

为了解决传统L文法中树木建模困难和运行效率低的问题,提出了支持森林场景动态生成的树木生长模拟方法.该方法基于分形元建立树木模型,在此基础上对大规模树木实体实例化的简化算法进行研究,将基于视点的多分辨率简化模型融入树木建模方法之中,并选择典型树种进行生长模拟.实验结果表明,该方法能较真实地模拟树木的生长过程,并实现在虚拟森林场景中的实时漫游.     

人工植物的动态结构模型研究

首先回顾了人工植物形态结构模型中两种主要方法———L-系统与分形,并对其人工生命特征进行了分析。针对两种方法存在比较抽象,不易理解和使用等问题,该文提出了基于状态空间的人工植物结构模型。理论分析和仿真结果说明,该模型综合了L-系统并行生长与分形自相似特征,具有人工生命特征;能够连续、动态反映植物结构的生成,便捷易行;适合应用于诸如虚拟农业、虚拟植物科研与教学等领域。     

分形在模拟植物生长中的应用

分形已成为当今最热门的研究领域之一。本文基于分形提出了一种模拟植物生长的实用方法，并取得了良好效果。     

Fractal model for simulation of frost formation and growth

A planar fractal model for simulation of frost formation and growth was proposed based on diffusion limited aggregation(DLA)model and the computational simulation was carried out in this paper.By changing the times of program running circulation and the ratio of random particles generated,the simulation figures were gained under different conditions.A microscope is used to observe the shape and structure of frost layer and a digital camera with high resolution is used to record the pattern of frost layer at...     

Diffusion Limited Aggregation: Algorithm optimization revisited

The Diffusion Limited Aggregation (DLA) model developed by Witten and Sander in 1978 is useful in modeling a large class of growth phenomena with local dependence. Besides its simplicity this aggregation model has a complex behavior that can be observed at the patterns generated. We propose on this work a brief review of some important proprieties of this model and present an algorithm to simulate a DLA aggregates that simpler and efficient compared to others found in the literature.     

Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.     

基于低通滤波的连续刚构桥冲击系数试验研究

为了明确特殊桥梁冲击系数测试方法及其适用性,以高墩大跨连续刚构桥为研究对象,通过现场随机车流试验获得了典型截面的应变时程曲线,研究了低通滤波限值对桥梁结构最大动态响应及冲击系数的影响,基于传统试验法及加权平均法分析了不同滤波限值下的应变冲击系数,通过对比分析给出了合理的滤波上限值及计算应变静态分量时的截止频率值.研究结果表明:不同的低通滤波值对应变冲击系数计算值有影响;采用传统定义法计算冲击系数,1 Hz可作为该类结构求近似静态应变响应的低通截止频率;采用加权平均法计算高墩大跨连续刚构桥这类特殊结构的冲击系数时,对时程曲线不进行低通滤波处理就可以满足实际工程精度要求.     

An elastic contour matching model for tropical cyclone patternrecognition

In this paper, an elastic graph dynamic link model (EGDLM) based on elastic contour matching is proposed to automate the Dvorak technique for tropical cyclone (TC) pattern interpretation from satellite images. This method integrates traditional dynamic link architecture (DLA) for neural dynamics and the active contour model (ACM) for contour extraction of TC patterns. Using satellite pictures provided by National Oceanic and Atmospheric Administration (NOAA), 120 tropical cyclone cases that appeared in the period from 1990 to 1998 were extracted for the study. An overall correct rate for TC classification was found to be above 95%. For hurricanes with distinct "eye" formation, the model reported a deviation within 3 km from the "actual eye" location, which was obtained from the aircraft measurement of minimum surface pressure by reconnaissance. Compared with the classical DLA model, the proposed model has simplified the feature representation, the network initialization, and the training process. This leads to a tremendous improvement of recognition performance by more than 1000 times.     

Morphological diagram of diffusion driven aggregate growth in plane: Competition of anisotropy and adhesion

Two-dimensional structures grown with Witten and Sander algorithm are investigated. We analyze clusters grown off-lattice and clusters grown with antenna method with Nfp=3,4,5,6,7 and 8 allowed growth directions. With the help of variable probe particles technique we measure fractal dimension of such clusters D(N) as a function of their size N. We propose that in the thermodynamic limit of infinite cluster size the aggregates grown with high degree of anisotropy (Nfp=3,4,5) tend to have fractal dimension D equal to 3/2, while off-lattice aggregates and aggregates with lower anisotropy (Nfp>6) have D≈1.710. Noise-reduction procedure results in the change of universality class for DLA. For high enough noise-reduction value clusters with Nfp?6 have fractal dimension going to 3/2 when N→∞.     

Analysis and modeling of biological systems using fractal geometry

Fractal objects which, by definition, are objects that have scale-invariant shapes and fractional scaling dimensions (fractal dimension) with magnitudes related to the complexity of the objects, are ubiquitous in nature. In particular, many biological structures and systems have fractal properties and therefore may be well studied and modelled using fractal geometry. In the box counting algorithm, one of several different approaches to calculate the fractal dimension, one determines, for several values of, the number of boxesN() with side length needed to completely cover the studied object. IfN() andr are found to be related by the power law relationshipN(r) r ^–D, whereD is the scaling dimension, and ifD is a non-integer, then the object is fractal andD is the fractal dimension.In certain circumstances in which one may need to calculate the fractal dimension of a three-dimensional fractal from a two-dimensional projection (e.g. an X-ray), a new mathematical relationship may be utilized to obtain the actual dimensionD; it readsD=–log[1–(1–R ^2– D _p) ^R ]/logR + 3, whereD _p is the fractal dimension of the two-dimensional planar projection of the object andR is the box size used in calculating the box-counting dimension.Fractal dimension calculations have been found to be particularly useful as quantitative indices of the degree of coronary vascularity and the degree of heart interbeat interval variability. Fractal growth models such as diffusion limited aggregation (DLA) can be used to model artery growth.To sum up, fractal geometry is very useful in studying and modeling certain scale-invariant biological structures or systems which may not be easily described with Euclidean shapes.