植物三维建模方综述

A series of plant’s 3D modeling methods are introduced in this paper.They include fractal modeling method,A-system,particle system,L-system,layer based modeling method, mcdeling method faithtul to botanical structure and development and interactive modeling method.This will be helpful in the future research of plant’s 3D model.     

不规则曲线模拟技术的研究

曲线的生成是计算机图形学中的重要内容,本文给出了基于迭代函数系统（IFS）的不规则曲线的模拟方法。首先介绍了迭代函数系统的基本概念,然后构造出分形插值函数,给出了具体算法,讨论了其维数性质,最后给出了应用实例。     

基于迭代函数的不规则图像快速生成算法

本文以迭代函数为基础,对不规则图形采用快速算法绘制分形图。     

基于L系统的植物建模方法改进

为了克服将L系统代码用于编程指导时繁琐的缺点,在简要介绍了L系统代码生成的各阶段图形的性质进行了分析,利用分析结果,给出了一种改进方法,该方法首先把系统代码转换成简单递归表达式,然后,根据这个表达式编写具有递归结构的程序,从而不仅避免了传统方法中庞大的链表结构和复杂的遍历过程,而且使植物建模和实现更加简洁而高效。最后给出该方法的一个具体应用实例。     

虚拟战场中不规则物体的实时显示研究

虚拟战场中不规则物体的实时显示能够增强人们在虚拟环境中的沉浸感。文中论述了在实时性要求很高的某型车辆视景仿真系统中,应用粒子系统方法构建简单模型实现爆炸效果的具体步骤,并提出了提高算法实时性的技巧,此方法已应用于仿真系统中,在一般配置的微机上实现20f/s的刷新率,模拟效果逼真,同时满足了系统实时性。     

虚拟战场中不规则物体的实时显示研究

虚拟战场中不规则物体的实时显示能够增强人们在虚拟环境中的沉浸感.文中论述了在实时性要求很高的某型车辆视景仿真系统中,应用粒子系统方法构建简单模型实现爆炸效果的具体步骤,并提出了提高算法实时性的技巧,此方法已应用于仿真系统中,在一般配置的微机上实现20f/S的刷新率,模拟效果逼真,同时满足了系统实时性.     

基于CV／CAD的三维物体几何建模

在虚拟现实等技术领域中,都涉及到由现实世界中的实际景物建立对应的计算机描述的虚拟景物的问题,为此提出了利用计算机视觉与CAD几何建模技术相结合的三维珠体建模途径,首先通过编码光栅方法获取三维物体的深度图象,并采用数学形态学的方法加以分割,然后利用代数曲面拟合手段对分割后的三维曲面片进行重建,并使用CAD几何建模工具由重建的曲面片构成物体的几何模型,该文给出了初步的实验结果,证明所提出的技术途径基本可行。     

基于图像建模技术研究综述与展望

基于图像建模技术是计算机图形学和计算机视觉领域共同关心的重要问题．文中侧重从计算机图形学的研究角度对基于图像建模技术进行了综述,介绍了近年来提出的典型的基于图像建模方法及其最新研究进展,给出了这些方法的基本原理并加以分析比较,最后对基于图像建模技术的未来研究给出了一些建议。     

一种基于骨架的可变形物体建模方法

提出了一种新的基于骨架的混合型的软组织建模方法,用于虚拟手术仿真及手术训练。介绍了通过自动的方法抽取物体的骨架,建立物体的表面物理模型、体物理模型以及对应的计算模型。还介绍了利用骨架进行碰撞检测及外力计算的方法并介绍了实验的软件结构及实验结果。     

细胞自动机及其在复杂系统研究中的应用

近年来,随着计算机技术的飞速发展,细胞自动机理论及其在复杂系统研究领域中应用的研究受到科学界的高度重视。由于细胞自动机具有结构简单、并行计算能力强和易于在计算机上实现系统的可视化模拟等特点,它已成为复杂系统研究领域一种具有广阔发展前景的新方法,在复杂系统(如:生物系统、交通系统、经济系统、环境系统、工程系统、社会系统和各种工业生产系统等)研究中得到了广泛的应用,并取得了令人瞩目的成果。文章在简要介绍复杂系统、细胞自动机概念及特点的基础上,分析了二者的相互关系,较详细地综述了细胞自动机在各种复杂系统研究领域的应用,最后结合笔者的研究指出了细胞自动机理论及其在复杂系统应用研究中有待进一步完善和深入研究的问题。     

薄膜生长的元胞自动机模型

在一定条件下,单个原子或原子团簇在液体基底表面无规律扩散和凝聚,并形成具有分形结构的分枝状凝聚体,形成此类凝聚体所需的时间非常短(约1—10s),直接观察原子或原子团簇在液体表面的成核、扩散及凝聚过程十分困难。元胞自动机是研究复杂体系的一种理想化模型,特别适合那些难以用数学定量描述的复杂动态体系实施计算机模拟。针对在液体基底表面的金属原子会凝聚形成具有特殊结构的分形凝聚体这一实验结果,建立了团簇一团簇凝聚的元胞自动机模型,对此类液体基底表面的金属原子分枝状凝聚体的生长过程进行了计算机模拟。模拟结果表明,凝聚体的分形维数和回旋半径都与实验结果相一致,为研究薄膜生长机理提供了一种新的方法。     

一种基于受限网络的移动对象索引

为了有效地支持城市交通网络中移动对象的过去、现在和将来的轨迹查询,在基于模拟预测的位置表示模型基础上,提出了一种两层R树加上一个表结构的复合索引结构AUC(Adaptive Unit Compounding).根据城市交通网的特征,采用了一种带有环形交叉口的元胞自动机模型模拟移动对象的将来轨迹,并用线性回归和圆弧曲线拟合分别得到对象在规则路段和交叉口的轨迹预测方程;根据移动对象的运动特性,采用了一种新的自适应单元(AU)作为索引结构的基本单位.实验表明,AUC索引的查询和更新性能都要优于TPR树和TB树.     

Cellular particle swarm optimization

This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.     

Asynchronous cellular learning automata

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous cellular learning automata) is needed. In this paper, we introduce asynchronous cellular learning automata and study its steady state behavior. Then an application of this new model to cellular networks has been presented.     

Multi swarm bare bones particle swarm optimization with distribution adaption

Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. However, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape. This paper also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other well-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions.     

A cellular automata model considering diversity associated with HIV infection

The diversity of human immunodeficiency virus (HIV) in vivo has been reported. In this article, we propose a cellular automata (CA) model describing the interactions between the immune system and HIV, and examine the effect of the diversity of these interactions. The novel aspects of our CA model are that it not only considers four states (HIV, virgin, dead, infect) but also the diversity exhibited by both HIV and T cells. We simulated maximum diversities for these states by simulating CA on a computer. The model revealed that increased diversity had the effect of increasing the HIV population and simulation steps. In addition, we observed that the CA model accurately reflects the occurrence of infection, incubation period, and the development of AIDS. The CA model demonstrated that the diversity of the virus is the major factor affecting the success rate of the escape of HIV from the immune response. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Effects of cell size and configuration in cellular automata based prey–predator modelling

Cellular automata (CA) have shown to be a viable approach in ecological modelling, in particular when dealing with local interactions between species and their environment. In CA modelling complex patterns emerge on a global scale through the evolution of interactions at a local level. Although the validity of a cell-based approach has successfully been demonstrated in numerous cases, very few studies have been reported that address the effects of cell size and configuration on the behaviours of CA-based models. In this paper, the performance of a cellular automaton based prey–predator model (EcoCA) developed by the author was first calibrated against the classical Lotka–Volterra (LV) model. The model was then used to investigate effects of cell size and cellular configurations (viz. the ‘computational stencil’). By setting up systematic simulation scenarios it was observed that the choice of a particular cell size has a clear effect on the resulting spatial patterns, while different cellular configurations affect both spatial patterns and system stability. On the basis of these findings, it is proposed to use the principal spatial scale of the studied ecosystem as CA model cell size and to apply the Moore type cell configuration. Methods for identifying principal spatial scales have been developed and are presented here.