元胞自动机仿真技术

元胞自动机仿真技术（CAST）开创了探索复杂性的新途径,在材料、生物、物理等领域有广泛的应用前景,是发达国家激烈竞争的重大前沿学科领域．本文综述了CAST的产生与发展过程,概括了仿真模型的构成要素,评述了CAST的典型应用实例,着重指出了CAST是对材料进行原子级微观辅助设计的强有力工具．最后,探讨了CAST的特点：它抓住了简单性与复杂性这一对主要矛盾,从而触及并体现了其它有关矛盾,诸如局部与整体,宏观与微观,线性与非线性,决定性与随机性,数学模型与物理实质．     

Spectral element spatial discretization error in solving highly anisotropic heat conduction equation

This paper describes a study of the effects of the overall spatial resolution, polynomial degree and computational grid directionality on the accuracy of numerical solutions of a highly anisotropic thermal diffusion equation using the spectral element spatial discretization method. The high-order spectral element macroscopic modeling code SEL/HiFi has been used to explore the parameter space. It is shown that for a given number of spatial degrees of freedom, increasing polynomial degree while reducing the number of elements results in exponential reduction of the numerical error. The alignment of the grid with the direction of anisotropy is shown to further improve the accuracy of the solution. These effects are qualitatively explained and numerically quantified in 2- and 3-dimensional calculations with straight and curved anisotropy.     

A verifiable multi-secret sharing scheme based on cellular automata

In this paper, we propose a verifiable (t, n)-threshold multi-secret sharing scheme, based on one-dimensional cellular automata where the number of secrets is not restricted by n or t. We show that our scheme can be used to solve an open problem proposed recently in Alvarez et al. (2008) [G. Alvarez, L. Hernández Encinas, A. Martín del Rey, A multisecret sharing scheme for color images based on cellular automata, Information Sciences 178 (2008) 4382-4395].     

Remote sensing of structural complexity indices for habitat and species distribution modeling

Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI₈). CSI and AI₈ showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI₈)), and predicted scores 60-110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R² = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI₈ model R² = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI₈ most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.     

Numerical simulation of steady planar die swell for a Newtonian fluid using the spectral element method

This paper is concerned with two dimensional numerical simulations of plane extrusion of a Newtonian fluid. The problem is discretized using the spectral element method and the free surface is evolved according to an ALE treatment. Numerical simulations are performed over a wide range of Reynolds and Weber numbers to highlight the effects of inertia and surface tension, respectively. Convergence of the numerical approximations with respect to polynomial order is demonstrated for the sensitive measures of free surface location and downstream relaxation distance. The higher the inertia the further downstream the relaxation occurs. Numerical results show good qualitative and quantitative agreement with predictions of other numerical schemes and experiments.     

一种自反馈细胞神经网络稳定性分析及在函数优化中的应用

提出一种具有暂态混沌的细胞神经网络,该网络是利用欧拉算法将模型的状态方程转化为离散形式并引入一项负的自反馈而形成的.由对单个神经元的仿真发现,该模型具有分叉和混沌的特性.在函数优化中,该网络首先经过一个倍周期倒分叉过程进行混沌搜索;然后进行类似Hopfield网络的梯度搜索.由于该网络利用了混沌搜索固有的随机性和轨道遍历性,因而具有较强的全局寻优的能力.最后通过2个函数优化的例子验证了该网络的有效性.     

基于细胞自动机的移动机器人路径规划算法

针对移动机器人全局最优路径规划问题,提出一种基于细胞自动机的路径规划算法.该算法首先将移动机器人的起点、目标点和空间障碍物定义为一组离散的细胞状态,建立环境的细胞自动机模型;然后由机器人移动的曼哈顿距离设计演化规则;最后根据演化后的细胞状态搜索最优路径.对简单和复杂环境下的机器人路径规划问题进行了仿真实验,实验结果验证了该算法的有效性.     

H∞ Controller design for spectral MIMO models by convex optimization

A new method for robust fixed-order H_∞ controller design by convex optimization for multivariable systems is investigated. Linear Time-Invariant Multi-Input Multi-Output (LTI-MIMO) systems represented by a set of complex values in the frequency domain are considered. It is shown that the Generalized Nyquist Stability criterion can be approximated by a set of convex constraints with respect to the parameters of a multivariable linearly parameterized controller in the Nyquist diagram. The diagonal elements of the controller are tuned to satisfy the desired performances, while simultaneously, the off-diagonal elements are designed to decouple the system. Multimodel uncertainty can be directly considered in the proposed approach by increasing the number of constraints. The simulation examples illustrate the effectiveness of the proposed approach.     

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

A two-dimensional (2-D) cellular automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. In this paper, the study is focused on the optimal parameterisation of the model introducing the use of genetic algorithm (GA). The optimisation of the CA model parameterisation, by applying a standard GA, extends its ability to study various hypotheses concerning the seismicity of the region under consideration. The GA evolves an initially random population of candidate solutions of model parameters, such that in time appropriate solutions to emerge. The quality criterion is realised by taking into account the extent that the simulation results match the Gutenberg-Richter (GR) law derived from recorded data of the area under test. The simulation results presented here regard regions of Greece with different seismic and geophysical characteristics. The results found are in good quantitative and qualitative agreement with the GR scaling relations.