Asynchronous cellular automata and pattern classification

This article designs an efficient two‐class pattern classifier utilizing asynchronous cellular automata (ACAs). The two‐state three‐neighborhood one‐dimensional ACAs that converge to fixed points from arbitrary seeds are used here for pattern classification. To design the classifier, (1) we first identify a set of ACAs that always converge to fixed points from any seeds, (2) each ACA should have at least two but not huge number of fixed point attractors, and (3) the convergence time of these ACAs are not to be exponential. To address the second issue, we propose a graph, coined as fixed point graph of an ACA that facilitates in counting the fixed points. We further perform an experimental study to estimate the convergence time of ACAs, and find there are some convergent ACAs which demand exponential convergence time. Finally, we identify there are 73 (out of 256) ACAs which can be effective candidates as pattern classifier. We use each of the candidate ACAs on some standard datasets, and observe the effectiveness of each ACAs as pattern classifier. It is observed that the proposed classifier is very competitive and performs reliably better than many standard existing classifier algorithms. © 2016 Wiley Periodicals, Inc. Complexity 21: 370–386, 2016     

Thermodynamic Automaton Simulations of Fluid Flow and Diffusion in Porous Media

A thermodynamic automaton model of fluid flow in porous media is presented. The model is a nonrelativistic version of a Lorentz invariant lattice gas model constructed by Udey et al. (1998). In the previous model it was shown that the energy momentum tensor and the relativistic Boltzman equation can be rigorously derived from the collision and propagation rules. In the present paper we demonstrate that this nonrelativistic model can be used to accurately simulate well known results involving single phase flow and diffusion in porous media. The simulation results show that (1) one-phase flow simulations in porous media are consistent with Darcy's law; (2) the apparent diffusion coefficient decreases with a decrease in permeability; (3) small scale heterogeneity does not affect diffusion significantly in the cases considered.     

Decentralized Interaction and Co-Adaptation in the Repeated Prisoner&2018;s Dilemma

A Prisoner&2018;s dilemma that is repeated indefinitely has many equilibria; the problem of selecting among these is often approached using evolutionary models. The background of this paper is a number of earlier studies in which a specific type of evolutionary model, a genetic algorithm (GA), was used to investigate which behavior survives under selective pressure. However, that normative instrument searches for equilibria that may never be attainable. Furthermore, it aims for optimization and, accordingly, says what people should do to be successful in repeated prisoner&2018;s dilemma (RPD) type situations. In the current paper, I employ simulation to find out what people would do, whether this makes them successful or not. Using a replication of Miller&2018;s (1988) GA study for comparison, a model is simulated in which the population is spatially distributed across a torus. The agents only interact with their neighbors and locally adapt their strategy to what they perceive to be successful behavior among those neighbors. Although centralized GA-evolution may lead to somewhat better performance, this goes at the cost of a large increase in required computations while a population with decentralized interactions and co-adaptation is almost as successful and, additionally, endogenously learns a more efficient scheme for adaptation. Finally, when the agents&2018; perceptive capabilities are limited even further, so that they can only perceive how their neighbors are doing against themselves, rather than against all those neighbors&2018; opponents&2014;which essentially removes reputation as a source of information&2014;cooperation breaks down.     

Computational Power of Asynchronously Tuned Automata Enhancing the Unfolded Edge of Chaos

Asynchronously tuned elementary cellular automata (AT-ECA) are described with respect to the relationship between active and passive updating, and that spells out the relationship between synchronous and asynchronous updating. Mutual tuning between synchronous and asynchronous updating can be interpreted as the model for dissipative structure, and that can reveal the critical property in the phase transition from order to chaos. Since asynchronous tuning easily makes behavior at the edge of chaos, the property of AT-ECA is called the unfolded edge of chaos. The computational power of AT-ECA is evaluated by the quantitative measure of computational universality and efficiency. It shows that the computational efficiency of AT-ECA is much higher than that of synchronous ECA and asynchronous ECA.     

高速公路封道施工路段交通流特性及通行能力分析——以杭州湾大桥内侧两条车道封道情形为例

基于交通流实测数据,针对三车道高速公路杭州湾大桥路段内侧两条车道因施工而封闭时的情形,建立元胞自动机交通流模型.根据公路养护安全作业规程和车辆行驶特点,细致划分了施工路段各区域,采用不同的换道规则来模拟不同区域中车辆的换道行为,区分车型和司机性格差异,模拟结果与实测数据吻合良好,验证了模型的有效性.通过数值模拟,研究发现封道时流量和速度随着进车率增加均有不同程度的下降,并且流量还会因施工区域长度的增加而下降.此外,通过分析封道时的道路服务水平评价指标与交通流特征值之间的关系,得出在三级服务水平以上时,道路通行能力能得到基本的保证,以此推荐了极限流量值,可为高速公路封道时的交通管理提供理论依据.     

一种基于一维元胞自动机的灾害后果推演模型

突发事件发生后,其灾害后果随时间不断演变。了解灾害后果的发展趋势,有助于快速应急响应,为此提出了一种灾害后果推演模型。首先从承灾体角度,考虑灾害后果的共性,描述了突发事件灾害后果的演化机理;然后提出基于一维元胞自动机的灾害后果推演模型,完成了对元胞、元胞空间、邻居和规则的设置。最后,通过两个算例说明了本文提出模型的有效性。该模型适用于多种突发事件,推演的结果能为应急决策提供支持。     

基于一维元胞自动机的复杂网络恶意软件传播研究

基于一维元胞自动机,研究复杂网络恶意软件传播行为.利用信息网络节点全局交互的特点,建立元胞自动机邻域和状态转换函数,提出恶意软件传播模型,研究在多种网络拓扑下恶意软件传播的概率行为.研究表明,该模型能够准确描述在最近邻耦合网络（nearest-neighbor coupled network, NC）,Erdos-Renyi（ER）随机网络,Watts-Strogatz（WS） 小世界网络和Barabasi-Albert（BA）幂率网络等拓扑下的传播动力学行为,不仅能反映恶意软件传播的平均趋势,而且可以描述病毒消亡和渗透等稀有概率事件,有效克服基于平均场方法建立的微分方程模型只能反映传播的平均趋势,只适合对传播作整体预测的局限性.同时,研究指出网络中度分布的异质化程度和网络的局域空间交互特征是影响传播及免疫行为的关键要素. 关键词： 复杂网络 恶意软件传播 元胞自动机 状态转换函数     

二维伊辛模型相变临界现象的元胞自动机模拟

用元胞自动机模型模拟二维伊辛模型的相变临界现象,得出了相变图和时空演化图;当不存在外场时,数值模拟得到的临界温度与理论值精确吻合。     

Cellular automata simulations on nanocrystallization processes: From instantaneous growth approximation to limited growth

Cellular automata simulations have been performed to simulate the crystallization process under a limited growth approximation. This approximation resembles several characteristics exhibited by nanocrystalline microstructures and nanocrystallization kinetics. Avrami exponent decreases from a value n = 4 indicating interface controlled growth and constant nucleation rate to a value n ~ 1 indicating absence of growth. A continuous change of the growth contribution to the Avrami exponent from zero to 3 is observed as the composition of the amorphous phase becomes richer in the element present in the crystalline phase.     

Synthesis of cryptographic interleaved sequences by means of linear cellular automata

This work shows that a class of pseudorandom binary sequences, the so-called interleaved sequences, can be generated by means of linear multiplicative polynomial cellular automata. In fact, these linear automata generate all the solutions of a type of linear difference equations with binary coefficients. Interleaved sequences are just particular solutions of such equations. In this way, popular nonlinear sequence generators with cryptographic application can be linearized in terms of simple cellular automata.