Synchrony in a self-repair network with a simple lattice

We investigate a synchrony in a self-repairing network of autonomous agents capable of repairing mutually. In this paper, we define two models: a synchronous model and asynchronous one. They differ in the timing when the agents change their state. Computer simulations revealed that the synchronous model has a critical point, while the asynchronous one does not. We also studied a repair scheme in asynchronous model where the repaired agents in turn repair neighbor agents successively in a chain-reactive fashion. Performance of the scheme has been examined by computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Stability of an equilibrium state in a multiinfectious-type SIS model on a truncated network

In a homogeneous constant population, the basic SIS model potentially has an epidemic equilibrium state with global asymptotic stability since it can be reduced to the logistic equation. On the basic SIS model with a nonhomogeneous constant population, viewed as a multitype SIS model, the global or local asymptotic stability of an epidemic equilibrium state has also been studied.^1–4 However, this kind of analysis in other models with nonhomogeneous populations has rarely been developed, even though the corresponding models with homogeneous populations are well known. In addition, recent studies of complex networks have revealed that heterogeneity of the link number of vertices drastically changes the epidemic thresholds.^5–9 For these reasons, figuring out the roles of heterogeneity is a major topic in epidemic modeling. Here, we consider a multiinfectious-type SIS model on a network, and show the (local or global) asymptotic stability of an epidemic equilibrium state whenever it exists. This work was presented in part at the 11th International Symposium of Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

一种具有随机邻居元胞自动机的混沌特性

研究了一种具有随机邻居的2值元胞自动机模型的动力学性质,给出了其理想状态的动力学模型,分析了该模型的混沌特性,并通过分叉图、Lyapunov指数和Sehwarzian导数解释了模型由倍分叉通向混沌的过程。最后,通过计算对比,分析了非理想状态与理想状态下模型动力学性质的差异。     

Simulating thermohydrodynamics with lattice BGK models

In this paper, we propose new lattice BGK models for one-, two- and three-dimensional thermohydrodynamics. A proper internal energy is introduced and the energy equation is obtained. The derivation of the thermohydrodynamic equations is systematic and numerical simulations are carried out to verify the theoretical values of the sound speed, the shear viscosity and the conductivity. One-dimensional shock-tube problem and two-dimensional Rayleigh-Bénard convection are simulated. Good performance and satisfactory results are obtained. These models can be applied to many interesting cases, in particular, the transonic regimes where the compressibility can not be neglected.     

融入迁移限制与双层集合种群网络的传染病模型

本文在斑块环境下基于易感–感染–易感模型(SIS模型)研究了感染者迁移限制对传染病传播的影响,其中迁移限制用双层网络进行表示,并提出了双层集合种群动态网络.子种群(即斑块)用双层网络上的节点表示,双层网络上的链接分别代表易感节点斑块和感染节点斑块间的迁移路径,易感染和感染节点分别通过双层网络上的链接随机游走.并提出了两种反应扩散方程分别作为易感染与感染节点的微分方程,分别计算其数值解,以评估每个斑块(节点)的感染风险.研究表明:在双层网络中,迁移限制会降低感染节点密度,将感染节点限制在中心节点(度值最高的子种群)中.感染节点密度高度依赖于双层网络结构.     

通用有效的动态系统网络病毒传播模型方法研究*

研究了依存于网络拓扑的病毒传播特征,并提出了一个通用的动态系统网络模型。该模型对爆发一种病毒后,其是很快灭亡还是长期生存,给出了一个简单易求的传播临界值来进行判断。最后通过对多个网络实际数据集进行计算机仿真,证明了本模型及传播临界值的有效性和精确性。     

Satisfying demands in a multicellular network: A universal power allocation algorithm

Power allocation to satisfy user demands, in the presence of large number of interferers (in a multicellular network), is a challenging task. Further, the power to be allocated depends upon the system architecture, for example upon components like coding, modulation, transmit precoder, rate allocation algorithms, available knowledge of the interfering channels, etc. This calls for an algorithm via which each base station in the network can simultaneously allocate power to their respective users so as to meet their demands (whenever they are within the achievable limits), using whatever information is available of the other users. The goal of our research is to propose one such algorithm which in fact is universal: the proposed algorithm works from a fully co-operative setting to almost no co-operation and or for any configuration of modulation, rate allocation, etc. schemes. The algorithm asymptotically satisfies the user demands, running simultaneously and independently within a given total power budget at each base station. Further, it requires minimal information to achieve this: every base station needs to know its own users demands, its total power constraint and the transmission rates allocated to its users in every time slot. We formulate the power allocation problem in a system specific game theoretic setting, define system specific capacity region and analyze the proposed algorithm using ordinary differential equation (ODE) framework. Simulations further confirm the effectiveness of the proposed algorithm. We also demonstrate the tracking abilities of the algorithm.     

Hidden Markov random field models for TCA image analysis

Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of mammalian teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines which are called tooth rings and appear in the cementum band. The number of rings is computed from an intensity (gray scale) image of the cementum band, by estimating the average ring width and then dividing the area of the cementum band by this estimate. The ring width is estimated by modelling the image by a hidden Markov random field, where intensities are assumed to be pixelwise conditionally independent and normally distributed, given a Markov random field of hidden binary labels, representing the“true scene”. To incorporate image macro-features (the long-range dependence among intensities and the quasi-periodicity in the placement of tooth rings), the label random field is defined by an energy function that depends on a parametric Gabor filter, convolved with the true scene. The filter parameter represents the unknown of main interest, i.e. the average width of the rings. The model is estimated through an EM algorithm, relying on the mean field approximation of the hidden label distribution and allows to predict the locations of the rings in the image.     

一种新的一维无格子超车跟车仿真模型

近年来,人们对超车和跟车进行了广泛的研究。但以往方法尚有其不足之处。主要体现在,不能统一处理超车和跟车模型以及不能较好地体现司机的行为特性,另外不能通过简洁的计算同时给出任意时刻车辆在道路的纵向和横向上的位置。为此,作者在其首创的固支梁挠度曲线超车跟车模型基础上,提出了一种新的一维无格子超车跟车模型。它根据前方车辆的情况及道路宽度的变化情况直接确定当前车辆在道路宽度方向上的位置。新模型能同时适用于道路宽度变化和前方车辆速度变化的情形。     

考虑动态速度上限的车辆行驶虚坡模型

研究交通管理问题,针对元胞自动机模型被广泛用于交通流的仿真,但它具有其自身固有的离散性。对仿真中车辆以跬跳的方式前进,使得难以用实验手段检验其计算结果。另外,元胞自动机难以处理道路宽度连续变化的情形。而以固支梁挠度曲线为基础的虚坡模型能有效地体现车辆行驶的连续性并能自然推广于道路宽度变化的情形。但以往虚坡模型中采用的车辆速度上限保持不变,驾驶缺乏灵活性。为解决上述问题,提出通过用固支梁挠度曲线表示不同阶段内的动态速度上限,以表示车辆的源于动力的加减速行为。对一个大规模问题进行了计算,结果表明模型能给出连续的车辆速度,并且能适用于道路宽度变化时的情形,能反映车辆行驶随意性,经仿真结果表明改进模型的优越性。     

一种自组织的二维元胞自动机网络模型及分析

提出了一种自组织的二维元胞自动机网络模型,并研究了网络中的临界特征和长相关特性。模型中,每个元胞包含一个路由器和随机数目的终端,并且能独立地根据探测到的网络拥塞状况调节其终端的数据包发送速率。仿真结果表明,在拥塞控制机制的作用下,网络能自组织地工作在临界状态,虽然节点间呈现出明显的异构特征,但节点数据包排队长度在时间和空间上仍都表现出较强的相关性。     

Critical phenomena in a collective computation algorithm for combinatorial optimization problems

This paper discusses the critical temperature (control parameter) of an annealed neural network, a typical collective computation for combinatorial optimization problems.It is shown that the theoretical critical temperatures determined by our estimation agree with those derived by computational experiments in graph partitioning problems and in the travelling salesman problem.     

基于消息传递的机载雷达组网航迹融合

机载雷达组网航迹融合需要解决目标跟踪、数据关联与航迹管理3个子问题, 然而这3个子问题相互耦合,采用开环序贯估计算法会导致性能下降. 本文提出了一种基于消息传递的机载雷达组网航迹融合方法, 该方法在联合优化框架下解决目标跟踪、数据关联与航迹管理3个子问题. 首先, 建立机载雷达组网航迹融合的联合概率密度函数, 并将其转换为因子图. 其次, 将因子图分解为置信传播区域与平均场近似区域. 目标运动状态的统计模型服从共轭指数模型, 因此采用平均场近似以获得简单的消息传递更新公式. 数据关联包含一对一约束, 因此采用置信传播. 目标存在状态同样采用置信传播, 以获得更好的近似结果. 最后, 可以通过闭环迭代框架近似估计后验分布, 从而有效处理目标跟踪、数据关联与航迹管理之间的耦合问题. 仿真结果表明, 所提算法的性能优于多假设跟踪算法和联合概率密度关联算法.     

Performance of garbage collection algorithms for flash-based solid state drives with hot/cold data

To avoid a poor random write performance, flash-based solid state drives typically rely on an internal log-structure. This log-structure reduces the write amplification and thereby improves the write throughput and extends the drive’s lifespan. In this paper, we analyze the performance of the log-structure combined with the d$d$-choices garbage collection algorithm, which repeatedly selects the block with the fewest number of valid pages out of a set of d$d$ randomly chosen blocks, and consider non-uniform random write workloads. Using a mean field model, we show that the write amplification worsens as the hot data gets hotter.     

自组织多模型逼近性能分析与改进

针对基于自组织映射神经网络的非线性函数逼近,研究其方法和原理,指出它与一般前向神经网络在逼近原理上的不同．在此基础上,进一步研究该方法的逼近性能,分析其两个不足之处,进而提出一种提高逼近性能的改进神经网络训练策略．最后通过仿真实例验证了所得结论,表明了改进方法的有效性．     

Modeling birdsong learning with a chaotic Elman network

Among passerines, Bengali finches are known to sing extremely complex courtship songs with three hierarchical structures: namely, the element, the chunk, and the syntax. In this work, we theoretically studied the mechanism of the song of Bengali finches in aides to provide a dynamic view of the development of birdsong learning. We first constructed a model of the Elman network with chaotic neurons that successfully learned the supervisor signal defined by a simple finite-state syntax. Second, we focused on the process of individual-specific increases in the complexity of song syntax. We propose a new learning algorithm to produce the intrinsic diversification of song syntax without a supervisor on the basis of the itinerant dynamics of chaotic neural networks and the Hebbian learning rule. The emergence of novel syntax modifying the acquired syntax is demonstrated. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Learning the insertion operation of a flexible beam into a hole with a manipulator

Flexible objects are widely used in the aerospace, automobile, electronics, and medical industries, but automated assembly of flexible objects is difficult to realize. In most cases, flexible objects are still handled and assembled by people. This article researches a typical flexible objects assembly operation, i.e., to insert a flexible beam into a hole. A learning method is proposed to learn the mapping from the sensed force to the end-effector's motion, by which the insertion operation can be achieved efficiently. The mapping is decomposed in Cartesian space. Agents based on a learning automaton are defined between the input space, formed by the sensed force, and the output space, formed by the end-effector's motion, to implement the functions of the decomposed mappings. The input space is partitioned into different contexts. Through learning, agents can learn optimal actions according to different contexts, and then fulfill the insertion task cooperatively and efficiently. Simulation results of a 2D insertion operation prove the feasibility of the proposed method.     

Epidemic spreading model of complex dynamical network with the heterogeneity of nodes

In this paper, we model epidemic spreading by considering the mobility of nodes in complex dynamical network based on mean field theory using differential equations. Moreover, a resistance factor which can characterise the impact of individual's difference on the propagation dynamics in complex dynamical network is proposed by considering the influence of total number of connections and the continuous time to remain in contact. The effect of heterogeneity on the evolution process of propagation dynamics is explored by simulation. Extensive simulations are conducted to study the key influence parameters and the influence of them on the spreading dynamics, which are helpful to the understanding of epidemic spreading mechanism and the designing of effective control strategies.