Cellular automata modeling of static recrystallization based on the curvature driven subgrain growth mechanism

A two-dimensional cellular automata model was developed to describe the static recrystallization (SRX) arising from the subgrain growth, the driving force of which is dependent on boundary energy and local curvature. At the same time, the subgrain boundary energy and mobility rely on the boundary misorientation angle. On the basis, a deterministic switch rule was adopted to simulate the subgrain growth and kinetics of recrystallization quantitatively to provide an insight into the grain boundary bulging nucleation mechanism. Microstructure evolutions during SRX in different cases were simulated by the developed model. At the beginning of the simulation, the initial polycrystalline microstructure which contains large number of uniformly distributed subgrains in every pre-existing grain was prepared using simple assumption based on experimental observations. Then, both homogeneous and inhomogeneous subgrain growth phenomena were captured by the simulation with different inter-subgrain misorientation, which showed continuous and discontinuous recrystallization, respectively. The effects of initial mean subgrain radius, distribution of initial subgrains, distribution of inter-subgrain misorientations, and annealing temperature on the recrystallization kinetics were also investigated.     

The role of network theory and object-oriented modeling within a framework for the vulnerability analysis of critical infrastructures

A framework for the analysis of the vulnerability of critical infrastructures has been proposed by some of the authors. The framework basically consists of two successive stages: (i) a screening analysis for identifying the parts of the critical infrastructure most relevant with respect to its vulnerability and (ii) a detailed modeling of the operational dynamics of the identified parts for gaining insights on the causes and mechanisms responsible for the vulnerability. In this paper, a critical presentation is offered of the results of a set of investigations aimed at evaluating the potentials of (i) using network analysis based on measures of topological interconnection and reliability efficiency, for the screening task; (ii) using object-oriented modeling as the simulation framework to capture the detailed dynamics of the operational scenarios involving the most vulnerable parts of the critical infrastructure as identified by the preceding network analysis. A case study based on the Swiss high-voltage transmission system is considered. The results are cross-compared and evaluated; the needs of further research are defined.     

An object-oriented modeling framework for representing uncertainty in early variant design

This article presents a framework for the representation of uncertainty in the early design of complex adaptive products such as automobiles. The core of the framework is an object-oriented approach in which design objects and their inter-relationships may be modeled, and in which both the design attributes and the product structure may be uncertain. Relationship objects allow product variants and design alternatives to be represented. In addition to the design model, derivation methods for design attributes may be modeled, and methods may be incorporated to allow the deterministic or probabilistic computation of attributes. The modeling framework is the basis of a risk modeling tool, RiTo, in which Monte Carlo simulation is used to compute estimates for costs and other design attributes together with their probability of achievement in the final design. Uncertainties may be aggregated and levels of uncertainty in different parts of the model may be continually analysed and assessed. The framework also provides a mechanism for accumulating product knowledge, in particular knowledge concerning relationships between elements of part and assembly models, product volumes and manufacturing considerations.

Cellular automata and integrodifferential equation models for cell renewal in mosaic tissues

Mosaic tissues are composed of two or more genetically distinct cell types. They occur naturally, and are also a useful experimental method for exploring tissue growth and maintenance. By marking the different cell types, one can study the patterns formed by proliferation, renewal and migration. Here, we present mathematical modelling suggesting that small changes in the type of interaction that cells have with their local cellular environment can lead to very different outcomes for the composition of mosaics. In cell renewal, proliferation of each cell type may depend linearly or nonlinearly on the local proportion of cells of that type, and these two possibilities produce very different patterns. We study two variations of a cellular automaton model based on simple rules for renewal. We then propose an integrodifferential equation model, and again consider two different forms of cellular interaction. The results of the continuous and cellular automata models are qualitatively the same, and we observe that changes in local environment interaction affect the dynamics for both. Furthermore, we demonstrate that the models reproduce some of the patterns seen in actual mosaic tissues. In particular, our results suggest that the differing patterns seen in organ parenchymas may be driven purely by the process of cell replacement under different interaction scenarios.     

Rule-based modelling and simulation of biochemical systems with molecular finite automata

The authors propose a theoretical formalism, molecular finite automata (MFA), to describe individual proteins as rule-based computing machines. The MFA formalism provides a framework for modelling individual protein behaviours and systems-level dynamics via construction of programmable and executable machines. Models specified within this formalism explicitly represent the context-sensitive dynamics of individual proteins driven by external inputs and represent protein-protein interactions as synchronised machine reconfigurations. Both deterministic and stochastic simulations can be applied to quantitatively compute the dynamics of MFA models. They apply the MFA formalism to model and simulate a simple example of a signal-transduction system that involves an MAP kinase cascade and a scaffold protein.     

Experimental free-space optical network for massively parallel computers

A free-space optical interconnection scheme is described for massively parallel processors based on the interconnection-cached network architecture. The optical network operates in a circuit-switching mode. Combined with a packet-switching operation among the circuit-switched optical channels, a high-bandwidth, low-latency network for massively parallel processing results. The design and assembly of a 64-channel experimental prototype is discussed, and operational results are presented.     

A metric for characterizing the bistability of molecular quantum-dot cellular automata

Much of molecular electronics involves trying to use molecules as (a)?wires, (b)?diodes or (c)?field-effect transistors. In each case the criterion for determining good performance is well known: for wires it is conductance, for diodes it is conductance asymmetry, while for transistors it is high transconductance. Candidate molecules can be screened in terms of these criteria by calculating molecular conductivity in forward and reverse directions, and in the presence of a gating field. Hence so much theoretical work has focused on understanding molecular conductance. In contrast a molecule used as a quantum-dot cellular automata (QCA) cell conducts no current at all. The keys to QCA functionality are (a)?charge localization, (b)?bistable charge switching within the cell and (c)?electric field coupling between one molecular cell and its neighbor. The combination of these effects can be examined using the cell-cell response function which relates the polarization of one cell to the induced polarization of a neighboring cell. The response function can be obtained by calculating the molecular electronic structure with ab initio quantum chemistry techniques. We present an analysis of molecular QCA performance that can be applied to any candidate molecule. From the full quantum chemistry, all-electron ab initio calculations we extract parameters for a reduced-state model which reproduces the cell-cell response function very well. Techniques from electron transfer theory are used to derive analytical models of the response function and can be employed on molecules too large for full ab initio treatment. A metric is derived which characterizes molecular QCA performance the way transconductance characterizes transistor performance. This metric can be assessed from absorption measurements of the electron transfer band or quantum chemistry calculations of appropriate sophistication.     

Scalable optical hypercube-based interconnection network for massively parallel computing

Two important parameters of a network for massively parallel computers are scalability and modularity. Scalability has two aspects: size and time (or generation). Size scalability refers to the property that the size of the network can be increased with nominal effect on the existing configuration. Also, the increase in size is expected to result in a linear increase in performance. Time scalability implies that the communication capabilities of a network should be large enough to support the evolution of processing elements through generations. A modular network enables the construction of a large network out of many smaller ones. The lack of these two important parameters has limited the use of certain types of interconnection networks in the area of massively parallel computers. We present a new modular optical interconnection network, called an optical multimesh hypercube (OMMH), which is both size and time scalable. The OMMH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the torus (constant node degree and size scalability) networks. Also presented is a three-dimensional optical implementation of the OMMH network. A basic building block of the OMMH network is a hypercube module that is constructed with free-space optics to provide compact and high-density localized hypercube connections. The OMMH network is then constructed by the connection of such basic building blocks with multiwavelength optical fibers to realize torus connections. The proposed implementation methodology is intended to exploit the advantages of both space-invariant free-space and multiwavelength fiber-based optical interconnect technologies. The analysis of the proposed implementation shows that such a network is optically feasible in terms of the physical size and the optical power budget.     

Adaptive smoothed molecular dynamics for multiscale modeling

In the smoothed molecular dynamics (SMD), the high frequency modes are eliminated from the motion of atoms to enlarge the time step significantly. In some situations, however, rearrangements or atoms disorder may occur. Hence, it is desirable to use MD in localized regions to capture the interesting high frequency motion, while use SMD elsewhere to save the computational cost. In this paper, an adaptive smoothed molecular dynamics (ASMD) is developed. During the simulation process, if the high frequency motions of atoms are dominant in a region, the background grid in the region is refined hierarchically until it is able to capture the high frequency motion of the atoms.     

Fast parallel molecular algorithms for DNA-based computation: factoring integers

The RSA public-key cryptosystem is an algorithm that converts input data to an unrecognizable encryption and converts the unrecognizable data back into its original decryption form. The security of the RSA public-key cryptosystem is based on the difficulty of factoring the product of two large prime numbers. This paper demonstrates to factor the product of two large prime numbers, and is a breakthrough in basic biological operations using a molecular computer. In order to achieve this, we propose three DNA-based algorithms for parallel subtractor, parallel comparator, and parallel modular arithmetic that formally verify our designed molecular solutions for factoring the product of two large prime numbers. Furthermore, this work indicates that the cryptosystems using public-key are perhaps insecure and also presents clear evidence of the ability of molecular computing to perform complicated mathematical operations.     

Incrementally scalable optical interconnection network with a constant degree and constant diameter for parallel computing

A new scalable interconnection topology called the spanning-bus connected hypercube (SBCH) that is suitable for massively parallel systems is proposed. The SBCH uses the hypercube topology as a basic building block and connects such building blocks by use of multidimensional spanning buses. In doing so, the SBCH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the spanning-bus hypercube (SBH) (constant node degree, scalability, and ease of physical implementation), while at the same time circumventing their disadvantages. The SBCH topology permits the efficient support of many communication patterns found in different classes of computation, such as bus-based, mesh-based, and tree-based problems, as well as hypercube-based problems. A very attractive feature of the SBCH network is its ability to support a large number of processors while maintaining a constant degree and a constant diameter. Other positive features include symmetry, incremental scalability, and fault tolerance. An optical implementation methodology is proposed for the SBCH. The implementation methodology combines the advantages of free-space optics with those of wavelength-division multiplexing techniques. An analysis of the feasibility of the proposed network is also presented.     

新型自适应RBF神经网络应用于微带天线建模

矩形微带天线设计中,为解决谐振频率与尺寸结构之间存在多参数、强非线性关系而建立其精确模型的困难,提出一种动态自适应聚类与伪逆求权值相结合的新型RB F神经网络算法,其优点为：无需事先固定隐层神经元个数,以自适应调整的方式,获得较好的逼近效果,且计算量小、学习速度快。同时,结合RB F神经网络对于多变量、非线性函数有较好的逼近特性,将其用于矩形微带天线的谐振频率建模。实验表明,建立的神经网络模型无论是在精度还是速度上都明显优于已有文献的结果,从而为天线设计开发者提供了一种新的有效方法,提高设计效率。     

Fast parallel DNA-based algorithms for molecular computation: the set-partition problem

This paper demonstrates that basic biological operations can be used to solve the set-partition problem. In order to achieve this, we propose three DNA-based algorithms, a signed parallel adder, a signed parallel subtractor and a signed parallel comparator, that formally verify our designed molecular solutions for solving the set-partition problem.     

基于BP神经网络的光纤陀螺仪温度建模研究

目前光纤陀螺应用广泛，但是其性能容易受到环境温度影响，从而影响到惯性导航系统的性能．光纤陀螺的温度特性具有非常复杂的非线性特点，而BP神经网络具有良好的逼近复杂非线性函数能力。使用BP神经网络建立光纤陀螺温度特性的黑箱模型，不对零漂和标度因子进行补偿，而直接对陀螺输出进行校正．经实际数据检验，该建模补偿方法比未经补偿和经过传统工程补偿方法的精度提高了两个数量级．与传统的线性模型相比较，本文基于BP神经网络建立的光纤陀螺温度模型具有补偿方法简单，精度高，通用性好等优点．     

Evolutionary neural network modeling for software cumulative failure time prediction

An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg–Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.     

A topology partition algorithm for parallel network simulation based on random scanning

为了提高并行网络模拟的性能,研究了实现有效的拓扑划分的策略,提出并实现了基于随机扫描的并行网络模拟拓扑划分(TPBRS)算法.基于启明星辰探测获得的实际拓扑进行的蠕虫模拟表明,该划分方法可适用于实际拓扑,并可进行大规模网络安全事件的模拟.实验结果表明,相对于传统划分算法,该拓扑划分方法减少模拟时间约19％,各个模拟节点模拟时间差值平均减少约21.78％,内存差值平均减少约4.6％,并且模拟时间和内存的增长更具有规律性,即负载均衡度更好,划分更加合理,提高了网络模拟的性能.     

Use of massively parallel molecular dynamics simulations for radiation damage in pyrochlores

DL_POLY_3 is a general purpose molecular dynamics (MD) simulation package designed to simulate systems of the order of tens of millions of particles and beyond by efficiently harnessing the power of modern computer clusters. Here we discuss the package design, functionality and report on performance and capability limits. We then report the application of DL_POLY_3 to study radiation cascades in Gd₂Ti₂O₇ and Gd₂Zr₂O₇, potential materials for high-level radioactive waste storage and discuss problems associated with the analysis of the cascades. We see little direct amorphisation but rather the start of a transition to the fluorite structure which is more pronounced for the Zr than the Ti compound.

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RFID读写器防冲突问题的混沌神经网络建模与求解

针对密集读写器环境下的RFID读写器冲突情况,提出了一种通过中央计算机集中控制读写器分时操作来避免读写器冲突的方法,并设计了基于退火策略的混沌神经网络算法进行读写器时隙分配的求解.首先,根据平面图着色问题与读写器防冲突问题的相似性,确定四时隙分时操作的防冲突原理;然后采用二维Hopfield神经网络建立四时隙分配问题模型,并构造了满足冲突约束条件的神经网络能量函数;最后,通过引入混沌机制和模拟退火策略进行问题求解以使得算法具有较好的搜索能力和收敛速度.与现有的分布式防冲突算法相比,该方法能够保证读写器具有更多的扫描标签时间和更高的标签扫描频度.仿真实验结果表明用该算法求解读写器防冲突问题是可靠的、高效的.     

Neural network modeling and optimization of scheduling backwash for membrane bioreactor

Backwash scheduling for a membrane bioreactor was experimentally examined and theoretically modeled via neural networks. Flux was determined for different backwash and service time runs. Vacuum and backwashing streams pressure for different timing regimes were used to observe and monitor the fouling and a cake layer accumulated on the membrane surface causing a decline in the flux for the submerged membrane bioreactor. Experimental results were employed to develop an artificial neural network model (ANN) to predict the membrane flux as a function of the backwash and service times. Such modeling entails using a fairly large number of experimental data to reconcile model predictions with actual flux measurements in order to validate the ANN model. The ANN model was shown to be accurate in predicting the flux of the membrane and can be utilized to find optimum backwash scheduling strategy for such reactors.     

Feasibility study of a scalable optical interconnection network for massively parallel processing systems

The theoretical modeling of a novel topology for scalable optical interconnection networks, called optical multimesh hypercube (OMMH), is developed to predict size, bit rate, bit-error rate, power budget, noise, efficiency, interconnect distance, pixel density, and misalignment sensitivity. The numerical predictions are validated with experimental data from commercially available products to assess the effects of various thermal, system, and geometric parameters on the behavior of the sample model. OMMH is a scalable network architecture that combines positive features of the hypercube (small diameter, regular, symmetric, and fault tolerant) and the mesh (constant node degree and size scalability). The OMMH is implemented by a free-space imaging system incorporated with a space-invariant hologram for the hypercube links and fiber optics to provide the mesh connectivity. The results of this work show that the free-space links can operate at 368 Mbits/s and the fiber-based links at 228 Mbits/s for a bit-error rate of 10(-17) per channel. The predicted system size for 32 nodes in the OMMH is 4.16 mm × 4.16 mm × 3.38 cm. Using 16-bit, bit-parallel transmission per node, the system can operate at a bit rate of up to 5.88 Gbits/s for a size of 1.04 cm × 1.04 cm × 3.38 cm.