分布式环境中基于改进RS编码的数据存储

针对目前主流分布式处理平台数据冗余因子过大、数据可用性不高的问题,提出基于改进RS编码的二次分块数据存储方法.将分布式环境中的文件块分成多个数据子块,利用RS编码对其进行编码,并分布存储到不同机器节点中,以减少数据冗余.实验结果表明,该方法能有效降低数据冗余度,提高数据可用性,减少任务执行时间.     

基于非线性控制理论的突发需求订货策略研究

基于非线性控制理论,建立生产商、分销商和零售商在内的三阶供应链动态订货模型,使用混沌理论的李雅普诺夫指数度量突发需求扩散产生的订货量、库存量和缺货量不确定性.通过仿真软件Simulink比较了突发需求情景下,采用APIOBPCS订货策略和OUT订货策略时的系统不确定性变化.研究发现:在降低突发需求产生的库存量、订货量和...     

基于有限质点法的结构屈曲行为分析

有限质点法是一种新颖的结构分析方法,它以向量力学和数值计算为基础,将结构离散为质点群,采用牛顿第二定律描述这些质点的运动。该方法中引入移动基础架构求解结构单元内力。采用显示时间积分求解运动方程,避免了迭代求解非线性方程组。该文采用有限质点法分析结构的屈曲行为。以空间杆单元为例推导了有限质点法计算公式。利用自编程序,对空间杆系结构的屈曲行为进行了模拟。在不经过任何特殊处理的情况下,该方法不仅可以越过屈曲极值点,而且能够跟踪结构屈曲后的行为。此外,该方法无需分级加载即可模拟结构的屈曲行为,结构荷载可以在计算分析的初始步全部加在结构上,更符合实际情况。通过算例验证表明有限质点法在结构屈曲行为模拟中的适用性和真实性。     

Accurate Effective Potentials of Real Substances from Acoustic Virial Coefficients

Accurate second acoustic virial coefficients have been employed to determine parameters of an effective intermolecular potential for nine pure substances: argon, nitrogen, carbon dioxide, carbon tetrafluoride, and the first five alkanes (methane, ethane, propane, butane, and pentane). The values used for the second acoustic virial coefficients were taken from data reported in the literature. To obtain the form and parameters of the effective potential, we employed an inversion method recently introduced for volumetric second virial coefficients of nonconformal potentials. The potential parameters determined in this way are useful in predicting various gas properties. The model reproduces the experimental second virial data within their uncertainty, including volumetric second virial coefficients that were not used in the potential determination.     

Size-dependent cohesive energy of fcc nanomaterials

ABSTRACT

A theoretical model for the study of size dependent cohesive energy of face-centered cubic (fcc) nanomaterials has been developed by considering the cluster order and particle size of these materials. Using the newly developed theoretical model, cohesive energy of Au, Ag and Al nanomaterials have been calculated and compared with other theoretical models and existing experimental data. The comparison of theoretical findings of the present work indicates that present developed theoretical model is in good agreement with available experimental results which show the validity of our work.     

A New Convergence Theorem for Successive Overrelaxation Iterations

This paper contains a new convergence theorem for Gauss-Seidel (SOR) iterations for an arbitrary equation system. We use that theorem to show how to reorder equations and to extend their radius of convergence. It is not generally optimal to minimise the number or size of the above diagonal elements in a non-recursive system.     

Some Considerations on Branching Areas of Time

In this paper we show that properties of non-linear time structures have not been studied enough. Axioms forcing the existence of a branching point in a branching area of a structure are presented for various classes of structures. We show also that the classical Dedekind continuity axiom does not work well in non-linear structures and we suggest stronger versions. Finally, some interdependencies between the axioms presented are proved.     

Accounting for Lag Order Uncertainty in Autoregressions: the Endogenous Lag Order Bootstrap Algorithm

Conventional asymptotic and bootstrap methods for finite-order autoregressive models condition on the estimated lag order of the model as though it were known to be the true lag order. Even if the order is estimated correctly, this procedure ignores the sampling uncertainty about the lag order estimate and may result in spurious inferences. In this paper an appropriately modified bootstrap algorithm is introduced that reflects the true extent of sampling uncertainty in the regression estimates. This endogenous lag order bootstrap algorithm recognizes that lag order selection is an integral part of the sampling procedure by re-estimating the lag order in each bootstrap iteration. It is suggested that the endogenous lag order bootstrap algorithm should routinely replace the standard bootstrap algorithm in applications. Monte Carlo simulations show that ignoring lag order uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for vector autoregression impulse response estimates. Endogenizing the lag order choice is shown to improve coverage accuracy in small samples at negligible additional computational cost. As the lag order uncertainty declines in large samples, the performance of the endogenous lag order interval converges to that of the standard interval.     

Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching

One of the obstacles to widely using first-order logic languages is the fact that relational inference is intractable in the worst case. This paper presents an any-time relational inference algorithm: it proceeds by stochastically sampling the inference search space, after this space has been judiciously restricted using strongly-typed logic-like declarations.We present a relational learner producing programs geared to stochastic inference, named STILL, to enforce the potentialities of this framework. STILL handles examples described as definite or constrained clauses, and uses sampling-based heuristics again to achieve any-time learning.Controlling both the construction and the exploitation of logic programs yields robust relational reasoning, where deductive biases are compensated for by inductive biases, and vice versa.