一种带有链约束的连续型批处理机调度问题

针对链式约束下工件释放时间和工期同序的情况,证明了即使所有工件都是单位加工时间时,极小化最大拖期问题也是强NP-难的.对于工件的零时刻都到达且同一链中工件工期相同的特殊情况,给出了多项式时间的最优算法.     

加密技术浅析

数据加密技术是实现信息安全的关键技术之一,其重要性已经引起各国政府、企业的广泛关注,本文系统的介绍了数据加密技术的原理、数据加密技术算法及加密的方式等方面知识,并对加密技术发展应用做了展望描述。对于有效的构筑全面的安全体系,深入了解数据加密技术,提高数据安全具有一定的指导意义。     

基于单调链的简单多边形距离算法

简单多边形的距离问题是计算机图形学中的一个研究难点,为了能快速地获得距离信息,提出一种基于单调链的简单多边形距离算法.算法先对多边形边界进行关于坐标轴的单调链分割,然后根据可见性原则确定候选链对,再结合层次树理论和分支限界策略计算链对距离以求解多边形的最近距离.试验结果表明,该算法性能优于其他同类算法.     

高效柔性制造技术的新进展

以数控技术为中心的高效柔性制造技术是当前加工技术发展的主流，具有3F、3I和3S的特征。文章分析了高效化和柔性化的主要发展目标，其主要研究方向为发展适于全面高速化、过程链集约化和大批量订制化的制造装备及其系统，使之能在多品种变批量的市场需求条件下实现响应敏捷化、运行智能化和效益最优化。提出了发展快速重组制造系统及可重构制造设备作为统一高效和柔性两者要求的一个合理解决方案，并进一步探讨了可用于网络化环境的制造单元的结构体系。     

基于Linux的Internet防火墙的架构

该文阐述了NAT的工作原理，并结合实例讲述了其在Linux操作系统下的具体实现方法。     

邻接矩阵在平面机构分析中的应用

研究了邻接矩阵在平面机构自由度计算、同构识别和速度瞬心求解中的应用。揭示了邻接矩阵与机构自由度的关系。给出了同构运动链邻接矩阵的特征向量及特征值关系的充要条件 ,据此提出了识别机构运动链同构的方法。最后 ,提出了应用邻接矩阵自乘求解机构全部瞬心及求解给定瞬心最短路经的方法     

The tree MVA algorithm

A new algorithm to solve product form queueing networks, especially those with large numbers of centers and chains, is presented. This algorithm is a Tree version of Mean Value Analysis (MVA). Tree MVA is analogous to the Tree version of Convolution developed by Lam and Lien. Like Tree Convolution, Tree MVA allows exact solution of large networks which are intractable with previous sequential algorithms. As with the sequential versions of Convolution and MVA, Tree MVA has better numerical properties than Tree Convolution. Further, Tree MVA avoids the computational complexity of sequential MVA in networks with several queue dependent centers. Thus, we consider Tree MVA to be the best algorithm for general product form networks.     

Application of spherical subset simulation method and auxiliary domain method on a benchmark reliability study

This paper addresses a benchmark study designed to evaluate the performance of various methods in calculating the reliability of large systems. In particular, this paper focuses on evaluating two reliability methods recently proposed by the authors, referred to as spherical subset simulation (S³) and auxiliary domain method (ADM). S³ is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities associated with each of these subregions. The probability of each subregion is calculated as a product of factors. These factors can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. The generation of such samples is achieved through Markov Chain Monte Carlo (MCMC) simulations using a MCMC algorithm proposed by the authors. The proposed method is very robust and is suitable for treating general high-dimensional problems such as the given benchmark problems. ADM is applicable to reliability problems involving deterministic dynamic systems subjected to stochastic excitation. The first step in ADM involves the determination of an auxiliary failure domain (AFD). The choice of the AFD is based on preliminary MCMC simulations in the target failure domain. It must be noted that although the AFD is chosen to be specified as a union of linear failure domains, the method does not assume any restriction with respect to the target failure domain, which is assumed to be generally non-linear. Once the AFD is determined, the ADM proceeds with a modified subset simulation procedure where the first step involves the direct simulation of points in the AFD. This is in contrast to standard subset simulation (SSM) where the first step involves standard Monte Carlo Simulations. The number of steps and the computational effort required by ADM, assuming an appropriate AFD is chosen, can be smaller than that required by SSM. Results for the benchmark problems show that both S³ and ADM are efficient for treating high dimensional reliability problems.