Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics (SPH) is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.     

基于区域增长的连通域标记算法的优化

与以往基于区域增长的连通域标记算法用目标点作为种子不同,优化算法将游程作为种子,结合游程和种子算法的优点,减少种子的数量和目标点邻域的搜索次数.实验表明,本算法能有效地减少算法冗余操作,明显提高效率,且性能优于其它算法.     

求解矩阵Penrose广义逆的混合智能算法

有效求解矩阵Penrose广义逆是一个困难的问题．首先将求解Penrose广义逆转化为求最小极值问题,结合粒子群算法和差分算法的优点,设计了混合智能算法．仿真实验结果表明：混合智能算法求解Penrose广义逆是有效的和可行的．算法易于计算机实现,计算精度高．     

基于蚁群算法的最小电路测试集生成算法

为了提高电路故障的测试速度和精度．在现有的传统故障测试集生成算法的基础上,引入蚁群生物的路径规划策略,设计一种基于蚁群算法的电路故障最小测试集生成算法。详细阐述蚁群算法的设计思想,给出数字电路测试问题向蚁群规划问题的转换过程,包括基于蚁群算法的最小测试集生成流程。并对设计的电路故障最小测试集生成算法进行了理论上的性能分析和实际的测试验证,测试结果表明该算法具有较高的综合效率。     

基于遗传算法的高校试题组卷系统的研究

阐述了高校组卷系统的原理,重点研究了作为系统关键的组卷策略运算过程,提出了一个改进的动态分层遗传算法作为高校组卷系统的组卷策略,最后通过实验检验其性能。     

有多重极点时部分分式展开式的简易算法

对具有多重极点的有理函数,本文给出了部分分式展开的实用算法,该算法不需求导数值,     

Disaster preparedness,adaptive politics and lifelong learning: a case of Japan

Preparedness for disaster scenarios is progressively becoming an educational agenda for governments because of diversifying risks and threats worldwide. In disaster-prone Japan, disaster preparedness has been a prioritised national agenda, and preparedness education has been undertaken in both formal schooling and lifelong learning settings. This article examines the politics behind one prevailing policy discourse in the field of disaster preparedness referred to as ‘the four forms of aid’ – ‘kojo [public aid]’, ‘jijo [self-help]’, ‘gojo/kyojo [mutual aid]’. The study looks at the Japanese case, however, the significance is global, given that neo-liberal governments are increasingly having to deal with a range of disaster situations whether floods or terrorism, while implementing austerity measures. Drawing on the theory of the adaptiveness of neo-liberalism, the article sheds light on the hybridity of the current Abe government’s politics: a ‘dominant’ neo-liberal economic approach – public aid and self-help – and a ‘subordinate’ moral conservative agenda – mutual aid. It is argued that the four forms of aid are an effective ‘balancing act’, and that kyojo in particular is a powerful legitimator in the hybrid politics. The article concludes that a lifelong and life-wide preparedness model could be developed in Japan which has taken a social approach to lifelong learning.     

用于线性优化的基于核函数的动态步长原-对偶内点算法

In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming （LP） problems. The algorithms are based on a few kernel functions, including both serf-regular functions and non-serf-regular ones. The dynamic step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that the algorithms with dynaraic step size are more efficient than those with fixed step size.     

物体表面不同角度采集的三维图像配准的一种新算法

提出了一种能有效实现物体表面三维图像数据配准的算法.该算法利用了三维数据采集时的三维点的阵列信息,这些三维点的阵列信息在采集原始三维数据时可以很容易获得,同时,还利用了三维数据矢量/顶点相似特征作为数据匹配的基础.采用迭代最小均方误差算法来自适应优化变换矩阵参数.这些方法可以有效地提高三维图像配准的性能,加快匹配过程的运算速度.实验结果表明该算法可以获得较好的配准后三维图像的主观效果.尽管该方法主要针对人头模型,但经过少许修改后即可适用于其他物体.     

面向云计算基础课程的Paxos算法教学设计研究

云计算作为一种新型计算模型,自诞生以来受到了业界与学术界广泛关注,市场规模迅速扩大,国内许多大学的计算机专业都开始关注云计算课程教学。针对云计算课程技术内容新、涉及理论多、应用范围广,以及与商业运营模式结合紧密等特点,以谷歌公司的云计算核心技术分布式锁服务Chubby所采用的Paxos算法为例,对云计算教学内容与教学方法进行设计探究。详细阐述了Paxos算法工作原理,并设计了具体教学案例,该教学方案在实际教学实践中获得了良好的教学效果,加深了学生对Paxos算法的理解。