等参数平面裂纹问题的二级分形有限元分析

针对正交各向异性板的平面裂纹问题，应用二级分形有限元的办法研究了裂纹尖端应力强度因子的计算方法．与各向同性平面裂纹问题比拟，获得正交各向异性平面裂纹问题的一般解，并将它作为整体插值函数；利用二级分形有限元对平面裂纹板进行离散，使得求解的自由度极大地减少．结果表明，只需有限粗略的网格划分和简单的插值单元就可以有效地获得较精确的裂纹尖端应力强度因子．     

平图的transition多项式的Maple计算

将现有的计算方法改进得到了一种新的计算平图的transition多项式的方法。算法使用了圈置换的方法计算每个transition操作所产生的欧拉圈的数目。利用Maple软件编写出了该算法的程序,通过这个程序,可以实现任意一个平图的transition多项式的计算。     

基于流类型的SDN数据平面故障恢复算法

软件定义网络(software defined networking,SDN)的网络拓扑中,链路故障恢复目标是保证故障恢复时延在可容忍范围内、减少数据包丢失和节约交换机存储资源.现有研究方法对链路故障恢复考虑了恢复时延、数据包丢失率、网络吞吐量等因素,没有考虑数据流对网络带宽的要求及运营商/用户的一些特殊限制.为了解决以上问题,同时满足故障恢复时延要求和运营商/用户定制化需求,提出了基于流类型的SDN数据平面故障恢复算法(failure recovery algorithm based on flow type in SDN data plane,FR-FT).该方法根据服务质量要求将数据流分为3类,将运营商/客户的定制化需求绑定到不同数据流上,根据对应约束条件对不同类型数据流制定不同故障恢复策略.仿真结果表明,该方法可以减少交换机流表项消耗、故障恢复时延、数据包丢失率.     

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.     

Curie’s principle and causal graphs

Curie’s Principle says that any symmetry property of a cause must be found in its effect. In this article, I consider Curie’s Principle from the point of view of graphical causal models, and demonstrate that, under one definition of a symmetry transformation, the causal modeling framework does not require anything like Curie’s Principle to be true. On another definition of a symmetry transformation, the graphical causal modeling formalism does imply a version of Curie’s Principle. These results yield a better understanding of the logical landscape with respect to the relationship between Curie’s Principle and graphical causal modeling.     

热模拟平面应变条件下的热轧织构研究

以微合金钢为材料,采用光学显微镜和EBSD,研究热模拟平面应变实验条件下再结晶奥氏体和变形奥氏体的织构演变.研究发现,在热模拟平面应变实验的压缩过程中,试样的两个自由端限制了变形区金属的宽向流动,达到了很好的平面应变状态.对于再结晶奥氏体相变工艺,由于相变前奥氏体发生再结晶,无畸变保留,奥氏体分解为仿晶界铁素体、贝氏体和少量的珠光体,织构为{100}011α;对于变形奥氏体相变工艺,未再结晶区的变形促进了铁素体相变,使奥氏体分解为铁素体和珠光体组织,织构为{332}113α和{113}110α.此两种工艺条件下的织构,皆为平面应变条件下的奥氏体相变织构,即热模拟平面应变实验可以达到很好的平面应变状态,可用于研究热轧过程的织构演变.     

m个点不交的C4的并的点可区别全染色

给出了m（m≥2）个点不交的C4的并的点可区别全色数。     

平面图3-可着色的3个充分条件

平面图3-可着色是指可用3种颜色对该图的顶点进行着色,使得相邻的顶点着不同的颜色.研究了平面图在长度不大于6的圈或长度不大于7的圈之间满足一定条件下是3-可着色的.     

主城区人行交通道路照明质量评价指标

采用客观与主观相结合的实验方法对重庆市主城区的部分人行道照明进行了实测,通过对大量测试信息的处理及分析,证明了半柱面照度应是人行道照明评价的最佳参数,并指出了现行人行道照明标准中评价指标的不足之处.研究中首次将计算机灰阶分离方法用于夜间行人面部特征的分析,从而得出立体感指标的适用范围和应用条件.该研究为人行道照明评价指标的制定提供了实验依据及科学的方法.     

Essential Independent Condition for Graphs to Be Hamiltonian

Let G be a graph of order n．For graph to be Hamiltonian,beginning with Dirac’s classic result in １９５２,Dirac’s theorem was followed by that of Ore in１９６０．In１９８４,Fan generalized Dirac’s theorem and Ore’s theorem as if G is a２-connected graph of order n and max｛d（u）,d（v）｝≥n/２for each pair of vertices u and v with d（u,v）＝２,then G is hamiltonian．In１９９１,Faudree et al proved that if G is a２-connected graph and,| N（u）∪N（v）|＋δ（G）≥n for each pair of nonadjacent vertices u,v∈V（G）,then G is hamiltonian．This paper generalizes the above onditions of Dirac,Ore,Fan and Faudree et al in the case of３-connected graph and proves that if G is a３-connected graph of order n and max｛｜N（x）∪ N（y）｜＋d（u）,｜N（w）∪N（z）｜＋d（v）｝≥n for every choice of６Essential independent vertices,then G is hamiltonian．