管道超结构轴向带隙特性分析及实验研究

本文提出一种新型管道超结构元胞构型,其轴向振动带隙包括局域共振型和布拉格(Bragg)散射型两种带隙,该结构在2 500 Hz内共有两阶带隙,且第二阶带隙频率范围较宽。分别应用传递矩阵法和有限元法计算了该结构的能带结构分布及有限周期结构传输特性;搭建了包含4个元胞的管道超结构实验平台进行振动测试,并与计算结果进行对比验证;最后讨论了不同参数对其带隙分布的影响规律。结果表明,所研究管道超结构在2 500 Hz内共有两阶带隙,第一阶带隙主要为局域共振型带隙,凸台和振子的几何尺寸对其影响较大,元胞尺寸对其影响较小。第二阶带隙主要为布拉格散射型带隙,带隙宽度可达923 Hz,该带隙分布随元胞长度、凸台长度和振子厚度改变而改变。合理设计结构各部分几何尺寸,可满足工程中特定频段抑振的需求。     

基于局部加密纯无网格法非线性Cahn-Hilliard方程的模拟

为数值求解描述不同物质间相位分离现象的高阶非线性Cahn-Hilliard(C-H)方程,发展了一种基于局部加密纯无网格有限点集法(local refinement finite pointset method,LR-FPM).其构造过程为:1)将C-H方程中四阶导数降阶为两个二阶导数,连续应用基于Taylor展开和加权最小二乘法的FPM离散空间导数;2)对区域进行局部加密和采用五次样条核函数以提高数值精度;3)局部线性方程组求解中准确施加含高阶导数Neumann边值条件.随后,运用LR-FPM求解有解析解的一维/二维C-H方程,分析粒子均匀分布/非均匀分布以及局部粒子加密情况的误差和收敛阶,展示了LR-FPM较网格类算法在非均匀布点情况下的优点.最后,采用LR-FPM对无解析解的一维/二维C-H方程进行了数值预测,并与有限差分结果相比较.数值结果表明,LR-FPM方法具有较高的数值精度和收敛阶,比有限差分法更易数值实现,能够准确展现不同类型材料间相位分离非线性扩散现象随时间的演化过程.     

基于改进格林元的压裂水平井动态分析方法

以建立高效的动态分析方法为出发点,以边单元作为求解点,改进传统的格林元方法,减少未知数和求解矩阵维度;并提出基于改进格林元的加密网格加密方法,保证考虑复杂裂缝网络的压裂水平井动态模拟的早期精度.退化模型与半解析解、数值模拟结果进行对比,验证本文基于加密网格的改进格林元方法的准确性和动态分析的高效性.最后进行动态响应的敏感性分析,结果表明：①格林元方法是一种高精度的动态模拟方法,将求解节点设置在网格的边上可以提高压裂水平井动态模拟的速度;②改进格林元方法的加密基于叠加原理,不需要通过插值近似,其求解精度高.在相同加密网格条件下,基于本文改进格林元方法的加密效果比有限差分加密效果更佳;③复杂裂缝导流能力、改造区渗透率提高倍数、改造区大小等参数对压裂水平井动态特征影响较大,在动态分析和参数反演时,应着重考虑这些因素的影响.     

基于样本库的人脸图像处理方法

本文给出了一种基于样本库的人脸图像超分辨率方法,这一方法结合了人脸图像全局相似性和局部特征相似性的约束.局部结构的相似性将超分辨率解约束到人脸局部结构空间,不同尺度上的局部相似性指导搜索最优解,然后将局部结构重建的人脸图像投影到由人脸图像确定的特征人脸空间.实验结果表明该算法得到的人脸具有很好的准确性和视觉效果.     

Ti-HMS介孔分子筛的制备及其在一氧化氮催化分解反应中的活性

 在常温下制备了具有不同钛含量的大比表面积和孔体积的介孔分子筛HMS．XRD谱表明,这些Ti－HMS样品呈现出很好的六方晶形．经焙烧后,Ti－HMS样品的UV吸收光谱和XAFS谱结果表明,在Ti－HMS（1）和Ti－HMS（2）骨架中的钛离子以四配位存在,而在Ti－HMS（10）中至少有部分钛是以六配位存在．在NO存在的情况下,在273K下光催化剂Ti－HMS受紫外光照射后,导致NO分解生成N2,O2和N2O．实验结果表明,具有高分散四配位钛的Ti－HMS（1）使NO以高效率、高选择性分解生成N2和O2;随着钛含量的增加,其光催化活性反而降低．     

两个典型氧桥三核铁髥配合物[Fe_3O(O_2CCH_2OC_6H_5)_6(3H_2O)]和[Fe_3O(TIEO)_2(O_2CPh)_2Cl_3]的局域自旋和磁性质的理论探讨(英文)

用从头算波函数(UHF或UDFT波函数)代替ZILSH方法中的半经验波函数得到了ABLSH方法,接着用该方法研究了两个典型氧桥三核铁髥配合物[Fe3O(O2CCH2OC6H5)6(3H2O)]和[Fe3O(TIEO)2(O2CPh)2Cl3]的局域自旋和磁性质。通过计算得到的局域自旋结果和前人的具有可比性,同时所得的磁交换耦合常数和实验值很吻合。该方法可作为研究海森堡型磁性系统(HM)的新工具。     

Effects of local thickness defects on the buckling of micro-beam

A buckling model of Timoshenko micro-beam with local thickness defects is established based on a modified gradient elasticity. By introducing the local thickness defects function of the micro-beam, the variable coe-cient differential equations of the buckling problem are obtained with the variational principle. Combining the eigensolution series of the complete micro-beam with the Galerkin method, we obtain the critical load and buckling modes of the micro-beam with defects. The results show that the depth and location of the defect are the main factors affecting the critical load, and the combined effect of boundary conditions and defects can significantly change the buckling mode of the micro-beam. The effect of defect location on buckling is related to the axial gradient of the rotation angle, and defects should be avoided at the maximum axial gradient of the rotation angle. The model and method are also applicable to the static deformation and vibration of the micro-beam.     

Learning Visible Thermal Person Re-Identification via Spatial Dependence and Dual-Constraint Loss

Visible thermal person re-identification (VT Re-ID) is the task of matching pedestrian images collected by thermal and visible light cameras. The two main challenges presented by VT Re-ID are the intra-class variation between pedestrian images and the cross-modality difference between visible and thermal images. Existing works have principally focused on local representation through cross-modality feature distribution, but ignore the internal connection of the local features of pedestrian body parts. Therefore, this paper proposes a dual-path attention network model to establish the spatial dependency relationship between the local features of the pedestrian feature map and to effectively enhance the feature extraction. Meanwhile, we propose cross-modality dual-constraint loss, which adds the center and boundary constraints for each class distribution in the embedding space to promote compactness within the class and enhance the separability between classes. Our experimental results show that our proposed approach has advantages over the state-of-the-art methods on the two public datasets SYSU-MM01 and RegDB. The result for the SYSU-MM01 is Rank-1/mAP 57.74%/54.35%, and the result for the RegDB is Rank-1/mAP 76.07%/69.43%.     

Smooth equivalence of differential equations and linear automorphisms

For systems of ordinary differential equations admitting linear automorphisms, we consider the problems of smooth equivalence and linearization preserving these automorphisms. Translated fromMatematicheskie Zametki, Vol. 66, No. 4, pp. 567–578, October, 1999.     

基于半参数回归模型的最小一乘局部线性算法

根据最小一乘准则,推导出最小一乘局部线性估计的计算方法,并通过对模拟数据的计算和分析,对比最小一乘核算法和最小二乘局部线性算法,验证了最小一乘局部线性算法是一种有效的,稳健的估计方法,并且有降低边界效应的作用．