子流形上的F-调和映射

文章从子流形的平均曲率和第二基本形式长度平方的条件出发,研究了欧氏空间或球面的子流形为出发流形或目标流形的F-调和映照的稳定性,得到一些不存在性定理,从而推广了有关作者的结论.     

集值映射向量优化的最有性条件

文章首先引进了近似锥似凸集值映射的概念,并在实拓扑向量空间中建立了近似锥似凸集值映射的择一性定理,获得了近似锥似凸集值映射向量优化问题的最有性充要条件,最后给出了对偶问题并推导了对偶定理。     

三维激光扫描仪技术在地形测量中的应用

利用三维激光测量技术进行山区复杂地形测量的流程与方法,实验通过使用徕卡ScanStation2型号的三维激光扫描仪对西安市白鹿原北坡进行了扫描,对扫描后的各测站数据进行基于标靶的拼接,对拼接后的点云数据进行预处理。将基于扫描仪坐标系统的点云数据转换到基于施工独立坐标系统后,使用Geomagic和cass软件绘制地形图等高线。在测区内布设一定数量检核点,扫描时在检核点上安置蓝白标靶,采用全站仪测量检核点的独立坐标与扫描仪测量的坐标进行对比来验证扫描数据的精度,得到平面坐标差值绝对值最大值为0.024 m,而高程的差值绝对值最大值为0.029 m,实验结果表明,三维激光扫描仪满足地形测量精度要求。     

基于流形的图像检索算法的研究

针对静止图像中轮廓信息的表示,利用活动基模型,共享简图算法与和数-最大数简图计算结构来分别表示、学习和识别变形的模板,实现了同类图像的检索。     

域上交错矩阵空间的保秩导出映射

设F表示域,n是大于等于4的整数.Kn(F)是由域上的所有n阶交错矩阵构成的集合.设fij(i,j=1.2,…,n)是F到F上的映射,f是Kn(F)到Kn(F)的映射并且映射的形式被定义为f:[aij]|→[fij(aij)],(V)[aij]∈Kn(F)则f称为fij(i,j=1,2,…,n)诱导的映射(即导出映射)...     

弱ω-不连通空间

.一个拓扑空间被称为是弱ω不连通的, 如果它可以表示成可数无限个非空闭集的不交并. 在实直线上, 已知任何区间都不是弱ω不连通的. 本文证明了两条更一般的结论: (1) 任何连通, 局部连通, 局部可数紧的T2空间都不是弱ω不连通的; (2) 任何可序化的连通空间都不是弱ω不连通的     

向量优化问题ε-弱有效解的Lagrange乘子定理

本文在邻近锥次似凸性假设下,建立了集值映射向量优化问题ε-弱有效解的Lagrange乘子定理。首先,利用择一性定理,给出了集值优化问题ε-弱有效解的一个必要性条件。进一步,建立了集值优化问题ε-弱有效解的充分必要条件。最后,在邻近次似凸性假设下,建立了集值映射向量优化问题ε-弱有效解的Lagrange乘子定理。本文的主要结果推广了已有文献中的相应结果到近似解的情形,同时将次似凸性条件减弱到邻近次似凸的假设下。     

B（H）上广义Jordan triple可导映射

利用算子论方法,证明了YA∈（B）（B）,若δ满足δ（AA＊ A）=δ（A）A＊A-Aδ（A）＊A＋AA＊δ（A）,则（E） S,T∈（B）（B）和λ∈{C＼R}∪{0},且S＊-S=T＊-T=λi,使得（a） A∈（B）（B）有δ（A）=SA-AT.     

Construction of AFLP-based genetic linkage maps for the Chinese shrimp Fenneropaeneus chinensis

Fenneropaeneus chinensis is an important species in marine fishery resources and aquaculture in China. A genetic linkage map is essential for improving the efficiency of its breeding by marker-as- sisted selection and identifying commercially important genes. Linkage maps of F. chinensis were constructed with an F2 mapping population (110 progenies) using amplified fragment length polymor- phic (AFLP) marker in this study. Fifty-five AFLP primer combinations produced 532 AFLP markers fitting for map strategy in mapping family. The markers with 3:1 segregating ratios were analyzed using F2 intercross model for the common linkage map, while the markers with 1:1 ratio were analyzed using the pseudo-testcross strategy. The maps of male, female and common were constructed. The female map included 103 markers that formed 28 linkage groups, covering a total length of 1090 cM. All mark- ers were linked with the linkage groups. Segregation distortion was observed for 6 of 103 markers in the female map. The average distance between markers was 14.53 cM and ranged from 4.4 to 24.8 cM. The male map included 144 markers that formed 35 linkage groups. Ten markers remained unlinked in male map. Segregation distortion was observed for 7 of 144 markers in the male map. The total dis- tance of male map covered 1617 cM. The average distance between markers was 16.36 cM. The male map was 32.6% longer than the female map, which may reflect sex-specific recombination rates in Chinese shrimp. The common map was composed of 216 markers, including in 44 linkage groups covering a total distance of 1772.1 cM. Two markers remained unlinked. No distorted markers of 216 markers were shown in the common map. The distance between markers was 10.42 cM. An average estimated genome size for the Chinese shrimp was 2420 cM, which was consistent with the relative size of the Penaeid genome. The distribution of AFLP markers was relatively even in chromosomes of Chi- nese shrimp maps. The linkage analysis presented in this work provided some insight into the level of polymorphism and genetic variation of Chinese shrimp.     

Multi-Dimensional Traffic Flow Time Series Analysis with Self-Organizing Maps

The two important features of self-organizing maps （SOM）, topological preservation and easy visualization, give it great potential for analyzing multi-dimensional time series, specifically traffic flow time series in an urban traffic network. This paper investigates the application of SOM in the representation and prediction of multi-dimensional traffic time series. Ffrst, SOMs are applied to cluster the time series and to project each multi-dimensional vector onto a two-dimensional SOM plane while preserving the topological relationships of the original data. Then, the easy visualization of the SOMs is utilized and several exploratory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vectors evolve with time. Finally, the k-nearest neighbor （kNN） algorithm is applied to the clustering result to perform short-term predictions of the traffic flow vectors. Analysis of real world traffic data shows the effec- tiveness of these methods for traffic flow predictions, for they can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously.