四程抽运Yb:YAG盘式激光器

报道了激光二极管阵列(LDA)抽运Yb:YAG盘式激光器。Yb:YAG晶体掺杂浓度10-at.%,几何尺寸为Φ10mm×300μm,采用铟焊工艺将其焊接到水冷系统上,用可调式恒温水箱对其进行温度控制。利用蒙特卡罗方法对抽运光斑的大小进行了计算,将反射镜轴向与光纤头轴向夹角控制在120以内,以使抽运光斑半径与基模光斑半径比符合模式匹配原则。使用两个Φ=30mm,R=50mm的球面反射镜,完成了四程抽运。在LDA抽运功率为4.13W时,获得了最高功率为670mW的1030nm连续激光输出,光-光转换效率为16.2% 。     

Lift force on bubbles in a bubble column reactor: Experimental analysis

The lift force acting on bubbles in a swarm has been estimated by analyzing the instantaneous velocity-time data obtained using LDA in a cylindrical bubble column. Phase distinction was achieved through the multiresolution analysis of the velocity-time data. Several important issues related to the transverse motion of bubbles subjected to a shear field have been discussed quantitatively. The actually measured bubble sizes, the respective slip velocity values in transverse and axial directions and the local shear rates (γ) enabled the verification of known formulations for the lift coefficient (C_L) for bubbles. At many locations in the column the radial flux of the gas phase by turbulent dispersion and the radial slip were estimated. The radially inward movement of bubbles from low to high axial velocity (from column wall to center, i.e., C_L<0) was observed at most of the measurement locations. The local lift coefficient was estimated using the transverse drag force and the values support the results from the material balance approach. The estimated C_L values showed a wide variation over the column cross-section.     

The slabs for the rutile TiO2(110) surface

Traditionally, we use a slab to mimic a surface and we constrain the slab to have the bulk-terminated 2D lattice constants. Here we propose a different model in which we impose no constraints, allowing all coordinates including the 2D slab itself to relax. We perform DFT calculations on both models. We find that the unconstrained slabs yield better agreement with experimental results and they have lower total energies. The optimized 2D lattice constants of the unconstrained slabs eventually converge to the attached bulk value. The total energy difference reveals that, with odd number trilayers, the unconstrained slab is much closer to the corresponding constrained slab. The surface energies both converge to the individual values with the number of atomic layers.     

基于TSCM模型的网络短文本情感挖掘

针对网络短文本情感挖掘问题,提出一种新的基于LDA和互联网短评行为理论的主题情感混合模型TSCM,TSCM模型中的整篇评论中每个句子的主题分布是不同的,TSCM产生词的流程是先确定词的情感极性,再确定词的主题,TSCM考虑了词与词之间的联系.真实数据集Movie与Amazon上的大量实验表明,与代表性算法JST、S-LDA、D-PLDA和SAS相比较,TSCM模型能对用户真实情感与讨论主题进行更加有效的分析建模.     

Extensions of LDA by PCA mixture model and class-wise features

Linear discriminant analysis (LDA) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter. While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner. Second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modelling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for synthetic data classification, hand-written digit recognition, and alphabet recognition.     

High Power Fiber Bundle Array Coupled LDA Module

1 Introduction High power laser diode arrays (LDA) have many advan- tages such as small volume, long working life, high slope efficiency and high optical density, so they have many applications in medical treatment, material pro- cessing, and for the pumping source of solid laser and etc. But unfortunately, the LDA can not be easy to use directly in these fields because of their poor beam quality and extremely asymmetric divergent beams (!x≈ 5°～10°and !y≈20°～35°, for example), so it …     

Random Sampling for Subspace Face Recognition

Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.     

基于分块PCA的人脸识别方法

本文提出了一种称为M2PCA＋FDA的新的人脸识别方法．新方法从模式的原始数字图像出发，先对样本图像进行分块，对分块得到的子图像矩阵采用PCA进行特征抽取，从而得到能代替原始模式的低维的新模式，然后，对新模式施行“Fisherfaces”方法，实现模式的分类．其特点是能有效地抽取图像的局部特征，正是这些特征使此类模式区别于彼类．在ORL和NUST603两个人脸数据库上对M2PCAA-FDA方法进行了测试，实验的结果表明，本文提出的方法在识别性能上优于“Fisherfaces”方法和PCA方法．     

一种改进的线性判别分析法在人脸识别中的应用

提出了一种新的基于LDA的人脸识别算法。该方法重新定义了样本的类间散布矩阵，在原始的定义基础上增加了一种径向基函数（RBF）调节类间距离，使得在选择投影方向时能更好地分升各类样本；同时该方法存类间散布矩阵与类内散布矩阵的特征分解的基础上，通过变换求出符合Fisher准则的最优投影方向，可以证明这样得到的投影方向同时具有正交性与统计不相关性。通过ORL人脸数据库的数值实验，表明了该算法比传统算法有更好的性能。     

基于邻域信息的图像分割技术

提出一种基于图像邻域信息的分割方法.首先,根据像素点邻域信息得到高维特征向量;然后采用典型相关分析(CCA)改进线性判别分析(LDA)中的变换矩阵,使得特征向量的降维具有自适应性;最后用最近邻法对降维后的特征向量进行分类,从而实现了图像的分割.试验中,选取人脸图像分割来验证该方法,结果显示出其具有良好的分类效果.