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 …     

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.     

四气门汽油机缸内气流运动LDA测量结果的小波分析方法

主要采用小波分析方法研究了四气门汽油机缸内空气运动的频率结构。在一台四气门汽油机上 ,使用激光多普勒测速仪 (L DA)在倒拖工况下测量了气缸内的滚流运动 ,使用了 MATL AB 5 .1数学工具包中的小波分析方法对试验结果进行数据处理 ,得到了时域和频域分解速度信息。从这些信息中可以清楚地看到滚流 (低频湍流 )在压缩末期破碎并形成高频湍流的现象     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.     

3-D hydrodynamics generated in a stirred vessel by a multiple-propeller system

The aim of this study is to optimize the position and the number of propellers in a non-standard tall vessel. Laser sheet flow visualization experiments were carried out for selected geometrical arrangements which produced stable flow patterns and good transport between the propellers. Four double-propeller arrangements corresponding to frequent industrial cases and a three-propeller system have been chosen. Comparison of LDA measurements in the r-z plane, dimensionless global parameters N_Qp, Nt_m, N_p, E_p and spatial distribution of local energy dissipation rate ? shows that a three-propeller system is the most efficient.     

一种结合主题模型的推荐算法

针对传统协同过滤推荐算法存在的冷启动、数据稀疏以及相似度度量的准确性问题,基于LDA主题模型对文本隐式主题挖掘的有效性和KL散度在主题分布相似性度量的准确性,提出了结合LDA主题模型的矩阵分解推荐算法。首先,利用改进的LDA算法输出项目-主题分布,并用困惑度作为主题数设置的修正函数;然后分别基于余弦相似度和KL散度计算得到项目相似度矩阵,将得到的相似度矩阵结合原评分训练集输出预评分,再将预评分填充到训练集;最后将训练集输入ALS矩阵分解算法得到推荐结果。通过MovieLens数据集的实验结果表明,该算法在不同隐式参数设定下均能得到比ALS推荐算法以及更小的预测误差,并且最优预测误差小于传统推荐算法。该实验说明了通过集成LDA主题模型的ALS算法效果要优于其他推荐算法。     

基于主题模型与信息熵的中文文档自动摘要技术研究

提出了一种基于LDA模型以及信息熵的文档自动摘要技术,即通过LDA模型对文档进行浅层语义分析,得到文档的主题分布以及不同主题下的词语分布;通过对主题的分析,可以得到最能代表文档中心思想的主题,以及该主题下的词语分布。同时,提出了一种新的基于信息熵的度量句子重要性的方法,并将该方法应用于文档的关键句抽取过程中。该方法将文档中句子的出现看成一个随机变量,通过对随机变量建模并度量它的信息熵来选取文档中的关键性语句。实验结果表明,应用主题模型与信息熵摘取的文档摘要能有效地从文档中摘出中心句。     

一种基于主题建模的代码功能挖掘工具

代码复用是重要的软件复用方式之一,复用者需要理解软件代码实现的功能方能有效实施软件复用。基于主题建模技术的程序理解方法逐渐受到研究人员的重视,它能够帮助软件开发者和使用者更好地理解软件的功能。目前,基于主题建模技术的程序理解方法一般欠缺对挖掘出的Topic的语义分析,为此提出的基于代码静态分析和LDA技术的代码功能挖掘(Code Function Mining,CFM)方法可作为对这类方法的补充。CFM是一套以代码为研究对象的挖掘、筛选、组织和描述主题(Topic)的方法,该方法能够生成带描述的功能型Topic的层次结构,以供使用者更清晰和方便地浏览、学习软件的功能。功能型Topic的描述能够帮助复用者理解代码功能,其层次结构能够让复用者从不同抽象层次理解代码功能。CFM方法包括4个部分:挖掘Topic、筛选Topic、组织Topic、描述Topic。以CFM方法为基础,设计并实现了一个CFM工具。CFM工具能够分析用户提交的代码,通过Web页面向用户展示带描述的功能型Topic的层次结构。最后,对CFM方法中的几个关键算法进行实验分析,验证了CFM方法的有效性。     

一种基于上下文的医学图像ROI分类方法

感兴趣区域(ROI)的分类是医学图像的计算机辅助诊断过程的最后一步,传统方法只针对每个ROI区域单独提取特征,再利用统计学习的方法训练分类器进行分类.然而图像中每个区域所包含的视觉特征有限,很难进行准确的分类.文中提出一种基于LDA主题模型的改进模型(LDAC),考虑ROI周围区域,即图像的上下文关系,通过利用LDA对ROI周围区域所包含的上下文信息进行建模,同时结合ROI区域的视觉信息和类别标签,从而辅助ROI区域的分类,以达到提高分类准确率的目的.乳腺图像肿块分类实验表明,文中方法可提高分类的准确性.