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 …     

高功率LDA紧密侧面抽运Nd:YLF两级双程离轴放大系统实验研究

 介绍了一台10mm口径两级双程离轴放大系统，实现了对5mm×5mm口径光束的激光放大，耦合系统采用高功率LDA紧密侧面直接抽运棒状Nd:YLF方式。分析并实验研究了在不同抽运电流、放大脉冲与放大器LDA抽运时刻的不同延时及不同注入能量条件下，放大系统及光束每次放大时放大特性的规律。实验得到：在放大系统5mm×5mm软光阑处注入1.58mJ能量时，放大系统可输出129.2mJ能量，能量提取效率达到19.5%，满足该系统的设计指标。     

Interplay of Electronic Structure and Bulk Properties in 2D and 3D Ternary Carbonitrides from First Principles

The changes in electronic structure and hardness as inferred from the bulk modulus are investigated for model structures of ternary compounds XC₃N₃ (X = B, Al, P, As, Ga) within the framework of density functional theory (DFT). The optimisations of the proposed two‐ (2D) and three‐dimensional (3D) structures and the calculations of the bulk moduli are performed by a pseudo potential method. The electronic structures are calculated with the augmented sphere wave method (ASW). The obtained hardness for 2D BC₃N₃ system (B₀ ～ 220 GPa) points to a magnitude close to that of graphitic C₃N₄. For heavier X atoms it decreases rapidly. This is equally observed for the 3D systems examined in the β‐C₃N₄ structure for which B₀(β‐BC₃N₃) amounts to ～330 GPa. Within the magnitude of the well known hard material cubic BN, the BC₃N₃ phases can be predicted as candidates for ultra hard materials. The electronic effect induced by the chemical nature of the X substitutional was examined according to its position in the periodic table i.e. X_III or X_V. Both, band structures and the electron localisation function (ELF) were used for this analysis. The ELF plots show a decreasing covalency with heavier X‐atoms. Potential applications of the devised systems are proposed such as dopings with atoms (Li, rare gas) and molecules (N₂).     

Multivariate analysis of non-homogeneous peaks in liquid chromatography

Summary The effects of concentration, separation and spectral similarity as factors influencing the accuracy of iterative target testing factor analysis (ITT-FA) are investigated for three component systems by the application of analysis of variance (ANOVAR). ANOVAR is applied over a range of peak separations to map the changing effects of the three factors with increasing overlap. Two error responses were measured and analysed, (a) Relative cluster error (RCE) a measure of the error over all peaks in a cluster and (b) Relative peak error (RPE) the error of an individual peak. Multicomponent analysis (MCA) a method requiringa priori spectral information, is used as a referee method for ITT-FA.     

Applying Text Analytics for Studying Research Trends in Dependability

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability—The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)—emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like ‘security’, despite the general focus of the conferences in dependability and new trends that are related with ’machine learning’ and ‘blockchain’. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like ‘cyber-physical’, being popular at PRDC and not in the overall dataset.     

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.     

小波不变矩的低空风切变识别

针对微下击暴流、低空急流、顺逆风以及侧风低空风切变样本图像间的形状特性关系,主要研究了小波不变矩的特征提取技术在风切变识别中的应用.首先,采用基于三次B样条的小波不变矩提取风切变图像的形状特征.然后,将提取的特征通过Fisher线性判别分析(LDA)降低维数,实现风切变有效特征的提取.最后,采用三阶近邻分类器分类识别四种低空风切变.实验结果表明,该算法与应用Hu矩和Zernike矩特征进行分类识别相比,识别结果更加稳定,且平均识别率得到了较大提高,能够有效用于风切变图像的类型识别中.     

硼掺杂富勒烯储氢-从物理吸附到化学吸附的转变

本文采用密度泛函理论(DFT)中的局域密度近似(LDA)方法对硼(B)掺杂富勒烯(C_(35)B)储氢问题在前人的基础上做了进一步研究,结果表明被C_(35)B吸附的氢分子很容易解离,经历从物理吸附到化学吸附的转变,并且发现解离产物C_(34)BHCH有分子内氢转移反应发生,这时B原子仍能与氢分子有很强的相互作用,最终导致B位置以及与B最邻近的三个C原子上都有氢原子吸附.并利用过渡态理论从热力学上分析了这种反应的发生趋势.     

融合奇异值分解和线性鉴别分析的人脸识别算法

本文提出了奇异值分解（SVD）和线性鉴别分析（LDA）相结合的人脸识别算法。理论上，当两种数据或分类器具有一定的独立性或互补性时，数据融合或分类器融合才能改善识别率。SVD和LDA之间有着明显的互补之处，LDA在fisher准则下能最大限度地把不同的类别区分开来，但作为一种子空间方法，LDA敏感于位移、旋转等几何变换。而作为一种代数特征提取方法的SVD则具有位移、旋转不变性等优点。因此，将这两种方法相结合就有可能提高分类性能（好于单独的SVD方法和单独的LDA方法）。在ORL数据库上的实验表明，SVD和LDA相融合的识别方法的确提高了人脸识别率。