Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the ‘slope’ of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures—an assumption that does not fit the case of continua where any section of a gradient is as ‘pure’ as any other section like in modulations of grassland species composition.Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation.The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R²=0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R²=0.76), water supply (R²=0.66) and nutrient supply (R²=0.75), while models for the cover of single species were weak.Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.     

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.     

KSpaceNavigator as a tool for computer-assisted sample tilting in high-resolution imaging, tomography and defect analysis

This article describes a novel software tool, the KSpaceNavigator, which combines sample stage and crystallographic coordinates in a control sphere. It also provides simulated kinematic diffraction spot patterns, Kikuchi line patterns and a unit cell view in real time, thus allowing intuitive and transparent navigation in reciprocal space. By the overlay of experimental data with the simulations and some interactive alignment algorithms, zone axis orientations of the sample can be accessed quickly and with great ease. The software can be configured to work with any double-tilt or tilt–rotation stage and overcomes nonlinearities in existing goniometers by lookup tables. A subroutine for matching the polyhedral shape of a nanoparticle assists with 3D analysis and modeling. The new possibilities are demonstrated with the case of a faceted BaTiO₃ nanoparticle, which is tilted into three low-index zone axes using the piezo-controlled TEAM stage, and with a multiply twinned tetrahedral Ge precipitate in Al, which is tilted into four equivalent zone axes using a conventional double-tilt stage. Applications to other experimental scenarios are also outlined.     

混合驱动可控压力机的创新设计

混合驱动可控压力机是一个二自由度机构，能够提供多组输出运动规律。其创新设计以型综合为研究重点，通过型综合得到多种独立的结构异构体，然后根据机构组成原理，对各种异构体进行变换，最后得到了适于拉伸工艺的混合驱动压力机的机构构型。     

沱茶中茶多酚的分析与鉴定

用索氏提取器以三氯甲烷为萃取剂,在95 ℃下从沱茶中提取茶多酚。用旋转蒸发器将滤液浓缩,有机相中加入三氯甲烷(V(三氯甲烷):V(浓缩液)=3:1)将咖啡因萃取分离、去除。水相中加入乙酸乙脂(V(乙酸乙酯):V(水)=3:1)将沱茶提取物萃取分离。以硅胶G作填充剂,以乙酸乙酯(V(乙酸乙酯):V(乙醚)=4:1)为洗脱剂进行柱层析。用傅里立变换-红外光谱法(FT/IR)测定沱茶提取物待测组分的红外光谱图,提供官能团的有关信息。确定待测组分的可能结构;应用气相色谱-质谱法(GC/MS)对其进行分析与鉴定,由电子电离源质谱(EI/MS)获得待测组分的质谱图和相关数据,进而对子离子裂解途径和特征离子进行辅助解析,确证待测组分为茶多酚。为开发利用沱茶提供了科学依据。     

SWP-LE智能流量积算控制仪的应用

本文主要介绍了SWP-LE系列表计的实用功能、工作原理及实际操作中的注意事项,论述了该表计在生产中的应用价值.     

一种基于小波变换的织物组织与结构参数检测方法

织物组织结构及参数的正确识别与分析是一项费时而又重要的工作。本文提出了一种运用图像处理技术检测织物纹理方向、识别织物组织以及经纬密测量的新方法。把小波变换对织物图像信号良好的分解性能与Radon变换对纹理直线的检测特性结合，运用于织物纹理方向的检测分析、织物组织识别及经纬密测量。相关实验表明该方法不仅能较准确测定纹理方向和经纬密等结构参数，而且其检测的结果能为正确推断与识别织物所属的基本组织类型提供有效依据。     

分布式环境下全局序列模式挖掘技术研究

由于分布式环境下挖掘全局序列模式常常产生过多候选序列,加大了网络通信代价.为此提出一种基于分布式环境下的全局序列模式快速挖掘算法.该算法将各站点得到的局部序列模式压缩到一种语法序列树上,避免了重复的序列前缀传输;基于合并树中节点序列规则和简单的特点,提出一种项扩展和序列扩展剪枝策略,有效地约减了候选序列,减少了网络传输量,从而快速生成全局序列模式.理论和实验表明,在大数据集环境下该算法性能优越,能够有效地挖掘全局序列模式.     

一种将静态电流与动态电流相结合的集成电路故障定位算法

本文提出了一种新的故障定位算法。该算法将静态电流诊断方法与基于小波分析的动态电流诊断方法相结合，并通过模式识别进行决策，实现可精确到电路元件的CMOS集成电路多故障定位。经过仿真实验证明，该算法的可行性和有效性。     

T形配筋应变强化UHPC梁弯曲破坏机理

为分析应变强化UHPC梁弯曲破坏时UHPC和钢筋应变间关系,对5根梁试件进行了三分点加荷纯弯试验,试件变化参数为配筋率和钢筋种类.绘制了钢筋与应变强化UHPC的荷载-挠度曲线,将T形梁破坏过程分成3个阶段:弹性阶段、裂缝发展阶段、持荷至破坏阶段进行分析.并绘制了钢筋与应变强化UHPC的荷载-应变曲线,深入分析了在整个受弯过程中钢筋与UHPC的协同工作性.同时,结合UHPC和钢筋的应力-应变关系,将加载过程分为4个阶段,定量分析每个阶段钢筋和UHPC对抗弯承载力的贡献度.结果表明:钢筋达到屈服应变之前,钢筋与UHPC粘结可靠,保持应变一致性而共同受力;整个受弯过程中,应变强化UHPC抗拉强度提供的抗力与钢筋提供的相比占据重要比例(低配筋率时更大),建议在进行应变强化UHPC截面承载力计算时,应充分考虑UHPC抗拉强度的作用,保证承载能力计算方法的准确性.