Characterizing spatial patterns of invasive species using sub-pixel classifications

Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Considerable research focuses on determining the accuracy of various remote sensing techniques for distinguishing saltcedar from native woody riparian vegetation through sub-pixel, or soft classifications. However, there is a lack of research quantifying spatial distribution patterns from these classifications, mainly because landscape metrics, which are commonly used to statistically assess these patterns, require bounded classes and cannot be applied directly to soft classifications. This study tests a new method for discretizing sub-pixel data to generate landscape metrics using a continuum of fractional cover thresholds. The developed approach transforms sub-pixel classifications into discrete maps compliant with metric terms and computes and interprets metric results in the context of the region to explain patterns in the extent, distribution, and connectivity of saltcedar in the Rio Grande basin. Results indicate that landscape metrics are sensitive to sub-pixel values and can vary greatly with fractional cover. Therefore spectral unmixing should be performed prior to metric calculations. Analysis of metric trends provides evidence that saltcedar has expanded away from the immediate riparian zones and is displacing native vegetation. This information, coupled with control management strategies, can be used to target remediation activities along the Rio Grande.     

Using Semantic Classification Trees for WSD

This paper describes the evaluation of a WSD method withinSENSEVAL. This method is based on Semantic Classification Trees (SCTs)and short context dependencies between nouns and verbs. The trainingprocedure creates a binary tree for each word to be disambiguated. SCTsare easy to implement and yield some promising results. The integrationof linguistic knowledge could lead to substantial improvement.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

Gas particle industrial flow simulation using RANSTAD

A turbulent gas particle finite-volume flow simulation of a representative coal classifier is presented. Typical values of the loading ratio permit a one-way coupling analysis. As a case study, the computational fluid dynamics code,ranstad, and the modelling aspects are discussed in some detail. The simulation indicates that small (≈ 30 μm) coal particles pass through the classifier to the furnace but that large (≈ 300 μm) particles are captured and remilled. The computational simulation indicates that the classifier performance can be improved by internal geometric modification. The commitment of the Electricity Commission of New South Wales (Pacific Power) to the exploitation of Computational Engineering for the improvement of all aspects of electricity generation is acknowledged.     

土石坝渗流及应对施工技术探析

对土石坝出现流土、管涌及冲刷破坏进行了探索分析．对当今水利技术条件下的土石坝应对技术做出了探讨性分析。     

Laguerre超群上的Hardy-Littlewood-Sobolev不等式

利用Laguerre超群K上的广义次拉普拉斯算子L定义K上的Riesz位势,并证明它是Lp(1p+∞)有界和弱(1,1)有界的,即证明K上的Hardy-Littlewood-Sobolev不等式.它为进一步分析K上的偏微分方程问题提供了一个有利的工具.     

旅游资源两分法分类依据探讨

旅游资源两分法分类依据众说不一,为初学者带来了困惑。作者通过对现有旅游资源分类理论、实践成果的观察和分析,试图明确旅游资源两分法的分类依据,并对其进行了更深层次的辨析。     

中文问句分类特征的研究

针对"不同的问句分类特征对问句分类的影响不相同,提取和处理这些特征的时间复杂度也不相同"的问题,提取问题疑问词、核心关键词(疑问词的一二级依存词和问句中心语)的主要义原、核心关键词的首义原、问句主谓宾的主要义原、命名实体、名词单(复)数等六种分类特征,采用支持向量机分类算法,对事实疑问句进行不同特征组合的分类对比实验,发现采用词义消岐技术提取的主要义原不仅对分类的准确率影响明显,而且大幅降低特征向量的维数,减少了处理时间。     

基于多子空间线性判别分析的特征提取与分类

为了解决高维小样本数据的分类中Fisherface思想判别分析方法的不足,在最大散度差准则的基础上,提出了利用多线性子空间技术对每类样本进行单独描述的方法,该方法能更准确地反映样本在类内类间的分布关系.在分类中不是以距离作为判别依据,而是按照贝叶斯决策规则得到的隶属置信度作为衡量标准.实验结果表明了该方法的有效性,和同类方法相比,有更高的识别率.