汽油烃类族组成测试方法对比

介绍了使用荧光指示剂吸附色谱法、多维气相色谱法的测定影响因素和近红外光谱快速法的分析特点以及三种方法在汽油族组成测定中的应用，并就三种方法对同一试样的测定结果进行了对比，找出各种测定方法容易产生误差的原因及分析对策。     

GLU♮ embedded in C++: a marriage between multidimensional and object‐oriented programming

The embedding of a small but expressive language of multidimensional functional programming in a well known and widely used language of object‐oriented programming leads to the combination of two radically different programming models. In this paper, we formally define the syntax and semantics of GLU^?, which can be thought of as the multidimensional core of Lucid and GLU, and we describe its implementation as a language embedded in C++. With the aid of a few examples, we argue that the marriage of the two programming models is not only compatible and natural, but also that it produces a new and interesting hybrid language. Copyright © 2004 John Wiley & Sons, Ltd.     

Profile-Based Linguistic Uniformity as a Generic Method for Comparing Language Varieties

In this text we present``profile-based linguistic uniformity', a methoddesigned to compare language varieties on thebasis of a wide range of potentiallyheterogeneous linguistic variables. In manyrespects a parallel can be drawn with currentmethods in dialectometry (for an overview, see,Nerbonne and Heeringa, 2001; Heeringa, Nerbonneand Kleiweg, 2002): in both casesdissimilarities between varieties on the basisof individual variables are summarized inglobal dissimilarities, and a series oflanguage varieties are subsequently clusteredor charted using multivariate techniques suchas cluster analysis or multidimensionalscaling. This global similarity between themethods makes it possible to compare them andto investigate the implications of notabledifferences. In this text we specifically focuson, and defend one characteristic of ourmethodology, its profile-based nature.     

Improving Measurement Precision of Test Batteries Using Multidimensional Item Response Models.

A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts

Theknowledge transfer problem in artificial intelligence consists of finding effective ways to elicit information from a human expert and represent it in a form suitable for use by an expert system. One approach to formalizing and guiding this knowledge transfer process for certain types of expert systems is to use psychometric scaling methods to analyze data on how the human expert compares or groups solutions. For example, Butler and Corter [1] obtained judgments of thesubstitutability of solutions from an expert, then analyzed the resulting data via techniques for fitting trees and extended trees [2]. The expert's interpretation of certain aspects of the solutions were directly encoded as production rules, allowing rapid prototyping. In this paper we consider the problem of combining information from multiple experts. We propose the use of three-way or individual differences multidimensional scaling, tree-fitting, and unfolding models to analyze two types of data obtainable from the multiple experts: judgments of the substitutability of pairs of solutions, and judgments of the appropriateness of specific solutions to specific problems. An application is described in which substitutability data were obtained from three experts and analyzed using the SINDSCAL program [3] for three-way multidimensional scaling [4].     

Multivariate analysis of the correlation between noctuidae subfamilies and the chemical structure of their sex pheromones or male attractants

Female-emitted pheromones and sex attractants of Noctuidae were investigated using a specific computer procedure to analyze data collected from the literature. Correspondence analysis was used to survey the structure-activity relationships of sex pheromones in seven subfamilies. Structural, stereochemical, and functional features of active molecules were related to taxonomy. This multidimensional analysis revealed that the prevalent chemical frame of noctuid moth pheromones was a monounsaturated acetate withZ stereochemistry and a double bond on the fifth carbon closest to the nonfunctional branch of the molecule. Possible phylogenetic relationships within Noctuidae and between Noctuidae and other families are discussed in light of the sex pheromone biochemistry. Female sex pheromones appeared to be an additional character to be considered in the classification of noctuid moths.     

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.

Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.     

基于扩散距离和MDS的非刚性模型相似性分析

在非刚性模型相似性分析方法中, 基于测地距离的等距嵌入方法对模型拓扑变化非常敏感, 对于有局部拓扑变化的完全相似的模型也无法得到正确结果。为了弥补这一不足, 提高非刚性模型相似性分析的准确性, 将扩散距离与多维尺度分析(MDS)相结合, 计算非刚性模型的等距嵌入模型, 再利用迭代最近点(ICP)算法通过计算嵌入模型的相似性来实现原始非刚性模型的相似性分析。实例证明该方法对含有拓扑变化的非刚性模型可以得到理想的相似性分析结果。     

基于特征融合的调制识别增强与迁移演化

针对调制识别中单一图像的特征信息不足,区分度不够高,识别范围受限的问题。本文提出了一种基于时频图和星座图特征融合的调制识别特征增强方法,利用深度学习神经网络提取信号图像的特征,构建特征空间,通过多维特征融合,挖掘和整合不同特征的优势,增强模型算法的鲁棒性。此外运用了模型迁移的方法,仅需对分类器进行训练,大幅节约了训练时间和资源,具有很强的实时性和实用性。仿真结果显示,在0db左右的条件下,相比于单一特征图像,采用特征融合增强的方法能将信号的平均识别率提高约25%,通过模型迁移,省去了卷积神经网络的训练,所需的训练时间约为迁移前的9.6%,消耗内存约为迁移前的7.3%,同时模型的识别率损失控制在了5%以内。     

口令重用行为与多维口令体系研究

互联网的迅速发展与网络服务的高度分散,促使广大网民不断注册更多的账户,并导致口令重用行为普遍化,使得用户信息面临泄露的风险。为此,基于2011年底互联网泄密门数据和大学生在线调查数据,分析了网民口令的结构特征和重用行为,并由此设计融入信息维度和分级管理思想的多维口令体系。该体系以根口令-重用码结构为基础,内容维包含多个独立的信息因子,构成口令的可记忆性主体;形式维负责形式变换,以提升口令的复杂性和安全性;时空维用于保障口令的时效性和重用性。对比量化分析结果表明,该口令体系具备良好的记忆性和便捷性,能有效抵御暴力攻击和熟人攻击。