移动对象在空间网络数据库上的kNN查询

中国计算机学会

模式识别在Eu~(2+)光谱结构研究中的应用

由于Eu~(2+)离子在不同复合氟化物中存在不同的跃迁发射形式，主要有5d → 4f的宽带跃迁，位于365nm-650nm间和4f → 4f的窄带跃迁，中心位置在360nm附近。Eu~(2+)离子的跃迁形式决定于基质的化学组成。本工作就是用多种模式识别方法（KNN，ALKNN，BAYES，LLM，SIMCA和PCA）研究不同复合氟化物基质中Eu~(2+)离子的跃迁发射形式和基质晶体结构之间的关系，找出Eu~(2+)离子产生f → f跃迁其基质构成的一般规律性。收集了90个复合氟化物（AB_mF_n）作为样本集，根据其中Eu~(2+)离子跃迁形式的不同将它们分成两类，一类为具有f → f跃迁的基质45个；另一类为不具有f → f跃迁的基质45个。随机地选用63个基质作为训练集，其余的为验证集。每个基质样本利用其12个晶体结构参数作为描述。由于各参数间差别不大，对原始数据未进行标度化。特征提取是模式识别分析的一个重要步骤，本工作结合变化权重法，BAYES特征量评价法和SIMCA变量相关性评价法的特点，建立了一个以验评价判据式：d(i) = -5.0 + 2.3V(i) + 0.89f(i) + 7.2W(i)根据经验式，选取了变量Z_B/r_(kB)，r_(covA)/r_(covB)和Z_B/r_(covB)，并删除了变量Xσ_A，Xσ_B，r_(covA)。其它变量由于其D值接近，利用穷举法对它们进行选取，结果M，Z'_A和r_(covB)被选中。这样把这6个被选的变量作为对跃迁发射问题最相关的变量进行进一步分析。采用被选的6维变量对训练集样本施行主成份分析，结果表示前三个主成份已可解释原数据信息量的99%以上。所以分别以主成份1-3及主成份1和主成份3作了三维和二维的映射图。结果表示两类基质样本基本上分在不同区域。进一步分别用12维和6维变量对样本系进行了其它几种模式识别分析。所有这些方法对训练集的分类效果都比较理想。采取6维特征时，其正确分类率达79.4-96.8%，这说明与跃迁问题相关的大部分变量已被选入。但是结果显示，各种方法对训练集的分类有一定的差别。我们认为这是由于各种不同的方法对数据结构要求不同引起的。实验证明Bayes线性判别方法对该样本集数据的分类效果最佳。根据Bayes线性差别方法的执行得到了对基质样本分类模式，由此模式讨论了各结构参数对Eu~(2+)离子光谱结构的影响，并对七个未知基质中Eu~(2+)离子的光谱结构进行了计算机预报，结果表示KTbF_4，KBF_4，NaIn_2F_7和KLu_2F_7为具有f → f跃迁发射的基质，而NaCaF_3，MgBeF_4和MgAlF_5为不具有f → f跃迁发射的基质。

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

SEARCH FOR THE BASIC REGULARITIES OF THE TRANSITION EMISSION OF EUROPIUM(II) ION IN COMPLEX FLUORIDES USING PATTERN-RECOGNITION

The relationship between structures of complex fluorides and spectral structure of Eu(II) ion in complex fluorides (AB(m)F(n)) is investigated by means of pattern recognition methods, such as KNN, ALKNN, BAYES, LLM, SIMCA and PCA. A learning set consisting of 32 f-f transition emission host compounds and 31 d-f transition emission host compounds and a test set consisting of 27 host compounds were characterized by 12 crystal structural parameters. These parameters, i.e. features, were reduced from 12 to 6 by multiple criteria for the classification of these host compounds as f-f transition emission or d-f transition emission. A recognition rate from 79.4 to 96.8% and prediction capabilities from 85.2 to 92.6% were obtained. According to the above results, the spectral structures of Eu(II) ion in seven unknown host lattices were predicted.

模式识别在有机化合物碳—13核磁共振波谱中的应用

本文介绍了以碳—13 NMR 谱为基础,运用模式识别方法对于取代苯类有机化合物的分类情况。数据源为 CIAC-碳-13数据库。特征选择为简单的机率比率法。模式识别方法为Fisher 意义下的判别函数、KNN 及非线性映射。所得结果比较满意。