用基于邓氏灰色关联度的聚类方法对煤种进行聚类的研究

为实现煤种的准确分类,采用试验研究和定量分析的方法,测量了25组试验煤样的发热量、灰分、挥发分和硫分4项参数;运用基于邓氏灰色关联度的聚类方法,并根据其原理编写VB程序,实现该方法的程序化,对煤样的4项参数进行分析。由计算结果可知,当权值为0.4、临界值为0.68时,获得了最好的聚类结果,据此可从关联性角度研究煤样特性的相似特征,并对具备共同特征的煤种进行聚类。结果表明:在任选的25个试验样本中,误差样本只有一个,准确度为96%。     

KV490 柴油机工作过程故障的灰色诊断

目的对某柴油机工作过程的故障进行诊断.方法采用灰色系统理论的ABO型关联度分析方法.结果准确地诊断出柴油机工作过程的正常状态和故障状态.结论灰色ABO型关联度对柴油机工作过程的故障诊断具有较高的准确度.     

旅行社基于灰色关联度及熵权分析的聚类研究

提出以旅行社顾客满意度为依据的聚类方法。确定旅行社样本及评价指标体系,对满意度数据进行标准化处理,计算各指标熵权并归一确定权重,计算各旅行社样本的关联系数、关联度。由此,以离差平方和最小为聚类标准,对样本逐一聚类,得出一个动态聚类过程并用图表示。对确定的分类数,则得出确定分类。举出实例,得出分类为4类时,分为{x1,x3,x5,x6,x7,x8}{x2}{x4}{x9,x10}的结果。     

精品课程评估的灰色聚类模型

为使评估更具科学性,本文将灰色系统理论应用于精品课程的评估.依照国家精品课程评审指标(本科,2010)体系,我们利用灰色聚类方法定义了指标的白化权函数,并给出精品课程评估的灰色聚类数学模型,然后利用这一数学模型讨论了精品课程灰色聚类实例.本文的结果对当今高校教学质量工程建设具有一定的意义.     

基于灰色聚类法的老年人APP用户界面配色评价研究

目的 对老年人APP用户界面配色进行评价.方法 首先采用文献研究与焦点小组法,构建老年人APP用户界面配色评价体系;其次采用层次分析法(AHP)来确定APP用户界面配色评价体系中各评价指标的相对权重;接着根据市场调研与用户访谈的结果,与PCCS色彩体系相结合制作实验样本;然后使用灰色聚类法对实验观测值进行灰色聚类得到聚类系数,并由得到的聚类系数对聚类对象进行聚类;最后根据聚类结果对实验样本进行分类并进行结果分析.结论 本文以老年人医疗 APP 用户界面配色作为研究案例,结果验证了基于灰色聚类法的老年人APP用户界面配色评价方法的可行性与有效性.     

基于灰色关联分析的动态测量不确定度评定

提出了一种基于灰色关联分析的动态测量不确定度评定新方法.该方法分析了灰色关联度和灰色关联系数与动态测量不确定度的关系,利用灰色关联度的倒数作为动态测量不确定度的扩展系数,测量数据总体灰色关联系数作为动态测量标准不确定度,并将其与其他因素导致的不确定度进行合成,作为动态测量不确定度的最终结果.该方法尤其适用于被测信号是时变信号的动态测量不确定度评定.经仿真实验与其他方法对比,方法具有计算量小、原理简单、易于在计算机上实现、不受数据分布限制、准确度高等特点.     

KV490柴油机工作过程故障的灰色诊断

目的 对某柴油机工作过程的故障进行诊断。方法 采用灰色系统理论的ABO型关联度分析方法。结果 准确地诊断出柴油机工作过程的正常状态和故障状态。结论 灰色ABO型关联度对柴油机工作过程的故障诊断具有较高的准确度。     

基于petri网装配序列灰色聚类决策研究

利用petri网直观图形化特点建立装配模型,并根据petri网原理得出可行装配序列。影响可行装配序列的大部分因素是定性的、模糊的、非数值的,将装配序列看作灰色系统,采用灰色聚类决策方法对可行序列进行评估。分析了影响因素的灰色分类并且研究了灰色聚类决策步骤。实例分析表明,该方法为petri网原理所得可行序列进行正确的评估并且得出决策向量。     

改进的灰色关联法在柴油机故障诊断中的应用

在分析常规关联度计算方法存在问题的基础上,提出一种改进的灰色关联度对柴油机故障诊断的方法。针对柴油机故障的特征,对经过降噪后的振动信号,提取时域频域特征值相结合作为特征向量,分别通过改进的灰色关联度方法、常规灰色关联度方法以及灰色神经网络模型对待检测特征向量和标准模式向量进行关联度计算。对结果进行分析,得知改进的灰色关联度分析方法克服常规灰色关联度容易误判的缺陷,同时验证改进的灰色关联度方法大大的提高柴油机故障诊断的精度,说明该方法是一种有效可行的方法。     

基于灰色理论的电站机组状态监测与故障诊断方法

本文主要研究了灰色理论在大型旋转机械的状态监测与故障诊断中的方法与应用问题。鉴于一般关联度分析方法在故障诊断应用中没有考虑到特征参数对诊断结果的不同影响,本文提出了加权关联度这一新概念,并给出了加权关联度用于旋转机械诊断中的方法与步骤。这种诊断方法能根据诊断结果采用自学习修正方法来修正关联度参数,提高诊断的准确度,并有很好的前景。     

Clustering of polarization-encoded images

Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images. The spatial coherence of polarization information is considered. Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model. The proposed algorithms are applied and validated with two different Mueller images acquired by a fully polarimetric imaging system.     

Clustering in Fe-Mo alloys

A quantitative study was made of the diffuse scattering due to clustering at 550° C in an Fe-6.1 at. % Mo alloy. The intensity due to local order was separated from the effects of different atomic sizes of the species and first-order thermal diffuse scattering, making use of the symmetry of these contributions. The Warren local-order parameters have been derived and used in a computer program to generate the corresponding atomic distributions. Comparison of the atomic configurations for clustering in this alloy are made with those for our previous measurements on a more dilute alloy, Fe-3.9 at. % Mo at the same ageing temperature.In both alloys there are irregularly shaped Mo-rich clusters, but these are very dilute. For the smallest clusters there is some resemblance to the equilibrium precipitate Fe₂Mo. This weak Mo concentration in these zones explains why previous investigators have found little hardening due to GP zones in this alloy. Also, the average displacement of Fe atoms from lattice sites is less in the alloy of 6.1 at. % Mo than in the more dilute alloy; this suggests there may be a relationship of these displacements to alloy-softening.The available Mössbauer spectra for this alloy are re-examined, and the suggested assignment of the Fe peaks to specific numbers of Mo neighbours in the first two shells is revised on the basis of the computer-generated atomic configurations; the third neighbour shell appears to be important.     

Clustering in globally coupled oscillators

In this paper we describe approaches to clustering in systems of globally coupled identical oscillators. The first of these approaches is based entirely on the symmetry of such systems, and gives information about the behaviour of the systems near degenerate bifurcation points. We summarize existing results from such analysis, and indicate why further techniques are required to augment the symmetry-based methods. This leads to a second approach based on constructing certain reduced models. This modelling approach relies indirectly on symmetry, using the fact that the systems in question have many invariant subspaces as a result of their symmetry. It is shown how knowledge of behaviour on certain subspaces can be used to predict behaviour on others subspaces, even when their dimensions are different. In applications, this approach can be used to predict the stable clustering behaviour that cannot be predicted by other approaches and may be hard to find numerically. All results are illustrated with examples.     

Index-adaptive Triangle-Based Graph Local Clustering

Motif-based graph local clustering (MGLC) algorithms are generally designed with the two-phase framework, which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result. Despite correctness, this framework brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations. This research develops a purely local and index-adaptive method, Index-adaptive Triangle-based Graph Local Clustering (TGLC+), to solve the MGLC problem w.r.t. triangle. TGLC+ combines the approximated Monte-Carlo method Triangle-based Random Walk (TRW) and deterministic Brute-Force method Triangle-based Forward Push (TFP) adaptively to estimate the Personalized PageRank (PPR) vector without calculating the exact triangle-weighted transition probability and then outputs the clustering result by conducting the standard sweep procedure. This paper presents the efficiency of TGLC+ through theoretical analysis and demonstrates its effectiveness through extensive experiments. To our knowledge, TGLC+ is the first to solve the MGLC problem without computing the motif weight beforehand, thus achieving better efficiency with comparable effectiveness. TGLC+ is suitable for large-scale and interactive graph analysis tasks, including visualization, system optimization, and decision-making.     

CONCOR聚类不动点研究

CONCOR聚类有着广泛的应用,其结果取决于CONCOR变换的不动点,然而CONCOR变换都有哪些不动点的研究至本文以前还是一个国际关注的开放问题.本文创新地提出了完美矩阵,朴素矩阵,0均值不动点等全新概念.证明了完美矩阵,朴素矩阵都是CONCOR变换的本原不动点,并给出了从本原不动点构造组合不动点的方法,从而解决了C...     

Information Theoretic Weighted Fuzzy Clustering Ensemble

In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM), Reliability Based Graph Partitioning (RBGP) and Reliability Based Hyper Clustering (RBHC) as three new fuzzy clustering consensus functions. Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation. Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble. To do so, the new metric is defined to estimate the fuzzy cluster unreliability; then, the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator (RDCI). The time complexities of RBHC and RBGP are linearly proportional with the number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.     

Clustering scientific documents with topic modeling

Topic modeling is a type of statistical model for discovering the latent “topics” that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.     

Clustering Nominal data with Equivalent Categories

The problem considered in the present paper is how to cluster data of nominal measurement level, where the categories of the variables are equivalent (the variables are replications of each other). One suitable technique to obtain such a clustering is latent class analysis (LCA) with equality restrictions on the conditional probabilities. As an alternative, a less well known technique is introduced: GROUPALS. This is an algorithm for the simultaneous scaling (by multiple correspondence analysis) and clustering of categorical variables. Equality restrictions on the category quantifications were incorporated in the algorithm, to account for equivalent categories. In two simulation studies, the clustering performance was assessed by measuring the recovery of true cluster membership of the individuals. The effect of several systematically varied data features was studied. Restricted LCA obtained good to excellent cluster recovery results. Restricted GROUPALS approximated this optimal performance reasonably well, except when underlying classes were very different in size.

     

产品声音的交互式聚类设计研究

目的 声音是产品和用户之间的一种沟通媒介,为了增进设计师对产品声音的理解、合成与设计匹配,提出一种交互式可视化产品声音数据聚类分析框架。方法 首先通过神经网络将设计师感官描述式信息与声音的特征参数进行融合嵌套;其次基于高斯混合模型来描述非线性几何分布的产品声音数据;最后设计师输入个人先验知识经验参与交互聚类。结果 基于Python的Anaconda3包开发了产品声音交互式聚类的可视化分析实验工具,得到最优化产品声音聚类结果。结论 该产品声音交互聚类可视化分析工具融合了声音技术参数和人脑听觉反应机制,在聚类过程中允许用户参与交互并融入用户的先验知识,并行视图可以实时显示数据元素的流向和判别类别的稳定性。同时,可视化分析可以帮助用户横向比较各聚类结果的异同,样本的比例分布与合理性,以期寻求最优聚类结果。