基于改进无参数K-means算法的刀具状态分析

针对K-means算法需要人为确定聚类个数和随机选取初始聚类中心导致结果陷入局部最优的问题,结合基于密度峰值的聚类算法CFSFDP(Clustering by Fast Search and Find of Density Peaks),提出一种改进的无参数K-means算法。首先,计算样本点的局部密度和离散度。然后,建立决策图,将两个参数组成向量,计算每个点到周围5个点的距离,筛选出距离大于2倍均方差且密度大于平均密度的点作为算法的初始聚类中心,统计聚类中心个数k作为聚类个数,将初始聚类个数k以及初始聚类中心作为K-means算法的初始参数对数据进行聚类。最后,对UCI(University of California, Irvine)数据集、人工建立的高斯数据集以及真实刀具振动数据集3种不同类型的数据集进行聚类。结果表明,所提算法保持传统算法全局最优性,并验证了提出算法的有效性。由于K-means是一种无监督聚类方法,在获得较优刀具状态识别结果的同时,可减少人工数据标定、有监督训练等工作量及运算成本,这对于准确实时提取数控机床刀具运行状态具有较高的实际意义。     

自适应的k-means聚类算法SA-K-means

传统的k-means聚类算法对初始聚类中心非常敏感,聚类的结果也常常随着初始聚类中心而波动。为了降低聚类算法的这种敏感性,本文提出了一种自适应的聚类算法（SA—K—means）,该方法通过计算数据对象区域的密度,选择相互距离最远的高密度区域的中心作为初始聚类中心。实验表明SA—K—means聚类算法能有效地消除聚类算法对初始聚类中心的敏感性,得到满意的聚类结果。     

PCA-KDKM算法及其在微博舆情中的应用

针对K-means算法因随机选取聚类中心而易造成聚类结果不稳定的问题,提出PCA-KDKM算法。该算法使用主成分分析法对数据集的属性降维,提取主属性;利用k′dist曲线自动获取k值;计算平缓曲线上所含数据对象的均值并选取其中一值,作为首个初始聚类中心;利用基于密度和最大最小距离的算法思想进行聚类;结合类间距离和类内聚类提出聚类质量评价函数。将该算法与K-means、KNE-KM、QMC-KM、CFSFDP-KM在UCI数据集上进行聚类比较,结果表明该算法聚类结果稳定,聚类准确率高。将PCA-KDKM算法应用在微博舆情分析中,抓取不同类别的数万条数据进行聚类分析。实验结果表明,PCA-KDKM算法在微博舆情分析中有更高的准确性和稳定性,有利于及时发现热点舆情。     

K-means聚类算法初始中心选择研究

传统K-means聚类算法中聚类初始中心点是随机确定的,实际聚类数据集中可能有孤立点,造成了每次聚类的结果不同,聚类质量不同,有时陷入局部优化状态.针对这些问题,研究者曾试图用距离法解决孤立点的判断和确定初始聚类中心.这种思路存在不科学性.因为孤立点不仅指远离其他点,同时它的周围点稀疏;另外,当数据量过大、数据特征值过多时,算法的运算量大,需要占用大量的计算机资源,运算速度过慢.对传统的K-means聚类算法进行研究,提出了基于密度参数和距离理论的初始聚类中心的确定和孤立点的判断,对传统的K-means聚类算法进行改进.     

基于小波包和聚类算法的滚动轴承故障检测研究

针对Kmeans算法在滚轴故障检测中k值需要人工设定以及初始聚类中心的随机选取问题,提出I-Canopy-Kmeans算法的故障检测方法对其进行优化。该算法在初始聚类中心的随机选取方面,使用“最远最近”的原则,即在获取n个Canopy时,任意两个Canopy中心点之间的距离应该尽可能远,且第n个Canopy中心点应该是其他数据点与前面n-1个中心点最远距离中最小的一个;在阈值选取方面,使用欧氏距离求出所有数据点的均值点,再计算均值点到所有数据点的距离,并用L₁和L₂分别表示最远距离和最近距离,然后将(L₁+L₂)/2赋值给阈值T₁、(L₁+L₂)/3赋值给阈值T₂。实验结果表明,与传统Kmeans算法相比,I-Canopy-Kmeans算法的各项评价指标均有提高,其中I_AR提高最多,达到了40.01%。     

基于拟蒙特卡洛的K均值聚类中心初始化方法

针对传统K-means算法随机选择初始聚类中心容易造成聚类结果不稳定且准确率低等问题,基于拟蒙特卡洛(Quasi-Monte Carlo,QMC)方法提出一种新的初始聚类中心确定方法;该算法利用QMC序列分布的超均匀性特点,对整个样本空间中的样本分布进行采样估计;基于k近邻距离(k-distance)对QMC序列点进行加权的K-means聚类,得到初始聚类中心。该算法的计算复杂度为O(max(d、n)logn),其中d、n分别表示样本数据的维数和数量;在人工数据和实际数据集上的仿真实验表明,该算法能选择更优的初始聚类中心,有效降低K-means算法的迭代次数,提高聚类的准确性、鲁棒性和收敛速度。     

快速特征映射优化的流形密度峰聚类

经典的密度峰聚类不再适用于复杂的流形聚类,因此提出了快速特征映射优化的流形密度峰聚类,用快速特征映射优化的流形距离取代欧式距离,可以更好地反映不同类的点间相似性.算法首先通过寻找特征点,构造无向特征图,再通过无向特征图计算任意两个点之间的流形距离,最后按照流形距离的大小完成分配.在人工数据集和UCI数据集上的实验表明,新算法具有更高的准确率.     

基于密度的改进聚类算法比较

针对传统K-means算法的聚类结果依赖初始聚类中心的缺陷,提出了一种基于密度的改进K-means聚类算法,该算法选择位于数据集样本密集区且相距较远的数据对象作为初始聚类中心,实现K-means聚类。针对PAM算法时间复杂度高,且不利于大数据集处理的缺陷,提出了一种基于密度的改进K-medoids聚类算法,在选取初始中心点时根据数据集样本的分布特征选取,使得初始中心点位于不同类簇。UCI机器学习数据库数据集和随机生成的带有噪音点的人工模拟数据集的实验测试证明,基于密度的改进K-means算法和基于密度的改进Kmedoids算法都具有很好的聚类效果,运行时间短,收敛速度快,有抗噪性能。     

基于反向K近邻和密度峰值初始化的加权Kmeans聚类入侵检测算法

传统Kmeans聚类算法的性能易受初始类簇中心随机性和类簇中心计算的迭代过程中边缘点和离群点反复计入的影响，为了避免这些影响，该文提出一种基于反向K近邻和密度峰值初始化的加权Kmeans聚类算法。通过样本的近邻信息计算每个样本的反向K近邻，针对不同规模、不同密度分布数据集，可以自适应地搜索密度峰值点作为初始类簇中心；自适应设定相对簇半径，并通过样本加权进行类簇中心迭代，在不同数据分布下可以有效降低边缘点和离群点对聚类结果的影响。试验结果证明，该算法在聚类性能提升的同时迭代次数大幅降低，随着入侵行为类型和数据规模的增加，该文聚类算法仍体现出较好的性能，且在发现未知攻击类型上效果显著。     

一种优化初始聚类中心的k-means算法

随机选择初始聚类中心的k-means算法易使聚类陷入局部最优解、聚类结果不稳定且受孤立点影响大等问题.针对这些问题,提出了一种优化初始聚类中心的方法及孤立点排除法.该算法首先选择距离最远的两点加入初始化中心,再根据这两点将原始簇分成两个聚簇,在这两个簇中挑选方差较大的簇按照一定的规则进行分裂直至找到k个中心,初始中心的选择过程中用到孤立点排除法.在UCI数据集及人造含一定比例的噪音数据集下,通过实验比较了改进算法与其他算法的优劣.实验表明,改进后的算法不仅受孤立点的影响小、稳定性好而且准确度也高.     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Lower Paleozoic oil relationships within Williston Basin, Canada

The Williston Basin is a significant petroleum province, containing oil production zones that include the Middle Cambrian to Lower Ordovician, Upper Ordovician, Middle Devonian, Upper Devonian and Mississippian and within the Jurassic and Cretaceous. The oils of the Williston Basin exhibit a wide range of geochemical characteristics defined as "oil families", although the geochemical signature of the Cambrian Deadwood Formation and Lower Ordovician Winnipeg reservoired oils does not match any "oil family". Despite their close stratigraphic proximity, it is evident that the oils of the Lower Palaeozoic within the Williston Basin are distinct. This suggests the presence of a new "oil family" within the Williston Basin. Diagnostic geochemical signatures occur in the gasoline range chromatograms, within saturate fraction gas chromatograms and biomarker fingerprints. However, some of the established criteria and cross-plots that are currently used to segregate oils into distinct genetic families within the basin do not always meet with success, particularly when applied to the Lower Palaeozoic oils of the Deadwood and Winnipeg Formation.     

An Analysis of Catherine＇s Character——Wuthering Heights

Wuthering Heights, Emily Bronte＇s only novel, was published in December of 1847 under the pseudonym Ellis Bell. The book did not gain immediate success, but it is now thought one of the finest novels in the English language. Catherine is the key character of this masterpiece, because everybody and everything center on her though she had a short life. We can understand this masterpiece better if we know Catherine well.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

Design and analysis of generic LBS application developing platform

Location based services is promising due to its novel working style and contents.A software platform is proposed to provide application programs of typical location based services and support new applications developing efficiently. The analysis shows that this scheme is easy implemented, low cost and adapt to all kinds of mobile nework system.     

橡胶颗粒沥青混合料低温抗裂性试验研究

以AC-13级配为基础,将橡胶颗粒代替部分集料掺入混合料中,以低温弯曲试验为评价方法对不同橡胶颗粒掺量下沥青混合料的低温抗裂性进行研究,并引入应变能密度值对混合料的低温抗裂性进行综合评价.试验结果表明:橡胶颗粒沥青混合料试件的破坏微应变均超过2 300,满足冬寒区的技术指标;无论是否掺加橡胶颗粒,随着温度的下降,沥青混合料破坏时的最大弯拉强度增大,弯拉应变降低,劲度模量增大;弯曲应变能密度在胶粒掺量为1%左右时具有较大的弯曲应变能密度值,此时橡胶颗粒沥青混合料具有较好的低温抗裂性.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

Quantitative trait loci （QTLs） for quality traits related to protein and starch in wheat

Quality traits in wheat （Triticum aestirum L.） were studied by quantitative trait locus （QTL） analysis in a recombinant inbred line （RIL） population, a set of 131 lines derived from Chuan 35050 × Shannong 483 cross （ChSh）. Grains from RILs were assayed for 21 quality traits related to protein and starch. A total of 35 putative QTLs for 19 traits with a single QTL explaining 7.99-40.52% of phenotypic variations were detected on 10 chromosomes, 1D, 2A, 2D, 3B, 3D, 5A, 6A, 6B, 6D, and 7B. The additive effects of 30 QTLs were positive, contributed by Chuan 35050, the remaining 5 QTLs were negative with the additive effect contributed by Shannong 483. For protein traits, 15 QTLs were obtained and most of them were located on chromosomes 1 D, 3B and 6D, while 20 QTLs for starch traits were detected and most of them were located on chromosomes 3D, 6B and 7B. Only 7 QTLs for protein and starch traits were co-located in three regions on chromosomes 1D, 2A and 2D. These protein and starch trait QTLs showed a distinct distribution pattern in certain regions and chromosomes. Twenty-two QTLs were clustered in 6 regions of 5 chromosomes. Two QTL clusters for protein traits were located on chromosomes 1D and 3B, respectively, three clusters for starch traits on chromosomes 3D, 6B and 7B, and one cluster including protein and starch traits on chromosome 1D.