小波和主分量分析方法研究思维脑电

研究自发脑电和思维活动的关系．利用小波和主分量分析结合的WPCA算法对不同思维任务记录的六导脑电进行处理,并对思维特征的频谱能量和变化率等多指标进行综合分析和计算。结果表明WPCA算法不仅可以实现噪声的去除,而且能提高主分量的贡献率,降低输入矢量的维数。对脑电主分量的分析揭示了脑电与思维个体、思维种类、复杂度以及注意力的联系,思维任务的神经网络分类结果验证了WPCA方法研究脑电和思维的有效性,为进一步理解认知和思维过程,实现对思维的定位和分类提供了依据。     

PRINCIPAL COMPONENT ANALYSIS OF TIME-INTENSITY BITTERNESS CURVES1

Perceived bitterness in drinks is known to fade slowly over time. Time-Intensity curves are a means of studying this fading process. K subjects record the perceived bitterness intensity by moving a slider on a monitor using a mouse. Usually average TI-curves are calculated to give a representation of the TI-curve for one particular object. The problem is that there often are large individual differences, so the average TI-curve is not always a good representation. An alternative is to perform a Principal Component Analysis on the matrices of objects by individual TI-curves for each object. The resulting, so-called, Principal Time Intensity Curves (PTIC's) are better representations than the average curves. Often the PTIC's for the different products are hard to distinguish. In this case a noncentered PCA of the matrix with curves gives results which show more differences between the products.     

中国人群肤纹的主成分分析

近年来 ,我国学者多将中国人群分为南、北两大类群。作者对我国 5 9个人群的肤纹进行主成分分析。结果表明 ,根据肤纹特征 ,可将中国人分为 6个族群 ,除北方族群、南方族群外 ,存在着新疆族群与西藏族群。新疆族群与北方族群相比 ,斗型率低 ,尺箕、桡箕率高 ,小鱼际区与指间区Ⅱ、Ⅲ真实花纹率高。西藏族群斗型率高、尺箕率低。北方族群与南方族群相比 ,指纹嵴线总数、小鱼际区与指间区Ⅲ真实花纹率高。此外 ,还存在南北族群间的过渡族群与南北混合族群。 8个汉族人群未能聚集 ,分散于各族群中。这表明 ,汉族与少数民族间、南北方民族间存在着肤纹基因的交流。将中华民族分为南北两大类群的观点可能过于简单化了。     

EVALUATION OF WINE QUALITY USING A SMALL-PANEL HEDONIC SCALING METHOD

A hedonic scoring method for evaluating wines with a small panel was examined for reliability, effects of training level and agreement with a traditional 20-point scoring technique. The method was found to differentiate among qualities of 14 domestic Sauvignon Blanc wines with good reliability. Four panels were tested, three with a high degree of experience in wine judging and a fourth consisting of fine wine consumers with no special training. The three experienced panels agreed well about the wines' quality scores. Mean panel scores were less highly correlated (although still positive) with the untrained panel, which showed higher variability and lower reliability. Mean scores from the hedonic method were correlated at r =+0.94 with mean scores from a 20-point quality judging procedure. The small panel hedonic method is suitable for generating quality scores for consumer guidance in large scale wine surveys, as are commonly found in popular wine periodicals.     

高维混沌降维的投影追踪主分量分析法

一些生理信号,例如脑电是源自于高维混沌系统,因此低维混沌理论和方法不适用于分析这类高维混沌。采用投影追踪主分量分析法（Princiopal Component Analysis based on Projection Pursuit,PP PCA）对高维Lorenz模型系统进行了降维的研究。在用上述方法成功地对线性和非线性噪声－周期模型分别进行了PP PCA分析的基础上,对Lorenz高维混沌系统进行了PPPCA降维的研究。结果表明,正确选用非线性的投影追踪主分量分析法,可以通过简化原系统达到降维的目的,并能保留研究所关心的原系统的主要动态特性。同时也阐明了方法的稳定性和将该方法应用于高维脑电降维的可行性。     

主成分分析法在壳斗科植物化石鉴定中的应用*

壳斗科化石是北半球新生代地层的优势分子,然而由于其属种众多,叶片属种间特异性不明显,该科叶片化石的鉴定是古植物学研究的难点之一。面对化石标本不完整的性状特征信息,如何剔除冗余特征,重点考虑具有鉴定意义的标本特征,就显得非常必要。本研究通过调研多种降维算法,考虑到植物特征编码及赋值的数值特性,选用主成分分析法,以壳斗科青冈亚属植物叶片为例,对可能在化石叶片上观察到的22个性状特征(变量)进行降维处理,挑选出对于壳斗科化石叶片分类鉴定起主要作用的10个性状特征,并将其应用于化石鉴定进行验证。结果表明,仅考虑经过主成分分析法压缩挑选的10个主要性状特征,仍然能够实现壳斗科化石植物的准确鉴定。主成分分析法应用于壳斗科性状特征的降维处理效果良好,剔除冗余特征对标本鉴定结果没有影响。     

布氏田鼠种群生长指标的主分量分析

本文对布氏田鼠的体重、体长、尾长、颅全长、颅基长等１５项生长指标进行了主分量分析。结果表明：颅基长是最具有代表性的生长指标。同时,头骨的上齿隙宽因不存在性别差异,容易准确量表,可用作划分年龄组指标。根据上齿隙宽度,本文将布氏田鼠划分为５个年龄组。     

泡桐无性系苗期叶部性状的主成分分析

经对１５个泡桐无性系苗期叶部性状的主成分分析表明：叶大小和叶形状是泡桐无性系苗期叶部性状差异的主要因素,作为主成分可代表叶部信息量的８７％。因而在苗期选择时可作为主要参考因子。     

我国23个群体体质的聚类分析与主成分分析

作者选取13项指标（头长、头宽、额最小宽、面宽、形态面高、鼻宽、鼻高、口裂宽、两眼内宽、身高、坐高、肩宽、骨盆宽）对我国23个群体体质特征进行了聚类分析与主成分分析。研究结果显示：23个群体可分为南方组、北方组（含两个小组）和中间类群组。南方组与北方组的体质区别主要为体部的长度、宽度的差异。北方组各群体之间头面部指标值差异较大,南方组各群体之间头面部指标值则较为接近。     

主成分分析法在中药鉴别中的应用

本文以植物药百合、动物药海马、中成药二妙丸为研究对象．取百合的23个样品,海马的20个样品,二妙丸与加味药的17个样品进行紫外色谱或光谱分析,对所得的数量化指标矩阵进行主成分分析,压缩出两个用以区分样品间异同的综合性指标──二元主成分,在二元主成分平面图上实现了对动植物药、中成药的鉴别与分类．     

不同类型湖泊夏季浮游生物多元聚类分析

为深入了解湖泊环境生态系统的变化规律,为湖泊渔业和湖泊管理提供基础数据,于2007年夏季测定了武汉市梁子湖、斧头湖、柴泊湖、南湖和野芷湖5个养殖湖泊的浮游生物的数量和生物量。利用SPSS软件,对5个湖泊浮游植物6个门和浮游动物4个类的数量和生物量进行了主成分分析和因子分析。主成分分析和因子分析的结果是不相同的,其原因是两种方法的差异所致。两种方法结合使用,将有效地提高分析结果的准确度。分析结果表明,影响5个湖浮游生物的第1至第6个主成分分别为枝角类桡足类数量和重量因子、轮虫数量重量和绿藻门数量因子、隐藻门数量和重量因子、原生动物数量和重量因子、蓝藻门数量因子、硅藻门数量和绿藻门重量因子。使用SPSS软件,对5个湖泊13个点的浮游生物的数量和生物量予以了层次聚类法,并将5个湖泊的13个点分为5类,即梁子湖和斧头湖敞水区的3号和5号点为一类;梁子湖和斧头湖围拦养殖区的1号和4号点,以及梁子岛附近的2号点为一类;柴泊湖的6、7、8号3个点为一类;南湖原排污口附近的10号点为一类;南湖与野芷湖的其他9、11、12和13号4个点为一类。经分析认为,形成这个结果是由城市废水、生活污水和养殖生产所致。       

玉米自交系间杂交种F1农艺性状的主成分分析

用Hótlling倡导的主成分分析法,(Principal comporient analysis),分析了玉米32个自交系间80个杂交组合的F_1世代.分析性状有株高、穗位高、雄穗分枝数、单株有效穗数、穗长、秃尖长、穗行数、行粒数、穗重、百粒重和单株产量.结果表明,前4个主成分贡献率占了总变异的大部分,因此就前4个主成分得分值对80个杂交组合进行了选择,为玉米组合的选择提供了直接依据.     

芦苇生态型划分指标的主分量及模糊聚类分析

运用主分量分析及模糊聚类的方法首次对包括群落、形态,解剖,生理生化及遗传代谢等４５个指标的不同生态芦苇进行了分析,结果表明,生物量、单株干重、株高,可溶性蛋白质含量、ＲＮＡ含量、ＤＮａａｓｅ和ＲＮａｓｅ活性、Ｋｒａｎｚ结构的有无及土壤ｐＨ值等差异最为显著。４种生态型芦苇中,盐化草甸芦苇与盐化草甸－沙丘过渡带芦苇最相近,其次为沙丘芦苇,沼泽芦苇与其它３种生态相似程度最低。     

ILLUSTRATIVE EXAMPLES OF PRINCIPAL COMPONENTS ANALYSIS

In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. These examples are believed useful for aiding researchers in the interpretation of data and they highlight some of the properties and limitations of principal components analyses.     

TASTE GROUP THRESHOLDS AND SENSORY EVALUATION OF SPANISH WINE VINEGARS

Wine vinegar is a product obtained from wine acidification which contains at least 5% by wt. of acetic acid, in general without any additives or colorings.
Aspects studied in this work include: the determination of the taste group thresholds (geometric mean of the individual best-estimate thresholds "BET") of two different acids (citric and acetic acids) in aqueous solution and spanish vinegars produced from table and sherry wines. The results obtained suggest that wine vinegar can be considered something more than just an acidulant agent.
In order to evaluate differences among wine vinegars, discriminant tests for twenty-five spanish vinegars (sherry, table and flavored vinegars) were applied. Six of the twelve attributes freely chosen by assessors allowed grouping of the spanish wine vinegars according to their sensory aspects.     

PHYLOGENETIC ANALYSIS OF PHENOTYPIC COVARIANCE STRUCTURE. I. CONTRASTING RESULTS FROM MATRIX CORRELATION AND COMMON PRINCIPAL COMPONENT ANALYSES

Applications of quantitative techniques to understanding macroevolutionary patterns typically assume that genetic variances and covariances remain constant. That assumption is tested among 28 populations of the Phyllotis darwini species group (leaf-eared mice). Phenotypic covariances are used as a surrogate for genetic covariances to allow much greater phylogenetic sampling. Two new approaches are applied that extend the comparative method to multivariate data. The efficacy of these techniques are compared, and their sensitivity to sampling error examined. Pairwise matrix correlations of correlation matrices are consistently very high (> 0.90) and show no significant association between matrix similarity and phylogenetic relatedness. Hierarchical decomposition of common principal component (CPC) analyses applied to each clade in the phylogeny rejects the hypothesis that common principal component structure is shared in clades more inclusive than subspecies. Most subspecies also lack a common covariance structure as described by the CPC model. The hypothesis of constant covariances must be rejected, but the magnitudes of divergence in covariance structure appear to be small. Matrix correlations are very sensitive to sampling error, while CPC is not. CPC is a powerful statistical tool that allows detailed testing of underlying patterns of covariation.