Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions

In this paper, we deal with the problem of classification of interval type-2 fuzzy sets through evaluating their distinguishability. To this end, we exploit a general matching algorithm to compute their similarity measure. The algorithm is based on the aggregation of two core similarity measures applied independently on the upper and lower membership functions of the given pair of interval type-2 fuzzy sets that are to be compared. Based on the proposed matching procedure, we develop an experimental methodology for evaluating the distinguishability of collections of interval type-2 fuzzy sets. Experimental results on evaluating the proposed methodology are carried out in the context of classification by considering interval type-2 fuzzy sets as patterns of suitable classification problem instances. We show that considering only the upper and lower membership functions of interval type-2 fuzzy sets is sufficient to (i) accurately discriminate between them and (ii) judge and quantify their distinguishability.     

利用异或运算和编码约束的降维LDP人脸识别方法

为了有效表示面部特征,在局部方向模式(LDP)的基础上,提出降维局部方向模式(RDLDP)；首先,修改LDP编码模式约束以完成模式的重构,通过对LDP码进行异或运算来计算每个块的单一码；然后,将所得编码图像划分为生成直方图,连接所有区域的直方图块以形成最终描述符;最后,计算特征向量间的卡方相异性度量值,并使用最近邻分类器完成最终的人脸识别;实验采用了3个公开的标准数据库FERET、扩展YALE-B和ORL,提出的改进方法在3个数据集上的最高识别率分别可高达96.97%、96.10%、97.61%,该结果验证了提出方法的有效性。与其他基于局部描述符的先进方法相比,提出方法在准确度和错误识别率等方面更优。     

Spatial analyses of wildlife contact networks

Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark–recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, , was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density is low but hosts move more freely, and at high density is high but transmission is hampered by local build-up of infected or recovered animals.     

一种新的直觉模糊相似矩阵构造方法

基于直觉模糊相似度和相异度,研究了直觉模糊相似矩阵构造问题。给出了直觉模糊相似度和相异度的标准定义,选取了有效的直觉模糊相似度量和相异度量方法;在分析了现有的几种直觉模糊相似矩阵构造方法基础上,提出了一种新的直觉模糊相似矩阵构造方法。通过算例分析,验证了方法的有效性。     

应用区域间差异性和距离约束函数的图像分割

许多水平集图像分割模型需要不断重新初始化水平集函数,或需要图像的梯度信息来约束曲线进化。提出最大化区域间差异性和距离约束函数水平集图像分割模型,该模型引入距离约束函数作为内部能量保证水平集函数始终为符号距离函数（SDF）,避免了进化过程中对水平集函数的不断初始化。基于目标和背景两区域平均灰度值之差的平方构造外部能量函数（区域间差异性函数）,并使其最大化,确保零水平集曲线稳定地收敛于目标边界。实验结果表明,提出的模型不仅有效地克服了传统模型需重新初始化的缺点,并且由于外部能量函数融合了区域信息,对弱边界图像以及含噪声图像具有较好分割能力。     

推进式优化特征权重的K-中心点聚类方法

为获得更贴近于混合属性数据点集空间的相异性度量,从而探测出数据点集的更有意义的聚类分布,提出了一种推进式优化特征权重的K-中心点聚类算法。对该聚类算法进行了必要的讨论,给出其时间复杂度分析及算法收敛性分析。为实现该聚类算法的特征权重优化步骤,给出了二种不同的特征权重优化方法和几个自适应优化距离权重系数、目标函数系数的方法。这些优化方法在一定的理论层次上解决了相异性度量的自适应优化问题。通过几个UCI标准数据集验证了该聚类算法有时能取得更好的聚类质量,从而说明该加权聚类算法具有一定的有效性。给出了几点研究展望,为下一步的研究指明了方向。     

一种融合变异系数的k-mean聚类分析方法

K-means聚类算法的性能依赖于距离度量的选择,k-means算法将欧几里德距离作为最常用的距离度量方法。欧氏距离认为所有属性在聚类中作用是相同的,但是这种距离度量方法并不能准确反映样本间的相异性。针对这种不足,提出了融合变异系数的k-means聚类分析方法（CV-k-means）,利用变异系数权重向量来减少不相关属性的影响。实验结果表明,该方法的聚类结果优于k-means算法。     

A Histogram Matching Approach for assessment of flow regime alteration: application to environmental flow optimization

In this paper we present a Histogram Matching Approach (HMA) for assessment of the flow regime alteration. The HMA uses the degree of histogram dissimilarity as a metric for impact assessment, which is based on the quadratic‐form distance between the frequency vectors of the pre‐ and post‐impact histograms weighted by a specified similarity matrix. The HMA is coupled with an aggregated multiobjective optimization genetic algorithm and applied to a case study on the Kaoping diversion weir (Taiwan) for determining the optimal environmental flow scheme that balances the ecosystem and human needs objectives. Two key issues are addressed in this study. First, we compare the performances of the HMA and existing Range of Variability Approach (RVA). Second, we employ three types of similarity function to investigate their effect on the outcomes of the HMA. The results reveal that the HMA consistently outperforms the RVA in preserving the natural flow variability regardless of what type of similarity function is used. No single type of similarity function can be found that would simultaneously best preserve the natural patterns of 32 Indicators of Hydrologic Alteration (IHA). For the situations where the water‐supply reliability is of critical concern, the pulse similarity is recommended because it would assure the smallest water‐supply deficit. If, however, minor degradation in the water‐supply reliability may be overlooked, the linear similarity is suggested because it would generally result in the post‐impact flows that most satisfactorily resemble to the natural flow regime. Copyright © 2008 John Wiley & Sons, Ltd.     

Positive and negative opinion modeling: The influence of another's similarity and dissimilarity.

Modeling research that has focused on the effects of observing similar others appears to have underestimated the influence of observing dissimilar others. Two experiments demonstrated that observing a model express liking for a piece of music induced more favorable opinions of the music (positive modeling) when the model was similar to the participant observer in relevant opinions, and more negative opinions (negative modeling) when the model was dissimilar to the participant in relevant opinions. Of note, this pattern was more pronounced when participants also believed their general backgrounds were dissimilar rather than similar to that of the model. Underlying social comparison processes and the mediational role of participants' liking of the model are considered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)