基于主色提取和主色集扩充的图像检索算法

提出一种基于主色提取和主色集扩充的图像检索算法。算法首先对图像进行主色特征提取,采用提出的改进主色集扩充方法分别对两图像进行主色集扩展,使其维数相同;然后,将经过主色提取得到的主色集从传统RGB颜色空间转换为具有良好视觉一致性HSV颜色空间;最后,采用Quadratic距离进行图像的相似性度量。实验证明算法易于实现、具有较好的时间复杂度及较高的查准率和查全率。     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

多测度Value集相似度量

针对单一测度度量Vague集相似性上的缺陷,提出了两种Vague集的多测度相似度量.给出了一种Vague集的单形体几何表示方法,亦即将Vague集的真、假隶属度和未知度表达为划分单形体同一平面的3个三角形,进而提出了适合于度量Vague集相似性的面积测度,并结合距离测度和未知度测度构造了Vague集多测度相似度量,以在空间上体现出“点-线-面”多特征相似性度量格局.实例验证了多测度相似度量的有效性和优越性.     

同型多机电力系统间扭振相互作用的等效简化研究

提出了一种用于分析并列运行的同型多机系统次同步谐振(SSR)稳定性的等效简化方法。该方法基于相似矩阵具有相同特征值的原理，通过构造合适的变换矩阵，将同型N机系统的系统矩阵变换为具有N个子块的分块对角阵，其中N-1个子块完全相同，从而使N机系统的SSR问题可以简化为具有两类结构的N台单机系统来研究，并给出了这两类单机系统的建模方法。采用该等效法可以有效地降低系统维数，且保留了全部的系统特征值信息。理论推导和算例结果都显示了该等效法的正确性和实用性。     

基于范例库推理的软件成本估算模型研究

用传统的经验函数估算软件成本有很多局限性，采用基于范例库推理的估算方法可以很好地弥补其中的问题。讨论了软件成本估算和基于CBR推理研究的特点，从总体上阐述了COSCBR系统结构，描述了系统重要的研究方面：影响软件成本因素；层次推理；COSCRB系统的范例表示方法；相似度的基本计算算法。     

基于签名的近似垃圾邮件检测算法

针对垃圾邮件短小、一定时间内在网络上重复、大量地散发的特点，提出了基于签名的近似垃圾邮件检测算法（ASD）。该算法以句为基本单位，求取邮件所含的全部句子的摘要，垃圾邮件的近似检测转变为两个摘要集近似度的比较。通过与近似文本查询算法DSC、DSC-SS、I-Match的比较，ASD算法在近似垃圾邮件查询中，表现出样本集的存储空间大小适中、运算时问短、鲁棒性高、高准确率、高召回率的特征。     

汉语问答系统答案提取方法研究

答案提取是问答系统的关键部分，文章介绍了汉语问答系统的基本结构及其实现过程，以问题和答案中关键词的词频统计特性为基础，进一步考虑问题和句子中关键词位置分布信息，提出了一种结合向量空间模型（VSM）和关键词最小匹配距离的问题和句子相似度的计算方法。并以相似度为基础，结合问题类别，对汉语基于事实的简单陈述问题进行了答案句子提取实验，结果表明该方法有较好的效果。     

用户兴趣优化过滤方法研究

为了更准确地描述用户兴趣以及帮助他们得到真正感兴趣的信息，该文提出了一种优化加权用户兴趣进行过滤的方法。基于用户提出的过滤关键词的顺序赋予各关键词不同的权重，利用粗糙集理论对该用户兴趣进行同义优化回归过滤，得到用户所需要的信息。实验表明该方法可提高信息过滤的准确率。     

一类全不连通分形图的构造

为构造一类全不连通的分形图 ,利用了适当的类康托集 ,将某一迭代函数系统升腾为高维空间上的升腾迭代函数系统 ,并通过参数调整构造出大量造型上全不连通的分形图 ,同时发现了类康托集的结构和分形图的结构之间的变化关系 ,实验结果表明 ,该方法简单易用 .     

Discovery of a perceptual distance function for measuring image similarity

For more than a decade, researchers have actively explored the area of image/video analysis and retrieval. Yet one fundamental problem remains largely unsolved: how to measure perceptual similarity between two objects. For this purpose, most researchers employ a Minkowski-type metric. Unfortunately, the Minkowski metric does not reliably find similarities in objects that are obviously alike. Through mining a large set of visual data, our team has discovered a perceptual distance function. We call the discovered function the dynamic partial function (DPF). When we empirically compare DPF to Minkowski-type distance functions in image retrieval and in video shot-transition detection using our image features, DPF performs significantly better. The effectiveness of DPF can be explained by similarity theories in cognitive psychology.