A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as "bag of concepts" that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.     

SVM方法在滚动轴承故障诊断中的应用

SVM方法是在统计学习理论的基础上发展起来的一种新的机器学习算法,它是统计学习理论的具体应用．讨论了SVM方法在故障诊断领域中应用的分类算法,并以滚动轴承为例进行了试验论证．     

An information retrieval model based on vector space method by supervised learning

This paper proposes a method to improve retrieval performance of the vector space model (VSM) in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition (SVD) and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection. In order to train the model, multiple partitions are created for each collection. Improvement of average precision of the averages over all partitions, compared with the latent semantic indexing (LSI) model, are 20.57% (Medline) and 22.23% (Cranfield) for the two training data sets, and 0.47% (Medline) and 4.78% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve user-supplied relevance information for the long term in the system in order to use it later.     

改进遗传算法在文本聚类中的应用研究

分析了K均值聚类算法（K-means）存在的不足和改进遗传算法的全局优化能力,提出一种基于改进遗传算法的文本聚类方法,该方法将原始文档转化成用向量空间模型来描述的文本向量,首先随机产生若干个文档向量作为初始聚类中心形成遗传算法的染色体种群,经过改进遗传算法的选择、交叉、变异进化运算,得到较为优化的K均值聚类算法的初始聚类中心。实验表明该算法文本聚类提高了查准率和查全率,算法的高效性也得到了验证。     

基于RBF和PSO的双喉道气动矢量喷管优化设计

本文探索了一种能多变量综合优化的方法,即对喷管进行参数化设计后,用均匀试验设计(UED)将试验样本均匀散布在设计区间内,求出各性能参数后,利用径向基神经网络(RBF)对试验样本进行拟合,再用粒子群算法(PSO)对训练好的神经网络进行寻优,找出了更好的双喉道气动矢量喷管设计参数组合。数值模拟结果显示,优化后的双喉道气动矢量喷管的矢量角有了明显提高。试验表明这种优化方法具有很好的优化能力,可以用来对喷管几何外形进行参数优化。      

硫酸溶液中环丙沙星对低碳钢的缓蚀作用研究

采用失重法研究了环丙沙星在0.5 mol/L H2SO4溶液中对低碳钢的缓蚀性能以及温度对缓蚀效果的影响,通过量子化学计算和动电位极化曲线测试研究了缓蚀机理.结果表明:环丙沙星具有较好的缓蚀性能,在有效浓度为4mmol/L时对低碳钢的缓蚀效率可达到91.19%;它是以阳极抑制为主的混合抑制型缓蚀剂,通过苯环及O、N、F三种杂原子在碳钢表面形成的单分子层吸附达到缓蚀目的,并且随着温度的升高缓蚀效率降低.     

交流电路的一种简便分析方法

交流电路中电流与电压的关系在有效值上满足欧姆定律,根据负载的性质不同,相位上存在一个超前与滞后的问题,最简单最精确的分析方法就是矢量分析法,这种方法不仅适用于简单的串并联电路,也适用于交流电的所有电路,所以笔者在这些电路中提取相同的矢量分析法.     

点到直线的距离公式的推证

点到直线的距离公式是解析几何中的一个基本公式，本文从不同角度对这一公式给出了几种推证。     

广义向量F-互补问题

本文研究了一类广义向量F-互补问题(GVF-CP),并给出了它与F-广义似向量变分不等式(GVF-VLIP)之间的关系.且在不具有伪单调的情况下证明了(GVF-CP)解的存在性,推广了已有的一些结果.     

Applying two channels to vector space secret sharing based multi-signature scheme

Secret sharing and digital signature is an important research area in information security and has wide applications in such fields as safeguarding and legal use of confidential information, secure multiparty computation and electronic commerce. But up to now, study of signature based on general vector space secret sharing is very weak. Aiming at this drawback, the authors did some research on vector space secret sharing against cheaters, and proposed an efficient but secure vector space se…