基于粒矩阵的动态大数据多维关联性分析技术

针对当前动态大数据多维关联性分析过程中数据处理时间过长的问题,提出基于粒矩阵设计动态大数据多维关联性分析技术。采用模糊粗糙集粒化动态大数据,以此为基础,采用粒计算理论构建动态大数据粒矩阵,利用PSO算法优化粒矩阵,通过逻辑约简运算,得到动态大数据关联信息粒,实现基于粒矩阵的动态大数据多维关联性分析。通过实验结果显示,与现有的动态大数据多维关联性分析技术相比较,提出的动态大数据多维关联性分析技术极大地降低了数据处理时间,充分说明提出的动态大数据多维关联性分析技术具备更好的性能。

基于MapReduce框架的分布式软K段主曲线算法

传统的主曲线算法在小规模数据集上能获得良好的效果,但单节点的计算和存储能力都不能满足海量数据主曲线的提取要求,而算法分布式并行化是目前解决该类问题最有效的途径之一。本文提出基于MapReduce框架的分布式软K段主曲线算法(Distributed soft k-segments principal curve,DisSKPC)。首先,基于分布式K-Means算法,采用递归粒化方法对数据集进行粒化,以确定粒的大小并保证粒中数据的关联性。然后调用软K段主曲线算法计算每个粒数据的局部主成分线段,并提出用噪声方差来消除在高密集、高曲率的数据区域可能产生的过拟合线段。最后借助哈密顿路径和贪婪算法连接这些局部主成分线段,形成一条通过数据云中间的最佳曲线。实验结果表明,本文所提出的DisSKPC算法具有良好的可行性和扩展性。

不完备决策表冲突域的属性约简算法

属性约简是粗糙集理论的核心内容之一。目前,从不同的观点已经提出了一些不完备决策表中粗糙集属性约简算法。但是,这些算法相对于完备决策表还是有待进一步考虑。为此,本文借助于完备决策表中冲突域的概念,设计一种在不完备决策表中的新的属性约简方法。该方法通过计算数据粒化后,冲突域中冲突对象的数目为衡量标准来判断某些属性集是否为属性约简。并且,通过理论证明了基于冲突域所求出的属性约简与基于正区域属性约简的是一致的。最后,通过仿真实验说明该算法是可行的。

Fault diagnosis for on-board equipment of train control system based on CNN and PSO-SVM hybrid model

Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.

Geo-Scape,a Granularity Depended Spatialization Tool for Visualizing Multidimensional Data Sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity.The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods.Furthermore,this paper introduces the prototyping tool Geo-Scape,which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity,by making use of a kernel density estimation technique and on the landscape "smoothness" metaphor.A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data,by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.