Topological Characterization of Book Graph and Stacked Book Graph

Degree based topological indices are being widely used in computer-aided modeling, structural activity relations, and drug designing to predict the underlying topological properties of networks and graphs. In this work, we compute the certain important degree based topological indices like Randic index, sum connectivity index, ABC index, ABC₄ index, GA index and GA₅ index of Book graph B_n and Stacked book graph B_m,n. The results are analyzed by using edge partition, and the general formulas are derived for the above-mentioned families of graphs.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

含齿形裂隙类岩石材料单轴压缩试验研究

为研究含齿形裂隙岩石在单轴压缩下的破坏特征及强度特性，制作了含不同裂隙倾角和起伏角的齿形裂隙类岩石材料试件，并采用岩石力学伺服试验机进行单轴压缩试验。试验结果表明：（1）试件主要产生拉伸、剪切和拉剪复合裂纹，且根据裂纹的扩展路径可划分为A型（拉伸破坏）、B型（剪切破坏）、C型（复合破坏）3种破坏模式，裂隙倾角对试件最终破坏模式影响显著；（2）当裂隙倾角较小时，试件应力—应变曲线为多峰曲线，随着裂隙倾角的增大，曲线呈单峰形式，表现为延性减弱，脆性增强，而裂隙倾角相同但起伏角不同的试件应力—应变曲线大致相同；（3）当裂隙起伏角相同时，试件当量峰值强度随裂隙倾角的增大呈先减小后增大的规律，且裂隙起伏角对试件当量峰值强度的影响小于裂隙倾角。     

机载光电雷达它机试飞技术研究

机载光电雷达受平台及大气环境影响较大,而它机试飞能有效规避研制风险,是产品研制的必经阶段。本文介绍了机载光电雷达它机试飞试验系统组成、试验流程和数据处理等内容。经工程验证,实施过程满足任务需求,对其他光电系统的它机及本机试飞具有参考价值。     

Prediction methods and applications in the science of science: A survey

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.     

时态神经网络模型及其在股票分类预测上应用

对具有时间属性的数据进行数据挖掘称为时态数据挖掘，用以发现数据在时间上的知识，当数据变化不规律时，如股票交易数据,就很难发现有价值的规律与规则。而神经网络具有并行、容错、可以硬件实现以及自我学习的优点,可作为股票分类预测应用的一种方法。通过将股票数据与时态型相结合，将股票数据转换成时态型股票数据，提出时态神经网络模型的分类方法，对收集的若干上市公司十年内的股票数据进行分析，构建了时态股票数据神经网络分类器对股票进行分类预测。经过实验验证，相比改进前的神经网络和支持向量机方法，该分类器具有更高的分类准确率。结果证明，这种时态数据神经网络模型对于多只股票的分类预测是非常有效的，可以很好地运用到股票市场的分类预测中。     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

面向电信承载网的监控系统设计与实现

面对电信承载网连接的日益增长的海量终端设备，运营商需要结合网络拓扑对终端设备产生的数据进行高效的汇聚统计、异常分析、故障定位处理等操作。针对已有系统存在的操作困难、分析效率低等问题，设计与实现了一个面向电信承载网的高效监控系统，提供实时与离线数据分析和多维可视化分析的能力。对网管、认证、终端等系统及设备采集的数据进行结构化存储，对采集的数据进行拓扑相关性和时间序列方法分析，根据分析结果实现基于动态阈值控制的异常实时告警、定位等操作，并提供多维度可视化分析对网络状态进行实时监控。实际应用结果表明，该系统性能优异，具有良好交互性，能较好地满足承载网运维人员业务分析需求。     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.