Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels

Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behaviour of the programme. But the challenges in creating suitable oracles limit the ability to perform automated testing in some programmes, and especially in scientific software. Metamorphic testing is a method for automating the testing process for programmes without test oracles. This technique operates by checking whether the programme behaves according to properties called metamorphic relations. A metamorphic relation describes the change in output when the input is changed in a prescribed way. Unfortunately, finding the metamorphic relations satisfied by a programme or function remains a labour‐intensive task, which is generally performed by a domain expert or a programmer. In this work, we propose a machine learning approach for predicting metamorphic relations that uses a graph‐based representation of a programme to represent control flow and data dependency information. In earlier work, we found that simple features derived from such graphs provide good performance. An analysis of the features used in this earlier work led us to explore the effectiveness of several representations of those graphs using the machine learning framework of graph kernels, which provide various ways of measuring similarity between graphs. Our results show that a graph kernel that evaluates the contribution of all paths in the graph has the best accuracy and that control flow information is more useful than data dependency information. The data used in this study are available for download at http://www.cs.colostate.edu/saxs/MRpred/functions.tar.gz to help researchers in further development of metamorphic relation prediction methods. Copyright © 2015 John Wiley & Sons, Ltd.     

基于紧密中心性的无线mesh骨干网网关部署

将定向天线和Delaunay图应用于无线mesh骨干网络的网关部署,提出了基于紧密中心性的无线mesh骨干网络网关部署算法。根据已知的mesh路由器和网关的最大流通量对网络进行划分,形成Delaunay子图,在划分的子图中根据欧几里德距离找出距离中心点最近的3个节点,形成候选网关集,在候选网关集中选择到其他节点总路径最短的节点作为网关的部署位置,将每个子图的网关位置输出。仿真结果表明,根据网关最大流通量进行合理网络划分后,算法能最小化网络的网关数量,由mesh路由器到网关的总路径长度优于随机算法。     

电信大规模社交关系网络图数据挖掘研究

电信技术的发展使得通话网络几乎覆盖了整个社会,对电信运营商来说,这个庞大的社会网络蕴藏着巨大的商机.尤其在日益激烈的市场竞争中,客户关系的维护、客户潜在价值的挖掘以及有针对性地对客户进行营销等对电信图数据的挖掘,受到了强烈关注.通过对比,研究了当前针对类似电信网络的大规模社交关系网络图数据进行挖掘的分布式和单机图计算工具和框架,并对单机图计算工具Graphchi的单机处理数据能力进行了可行性和可用性测试,同时采用单机Graphchi对电信社交关系网络的数据进行了图挖掘研究和应用.     

基于无噪后验错误矢量信号能量的变正则因子仿射投影算法

变正则因子技术是提高仿射投影自适应算法性能的重要方法之一.由于环境噪声的影响,现有的变正则因子自适应算法收敛速度较慢且稳态误差较大,各种测量、评估误差的存在进一步恶化了算法性能.为提高自适应算法的跟踪性能,本文在分析无噪先验错误矢量、无噪后验错误矢量和额外均方错误间关系的基础上,提出通过最小化无噪后验错误矢量信号能量来推导自适应变正则因子表达式的方法.在实践应用中,该方法利用了测量噪声的统计方差特性,并提出一种更加光滑且更加容易控制的指数缩放因子评估方法.系统辨识的仿真结果表明本文方法与传统的变正则因子方法以及变步长方法相比有更快的收敛速度与更低的稳态误差.     

基于视频多目标跟踪的高度测量算法

本文提出了一种基于视频多目标跟踪的高度测量算法.首先,采用码本模型检测前景,利用图割理论实现对多目标的跟踪.然后,提取每一帧中目标的头部特征点和脚部特征点,根据投影几何的约束关系计算每个目标的高度.最后,融合多帧的测量结果进行数据优化.本文提出的算法不需要对相机进行完全标定,只需计算摄像机的灭点和地平面的灭线,降低了计算的复杂度.实验结果表明,本文提出的算法具有较高的测量精度,对遮挡和运动状态变化具有较强的鲁棒性,同时,能够满足实时性要求.     

三甘醇脱水填料塔计算方法适用性分析

塔径计算是进行三甘醇填料脱水塔设计的首要工作,为了选用准确适用的塔径计算方法,详细介绍了多种塔径计算方法。以两个实际项目的数据为计算案例,分别采用贝恩-霍根(Bain-Haugen)关联式、通用压降关联图法(GPDC)、塔负荷系数法、Aspen HYSYS软件等不同方法计算三甘醇填料脱水塔的塔径,并进行了计算结果偏差分析和计算方法适用性分析。结果分析表明,塔负荷系数法、通用压降关联图法(GPDC)、Aspen HYSYS软件计算结果相近,经圆整后的塔径一致。贝恩-霍根(Bain-Haugen)关联式算出的结果偏差较大,过于保守,分别为11.11%和8.73%。由于通用压降关联图法(GPDC)的计算结果受GPDC关联图内数据点影响准确度不易保证,建议选用塔负荷系数法和Aspen HYSYS软件进行三甘醇脱水填料塔塔径的计算。      

脉冲风洞短时测力信号的延拓与动态载荷辨识方法

研究了一种可快速辨识脉冲风洞中试验模型所受动态载荷的方法。给出了载荷、系统单位脉冲响应函数、天平输出三者的时域离散化模型,指出后者是前两者线性卷积的截断;将天平短时振荡输出延拓为完整的线性卷积结果,对前两者补零延拓,将上述模型转化为圆周卷积形式,进而通过离散傅里叶变换得到三者的频域离散化模型,求解频域模型得到载荷。通过单自由度系统简单载荷、多自由度系统复杂载荷两个数值模拟算例验证了该方法仅需对测量信号低通滤波即可准确辨识出载荷;经风洞试验,验证了方法的准确性和实用性。     

基于变分正则化的低质视频图像二维增强仿真

受环境、硬件设备及成本等因素限制,所生产的视频质量低,存在大量冗余噪声,导致局部区域图像模糊不清,无法对目标个体判定或识别。为解决上述问题,构建一种基于变分正则化的低质视频图像二维增强方法。通过采集先验信息,确定噪声去除和质量增强区域,随后使用ROF经典模型和偏微分对原始图像做平滑及去噪处理,并提出前后扩散方程,使方法能够有效抑制背景区域噪声,并对目标个体边缘做锐化处理,利用变分正则化提升视频图像整体分辨率,使其完成图像二维增强。仿真结果表明,所提方法具有控制噪声与加强图像质量双重优势,有效去除、抑制噪声影响,提升目标个体区域强散射点,且方法复杂程度较低,能够实现低质视频图像的均衡优化。     

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell-location-based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance-based threshold, K-Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell-graph. Histopathological images of liver were classified by using features extracted from T-Distance, K-Nearest Neighbor, Voronoi, Delaunay, and the proposed cell-graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell-graph model.     

“电网一张图”时空信息管理系统

文章首先总结了电力企业信息管理系统的现状,提出了多源信息管理系统融合集成的邦联、子集和完全融合3种方式,分析了3种方式的优缺点,以建设企业"互联网搜索"型数字引擎为目标,提出了融合基于本体的图数据库建模,多维关联与路径因果分析,和人机共生互动可视化决策于一体的企业信息管理系统技术发展趋势;在系统分析目前正在快速发展的图数据库技术与传统关系数据库之间的本质区别基础上,总结原创的电力图计算平台技术的特点;在此基础上讨论"电网一张图"在建设能源互联网时空信息管理平台数据关联索引图中的核心作用,提出了支持能源互联网建设的"电网一张图"时空信息管理系统的技术架构与特点,最后总结"电网一张图"时空信息管理系统在提高电网安全运行水平、提升客户服务水平、增强清洁能源消纳水平、改善经营绩效水平、以及为综合能源服务和建设能源生态等提供数据服务方面的广阔应用场景。