Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

火电仿真技术研究

在电力行业中，计算机仿真一直是不可缺少的工具，它在减少损失、节约经费、提高效率、确保安全经济运行等方面发挥了巨大作用。主要介绍了计算机仿真技术在660MW火电站中的具体应用情况。以邯峰发电厂^#1机为对象，采用STAR-90仿真支撑系统，建立起660MW火电机组1：1仿真机，并在机组投产前建成，取得了良好的培训效果。论述了火电站仿真机未来的发展趋势，包括开发高精度机理性仿真数学模型以及真实的DCS仿真模型、进行性能计算与故障诊断、应用于热工人员培训及机组调试等。     

风力发电与变流器

讨论了目前流行的两种主流风力发电系统：变速恒频双馈风力发电系统和永磁直驱同步风力发电系统。着重分析了两种风电变流器的拓扑结构、工作原理和性能特点,对风力发电系统发电机、变流器、无功支持和低电压穿越能力等方面进行了论述。     

基于MPC850 VxWorks系统的BSP设计

首先介绍嵌入式系统VxWorks及其启动过程;分析了基于MPC 850最小系统的硬件和BSP的执行流程;重点研究了MPC850BSP的设计与应用程序加载,包括其硬件驱动程序的设计、应用程序加载、建立调试环境和target server的配置等;基于MPC850系统的Vxworks BSP调试平台的实现,对Vxworks上层应用软件和底层硬件驱动的并行开发有着很重要的意义。     

基于Gabor小波与LBP直方图序列的人脸年龄估计

提出了一种基于Gabor小波和局域二值模式(Local binary pattern,LBP)直方图序列的人脸年龄估计方法。首先对人脸图像提取多方向与多尺度的Gabor幅值域图谱(Gabor magnitude maps,GMMs);然后采用基于局部特征的LBP算子对GMMs编码,并对之分块,由各子块的直方图序列来描述人脸;为进一步降低人脸特征维数,再对人脸直方图序列特征应用主成分分析(PCA);最后使用支持向量机回归(SVR)的LOPO策略对人脸年龄库进行训练和测试。实验结果表明,该方法可以较为快速有效地对人脸图像进行年龄估计。     

最小支持度在教学质量评价系统中的数据挖掘应用

以当前的高职高专院校教育教学为平台,研究分析最小支持度关联规则挖掘技术与教学质量评价相结合的问题,针对教学质量评价结果中的盲目性、不易把握等问题,提出利用最小支持度来分析质量评价体系,获取以影响因素为条件,以评价分析为结论的关联规则来解决目前教学质量评价中的不合理性,使教学质量评价公平、公正、合理、高效,并给教学管理人员提供决策分析资料。     

构建面向销售服务的运营商管理支撑系统

在电信运营行业竞争日益加剧的大环境下,市场对传统运营商运营模式的转变提出了更加具体的要求。面向销售服务的运营商管理支撑系统为市场经营"围绕客户、细分需求、有效沟通"提供了信息化解决方案。文章结合某电信运营商的具体案例,分析了现有的支撑系统,提出了销售服务管理支撑系统进一步建设的必要性、目的性、设计思路、规范原则、总体架构和技术体系,描述了系统的相关功能,归纳了其主要特点,并思考了系统实施过程中应注意的一些问题。     

地震断裂带高铁大跨拱桥深基坑钢板桩围堰支护试验与监测

针对大跨度高速铁路拱桥建设对深基坑开挖施工的安全性要求,为了研究钢板桩围堰支护在大跨度高速铁路拱桥深基坑施工中的可行性,以新建徐盐高铁徐洪河特大桥工程项目为依托,通过数值模拟及现场试验监测,对深基坑钢板桩围堰支护在施工过程中的形变及受力情况进行分析。结果表明:通过有限元模拟分析,钢板桩围堰最危险受力点处于钢板桩中部、中上部及四角连接处,围堰第4道内支撑内力较其他3道内支撑大。整个施工过程中工况5为施工最不利工况。由监测结果可知,在施工过程中钢板桩单日最大位移值为2.8mm,最大累计位移值为24.0mm,均未达到预警值,施工过程中钢板桩形变稳定。钢支撑轴力受基坑周边荷载的影响明显,围堰钢支撑最大压力值为147.31kN,最大拉力值为24.95kN,未达到预警值,结构安全。研究结果可供同类型工程施工参考。     

洪水预报智能模型在中国半干旱半湿润区的应用对比

为探究不同洪水预报智能模型在我国半干旱半湿润区的应用效果,选用决策树、多层感知器、随机森林和支持向量机4种模型在陕西省3个典型流域进行逐时洪水预报;选择相关系数、纳什效率系数、均方根误差、平均绝对误差和相对误差等评价指标,比较不同预见期下4种模型在半干旱半湿润典型流域洪水预报的适用性。结果表明:在短预见期预报中,4种模型在半湿润区典型流域均可获得较高的预报结果,在半干旱典型流域模拟精度相对偏低,仅支持向量机模型满足预报要求;随着预见期延长,不同模型性能变化差异大,支持向量机模型整体稳定,在小流域实时洪水预报中具有明显优势;随机森林模型与决策树模型精度随预见期延长而缓慢下降,前者适应性更好;多层感知器模型精度随预见期延长而骤减,模型稳定性较差。     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.