Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Direct microscale imprinting of Al at room temperature with Si inserts

For direct imprinting of metals, hard materials such as diamond and SiC have been used to construct mold inserts in preference to Si, despite the ease in fabricating Si-based micro-/nano- scale structures. In this communication, we demonstrate that micron scale Al structures can be replicated with good fidelity at room temperature by compression molding with Si inserts without incurring insert damage. We further report on results of a finite element analysis of the mechanics of the molding process. The finite element results provide some understanding of the observed lack of damage to the Si inserts.     

数据中心管理不再复杂

随着数据中心基础设施以及应用系统的增多,IT人员的工作量逐渐加大,自动化管理让这些变得更容易。     

无线局域网及其安全技术

无线局域网采用电磁波作为载体进行信息传输,存在着信息泄露及被窃听的安全隐患。笔者着重分析无线网络安全隐患的几个主流技术,认为完全可以将发生危险的可能降到最低程度。     

Polarization discrimination between repeater false-target and radar target

High fidelity repeater false-target badly affects a radar system’s detecting, tracking, and data processing. It is an available approach of confronting false-target for radar that discriminates firstly and then eliminates. Whereas for the technique progress about the repeater false-target jam, it is more and more difficult to discriminate this jam in the time-domain, frequency-domain, or space-domain. The technique using polarization information to discriminate the target and false-target is discussed in th...     

面向业务问题求解的知识供应过程模型

为了满足企业在业务问题求解方面的实际需求,提出了一个面向业务问题求解的知识供应过程模型。该模型提出了知识供应过程中的问题空间、知识空间和流程空间的概念及其关联关系,并进一步给出了包含知识的获取、评价、存储和重用的知识供应过程的有关算法。该模型与算法将业务问题求解过程与知识供应过程进行了融合,通过关联关系实现了知识的主动供应,在优化了业务问题求解过程的同时,提高了知识积累的质量和知识重用的效率。基于该模型建立了知识管理问题求解的原型系统KMPSS,并得到了初步应用验证。     

TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data

Statistical analysis of air mass back trajectories combined with long-term ambient air pollution measurements are useful tools for source identification. Using these methods, the geographic information system (GIS) based software, TrajStat, was developed to view, query, and cluster the trajectories and compute the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses when measurement data are included.