兴趣度在基于关联分析的选课指导系统中的研究与应用*

简要地介绍了数据挖掘技术,通过对关联分析的经典算法Apriori在学生选课指导系统中的应用分析,发现了Apriori不适合学生选课指导系统的缺陷.提出了增加兴趣度阈值以减少产生的无用规则,提高挖掘精度,克服原系统缺陷的新算法,为学生选课辅助决策提供了良好的理论依据和实现方法.     

圆锯片的有限元模态分析

针对金属冷热锯机锯片在锯切轧件过程中出现的振动问题 ,利用 MSC/ NASTRAN有限元软件 ,计算分析了各种参数对锯片模态的影响。从而为锯片的设计及其制造工艺的确定提供了可靠的参考依据 ,为最终解决生产中的实际问题发挥了重要作用     

面向二进制数据帧的聚类系统

为了分离复杂无线网络环境下获取到的二进制数据帧,为后续协议逆向解析提供前提条件,实现了对复杂协议簇协议的聚类系统.首先使用AC算法挖掘出二进制数据帧中的频繁序列特征;然后创新地使用了Apriori算法搜索分析这些特征的关联关系,并且结合二进制流数据帧的特点对结果进行了四步剪枝处理;最后利用筛选出的特征通过改进的K-means算法进行聚类.实验表明,该系统可以对二进制协议数据帧的聚类起到很好的效果,同时对存在TYPE字段的多层协议簇,还能进一步区分出多种协议间的层次关系.     

Rotation,scaling and translation invariant texture recognition by Bessel-Fourier moments

The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) invariant texture recognition using Bessel-Fourier moments. Compared with the others moments based methods, the radial polynomials of Bessel-Fourier moments have more zeros and these zeros are more evenly distributed. It makes Bessel-Fourier moments more suitable for invariant texture recognition as a generalization of orthogonal complex moments. In the experiment part, we got three testing sets of 16, 24 and 54 texture images by way of translating, rotating and scaling them separately. The correct classification percentages (CCPs) are compared with that of orthogonal Fourier-Mellin moments and Zernike moments based methods in both noise-free and noisy condition. Experimental results validate the conclusion of theoretical derivation: BFM performs better in recognition capability and noise robustness in terms of RST texture recognition under both noise-free and noisy condition when compared with orthogonal Fourier-Mellin moments and Zernike moments based methods.     

Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes’ future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.     

Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.     

虚拟天文台技术在HXMT卫星任务中应用的设想

随着国际上天文项目的发展与推进,天文数据呈爆炸式增长。如何从浩瀚如海的大数据集中快速检索所需信息,已成为各国科研人员共同面对的问题。虚拟天文台（Virtual Observatory,VO）为满足对大规模天文数据进行处理分析、检索访问和共享发布等需求,提供了一系列 VO 标准协议和集成化服务。开发符合 VO 标准协议的天文数据发布与检索系统、集成数据访问发布和可视化分析服务,符合当前的发展趋势。本文在回顾 VO发展历程的基础上,对 VO 体系结构、VO 标准协议等若干关键技术进行概括阐述。同时,基于 VO的技术框架,提出硬 X 射线调制望远镜（Hard X-ray ModulationTelescope,HXMT）卫星数据发布与检索系统的初步设想,设计符合卫星任务需求的体系结构,并选取相关的 VO 标准协议加以实现。