基于K-近邻法的股价曲线的模式识别分类研究

本文将模式识别分类技术应用于股票市场进行搜索选股。本项目采用股票价格走势来构造特征空间,依据样本股票价格走势类型对股票市场上的所有股票进行模式识别分类,并将所获得的分类结果中的第一类股票作为搜索选股的目标股票。通过对沪深两市股票进行的实证研究分析,结果表明:采用模式识别分类技术依据股票价格走势进行搜索选股是可行的,具有良好的实时性和较高的针对性,实用性较强,并可进一步改进。     

Branching and circular features in high dimensional data

Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in data analysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of high-dimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto low-dimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original high-dimensional data. Our solution is to utilize topological techniques to recover important structures in high-dimensional data that contains non-trivial topology. Specifically, we are interested in high-dimensional branching structures. We construct local circle-valued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal never-before-seen structures on real-world data sets from a variety of applications.     

Automatic detection of the topographical distribution of EEG rhythms based on an iterative adjustment of the averaged reference potential

Electroencephalogram (EEG) interpretation is important for the investigation of brain diseases. In this study, an automatic technique was developed to detect the topographical distribution of EEG rhythms. In order to obtain the topographical distribution, the amplitude of the EEG rhythm was analyzed based on the referential derivation where the reference potential was adjusted iteratively. The result of the automatic detection of the topographical distribution was helpful in highlighting the EEG rhythms of interest for automatic EEG interpretation. The technique developed has application significance for real clinics.     

基于试题关系树的自适应诊断测试模型

针对实际测试过程中试题可能包含多个知识点组合的问题,提出一种基于试题关系树的自适应诊断测试模型的解决方案.根据知识点组合的包含关系,分析得出试题之间存在的3种前提关系,在此基础上提出以与子结点和或子结点将试题库进行关联,形成试题关系树.重点分析了基于试题库构建试题关系树的过程,给出了基于试题关系树的测试策略与诊断过程,并最终形成诊断决策树.实验结果表明,该模型可有效地减小平均测试长度,提高诊断效率.     

带关键词检索的公钥加密方案

针对现有的带关键词检索的公钥加密(PEKS)方案中的大量对运算需要消耗较多时间,且都存在不能抵抗重放攻击的问题,在研究PEKS的基础上,提出一种高效的带关键词检索的公钥加密方案。与现有的PEKS方案相比,该方案保留了现有PEKS方案的优点,能有效地抵抗重放攻击。     

静电对电子会议系统的影响与对策

静电是日常生活中一种常见的现象,但它却能影响电子会议系统的正常运行.针对会场静电干扰问题,通过分析人的行为和环境因素,在反复试验的基础上,提出了铺设防静电地毯、喷洒防静电液、空气增湿等消除静电和减少静电产生的措施,通过综合采取上述措施,基本解决了会场的静电干扰问题,有效地提高了电子会议系统运行的可靠性.结果表明,这些措施对于解决静电干扰问题是易行有效的,同时考虑到产生静电因素的多重性,综合采取多项措施也是必要的.     

基于软件程序员课程开发的研究

软件程序员的培养是高职软件技术专业的主要培养对象。通过分析IT企业对程序员的需求及程序员必备的素质,结合软件程序员应有的核心能力,建立和完善面向高职院校的软件技术专业的人才培养的课程体系,为适应市场需求的程序员的培养进行了研究与实践。     

网络电视的测量与分析研究综述

随着基于Internet的流媒体播放技术的成熟,网络电视正逐渐推广开来并改变人们观看电视的习惯.为了提高服务质量,实现网络电视用户、内容供应商和网络运营商共赢的解决方案,必须从不同的视角对网络电视进行系统化地测量与分析.首先对互联网中主流的网络电视系统从体系结构、服务类型等方面进行了系统化的分类,讨论了不同类别的特点.其后,介绍了针对网络电视的测量研究现状、主动与被动测量方法,重点阐述了针对网络电视系统不同特征的测量与分析结果,包括拓扑结构、流量特征、用户访问模式、用户会话长度、节点选择策略和系统性能等.最后总结了已有网络电视测量的局限性,对今后的研究方向提出展望.     

从MC9S12D到MC9S12XS

从第四届全国大学生"飞思卡尔"杯智能车竞赛开始,竞赛组委会推荐使用MC9S12XS128单片机替代前三届竞赛中使用的MC9S12DG128。S12X是新一代双核单片机,制造工艺从0.25μm进步到0.18μm;总线频率从25MHz提高到40MHz;S12XS单片机保留了S12X CPU,去掉了另一个CPU-XGate,保留了技术进步,提高了性能,简化了操作。     

CAN节点总线关闭状态下通信中断的解决方法

CAN总线通信中,当某一节点进入总线关闭状态后,不会与总线其他节点通信.针对这个问题,分析了节点从总线关闭状态转化为主动错误状态的条件,提出了采用模拟开关和电压基准主动实现这种转化条件的解决方法,解决了CAN节点恢复时间不确定的问题.