PDE Toolbox和最小二乘法在导热问题中的应用

设计金属加热的控制策略,首先要获得金属导热体的开环系统模型.针对一个带有矩形孔的金属薄板上的热传导问题,采用PDE Toolbox进行求解和仿真,并用最小二乘方法进行热传导系统的建模与仿真,以获得模型中的参数.将此参数与求解微分方程方法所得到的数据进行对比,结果表明,采用最小二乘法可以更清楚地了解金属板的外部特性,并获得输入输出信号的差分方程,而求解微分方程的方法则显示了金属板的内部特性,两种方法是等效的.     

空间数据库中一种自适应的缓存替换策略

随着近年来空间数据库研究和应用的不断深入,针对空间数据库中数据组织和查询的特征来设计缓存页面替换策略成为一个新的研究问题.Voronoi图是一种重要的空间数据库组织技术,在处理kNN查询时具有非常好的性能.针对Voronoi图组织的空间数据库,首先利用空间局部性提出了一种基于欧氏距离的替换策略,在发生页面失效时选择距离上一次访问页面欧氏距离最远的页面进行替换;进一步,针对不同kNN查询的搜索空间大小差异非常大的特点,在LIRS替换策略基础上提出一种自适应替换策略,通过对HIR页面占缓存比例自动调整来适应不同的查询.综合两者,形成基于欧氏距离的自适应缓存页面替换算法AELIRS.大量实验表明,在缓存大小与搜索空间大范围变动中,AELIRS始终优于其他替换策略.     

可整体或局部调控的C^3，C^4连续的插值曲线

首先运用两种方法构造了带多形状参数的C^3连续的插值曲线．其次,利用方法二把构造出的带多形状参数的C^4连续的B样条曲线与参数化的奇异多边形按某个因子调配,可自动生成C^4连续的插值曲线．所有这些曲线的形状既能整体又可局部调控．     

基于树核函数的英文句子情感分类研究

最近几年,由于在线客户评论信息飞快地增长。如何把这些信息分类为正向和负向情感是一个迫切需要解决的问题。提出了一种细粒度级别(句子级别)的情感分类方法,该方法在SVM分类器中使用了树核和复合核函数来进行句子级别情感的分类。实验结果表明在句子级别的情感分类中树核和复合核的方法比线性核具有更佳的性能。     

基于模式映射的GML文档存储

随着地理信息系统GIS(Geography Markup Language)的广泛应用,GML己经成为事实上的空间数据编码、传输、共享和发布的一种国际标准。近年来,大量GML数据以文档形式出现,对如何高效地存储和管理GML数据提出了新的挑战。根据GML文档的特点,提出了一种基于模式映射的存储方法。首先,根据映射规则将GML模式生成对象-关系数据库模式;其次,解析GML文档并根据映射信息构造相应的SQL语句,将数据存储于数据库。实验表明,提出的存储方法是可行、高效的。     

利用反馈的车牌字符分割算法

字符分割是车牌识别系统的重要步骤。提出一种利用反馈的车牌字符分割算法。首先,对车牌图像进行预处理;其次,根据车牌图像的垂直投影进行字符粗分割;最后,通过两级反馈进行字符精分割。第一级反馈是字符个数与字符宽度反馈,第二级反馈是字符识别反馈。实验结果表明,算法对光照不均、对比度较小、倾斜、污迹、字符粘连和断裂等严重退化的车牌图像具有很好的字符分割性能。     

基于OPNET的一种典型现场总线的研究与仿真

随着网络规模的扩大,使用仿真软件对网络进行仿真,对关键参数和原理进行验证,已经是一种流行的网络开发模式.首先对现场总线和仿真软件OPNET进行了相关介绍和分析,接着对一种典型的现场总线的特征进行了详细说明,然后基于OPNET对该典型总线进行了建模仿真.基于对采集数据的分析,验证了仿真模型的时延符合典型现场总线标准的要求...     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

How does Internet information seeking help academic performance? – The moderating and mediating roles of academic self-efficacy

Although researchers tend to agree that Internet is a good source for learning and research, little empirical data has substantiated this claim by specifically linking time and effort spent on the Internet for school related information seeking to academic performances. This research investigates the relationship between vocational high school students’ information seeking activities on the Internet, academic self-efficacy, and academic performance. We propose that academic self-efficacy both mediates and moderates the relationship between Internet information seeking and academic performance. Using survey data from 295 vocational high school students in Taiwan, we found that the positive effect of Internet information seeking to students’ academic performance is mediated through academic self-efficacy. Academic self-efficacy, at the same time, moderates the relationship between Internet information seeking to academic performance such that students’ with low academic self-efficacy benefit more from Internet information seeking in regard to their academic performance. We discussed the implications of our findings and provided future directions for research.