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.     

High sensitive and selective formaldehyde sensors based on nanoparticle-assembled ZnO micro-octahedrons synthesized by homogeneous precipitation method

Nanoparticle-assembled ZnO micro-octahedrons were synthesized by a facile homogeneous precipitation method. The ZnO micro-octahedrons are hexagonal wurtzite with high crystallinity. Abundant structure defects were confirmed on ZnO surface by photoluminescence. Gas sensors based on the ZnO micro-octahedrons exhibited high response, selectivity and stability to 1–1000 ppm formaldehyde at 400 °C. Especially, even 1 ppm formaldehyde could be detected with high response (S = 22.7). It is of interest to point out that formaldehyde could be easily distinguished from ethanol or acetaldehyde with a selectivity of about 3. The high formaldehyde response is mainly attributed to the synergistic effect of high contents of electron donor defects (Zn_i and V_O) and highly active oxygen species (O²⁻) on the ZnO surface.     

Study of land cover classification based on knowledge rules using high-resolution remote sensing images

This paper deals with the limitations of visual interpretation of high-resolution remote sensing images and of automatic computer classification completely dependent on spectral data. A knowledge-rule method is proposed, based on spectral features, texture features obtained from the gray-level co-occurrence matrix, and shape features. QuickBird remote sensing data were used for an experimental study of land-use classification in the combination zone between urban and suburban areas in Beijing. The results show that the deficiencies of methods where only spectral data are used for classification can be eliminated, the problem of similar spectra in multispectral images can be effectively solved for the classification of ground objects, and relatively high classification accuracy can be reached.