A sliding window technique for interactive high-performance computing scenarios

Interactive high-performance computing is doubtlessly beneficial for many computational science and engineering applications whenever simulation results should be visually processed in real time, i.e. during the computation process. Nevertheless, interactive HPC entails a lot of new challenges that have to be solved – one of them addressing the fast and efficient data transfer between a simulation back end and visualisation front end, as several gigabytes of data per second are nothing unusual for a simulation running on some (hundred) thousand cores. Here, a new approach based on a sliding window technique is introduced that copes with any bandwidth limitations and allows users to study both large and small scale effects of the simulation results in an interactive fashion.     

数据中心网络TCP Incast仿真分析

为分析云计算数据中心网络中的TCP Incast问题的产生原因并探讨缓解该问题的有效途径,采用基于NS2仿真实验的方法,通过改变TCP协议类型、交换机缓冲区大小、服务请求单元大小及重传超时RTO值等影响因素,分析传输吞吐率性能的变化。分析结果表明,通过改变这些影响因素有助于缓解TCP Incast问题,但同时也存在各种缺点和局限性。     

利用曲线拟合对触觉数据的压缩

为减轻离线触觉数据压缩带来的失真变形,提出利用曲线拟合的压缩方法.在假定触觉数据所对应原触觉信号能用分段二次参数曲线表示的前提下,通过反向采样过程来构造触觉数据拟合曲线,该曲线由分段二次曲线组成,在连接处达到切线连续,且每段二次曲线对原触觉信号具有二次多项式逼近精度.为避免保存拟合曲线的所有系数以进一步提高数据减少率,对拟合曲线系数进行了迭代求解,从而只需编码保存部分系数即可解码.实验结果表明,该方法在保持高数据减少率的前提下减轻了压缩失真变形.     

S1000D标准下数据模块发布样式的研究

为促进S1000D标准在以信息化为中心的综合保障技术中的应用,提高装备技术文档制作和发布的正确性和效率,文章研究了S1000D标准对PDF格式发布样式的规定,分析了XSL的工作原理和各类技术信息的Schema模式,利用XSL-FO技术编写了把数据模块发布成PDF格式的XML文档,通过XMLSPY实现了技术文档的发布;通过实例展示了实现规定的发布样式的关键技术以及从数据模块到PDF发布样式的显示效果,证明了所用方法的正确性与实用性,为实现S1000D标准下的技术信息发布提供了新的可行方法.     

软件开发框架模型——现实世界问题的结构化分析

对软件开发框架模型提出了一种新的构化的分析方法,同时介绍了常用的结构化分析的工具及其更实用情况。     

异常数据挖掘在实际中的应用

本文主要讨论了异常数据挖掘在实际中的应用,简要地介绍了异常点的定义、分类及异常数据挖掘的定义、功能等,详细的介绍了异常数据挖掘技术在金融领域、电信领域、商业领域中和生物医学以及DNA分析中的应用。     

基于ASP.NET的互动多媒体学习社区网站建设

针对互动媒体网站建设要求,利用ASP.NET2.0技术设计开发互动多媒体学习社区网站,本文对实现网上教学以及多媒体学习进行探讨。并详细介绍互动多媒体学习社区网站的主要功能设计和核心代码。     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.