ITR: A Real Time Mining of Integrated Weather Data

ITR：综合天气数据的实时挖掘

• 批准号: 0205628
• 负责人: Theodore Trafalis
• 金额: --
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205628&HistoricalAwards=false
• 关键词: ITR; Real; Time; Mining; Integrated

EIA-0205628 Theodore B. Trafalis University of Oklahoma ITR: A Real Time Mining of Integrated Weather DataThe mission is to build systems and develop theory for extracting information and identifying patterns that are useful for making decision in real-time. Funding is being requested to build pattern recognition techniques that will exploit multisensor data in an integrated manner to provide information such as the presence or absence of tornados, supercells and mesocyclones; estimate precipitation; predict the occurrence of flash floods; assimilate and display large volumes of multisensor data and trigger the archive of selected data sets. These tasks will be accomplished by customizing and developing techniques for real-time data mining. The approaches used will include traditional data reduction methods such as PCA and clustering; Procrustes analysis; Kalman filters and non-linear time series analysis with regime switching; and, decomposition and robust optimization methods for training support vector machines.

EIA-0205628西奥多B。俄克拉荷马州特拉法利斯大学ITR：一个综合天气数据的真实的时间挖掘的使命是建立系统和发展理论，提取信息和识别模式，是有用的实时决策。 目前正在申请资金，以建立模式识别技术，综合利用多传感器数据，提供龙卷风、超级单体和中气旋存在与否等信息;估计降水量;预测山洪暴发的发生;吸收和显示大量多传感器数据，并启动选定数据集的存档。 这些任务将通过定制和开发实时数据挖掘技术来完成。 使用的方法将包括传统的数据简化方法，如PCA和聚类; Procrustes分析;卡尔曼滤波器和具有状态切换的非线性时间序列分析;以及用于训练支持向量机的分解和鲁棒优化方法。