Data Mining for the Advection Database of Wake Vortices
Data Mining for the Advection Database of Wake Vortices
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DOI：
10.2514/6.2010-7989
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
H. Kato;K. Shimoyama;S. Obayashi;M. Kudo
中科院分区：
文献类型：
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作者：
H. Kato;K. Shimoyama;S. Obayashi;M. Kudo
*† ‡ § 尾流涡流平流数据库是根据仙台机场的多普勒激光雷达测量结果构建的，旨在了解测量结果、天气因素和尾流涡流行为之间的关系。尾涡平流数据库由三个测量因子和十五个天气因子组成，作为确定尾涡行为的变量。然而，从高维数据中提取相关性通常很困难。因此，本文采用数据挖掘作为从尾流涡平流数据库中提取变量之间关系的有效方法。作为数据挖掘的第一步，通过斯皮尔曼等级方法去除冗余测量、天气因素，然后创建自组织图（SOM）以查找测量、天气因素和尾流涡行为之间的相关性。接下来，通过方差分析（ANOVA）确定了对尾流涡行为影响较大的测量、天气因素。最后，粗糙集理论揭示了确定尾流涡行为的天气因素的具体测量规则。
*† ‡ § Advection database of wake vortices were constructed based on the Doppler lidar measurements at Sendai Airport to understand the relations among measurement, weather factors and behavior of wake vortex. The advection database of wake vortex consists of three measurement factors and fifteen weather factors as variables to determine the wake vortex behavior. However, the extraction of correlations from the high-dimensional data is difficult in general. Therefore, in this paper, data mining was employed as an efficient approach to extract relations among the variables from the advection database of wake vortex. As a first step of the data mining, redundant measurement, weather factors were removed through Spearman rank method, then Self-Organizing Map (SOM) was created to find correlations among the measurement, weather factors and wake vortex behavior. Next, the measurement, weather factors which have larger influences to the behavior of the wake vortex were identified by Analysis of Variance (ANOVA). Finally, rough set theory revealed the specific rules of measurement, weather factors to determine wake vortex behavior.