航空运输无处不在世界中的应用数据挖掘方法

Saybani Mahmoud Reza, T. Wah, A. Lahsasna
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引用次数: 5

摘要

噪音对住在机场附近的人来说是个大问题,因此公众、机场当局和飞行员都在寻找减少人口稠密地区附近噪音的方法。最优的解决方案应该是离这些区域最远的飞行路线,而最糟糕的飞行路线则是刚好在这些区域上方的。有两类路径,即最优路径和非最优路径。本文将使用一种成功的数据挖掘技术,即神经网络,它具有识别模式的能力。我们使用各种飞行路径的一些坐标作为神经网络学习的输入,并定义了代表最优和非最优飞行路径的两个类。结果表明,该方法能够很好地识别最优和非最优飞行路径。这种技术可以用来降低噪声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applied Data Mining Approach in Ubiquitous World of Air Transportation
Noise is a big problem for people living near airports, therefore the public, airport authorities and pilots are looking for ways to reduce the noise in the vicinity of populated areas. Optimal solution would be flight paths that are farthest from those areas, and worst paths are those, that just go above them. There are two classes of paths, namely optimal and non-optimal ones. This paper is going to use one of successfully used data mining techniques, namely neural network, which is capable of recognizing patterns. We used some coordinates of various flight paths as input for learning purposes of Neural Network, and defined two classes representing the optimal and non-optimal flight paths. The results have shown that this technique is well capable of recognizing the optimal and non-optimal flight paths. This technique can be used to reduce the noise.
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