吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1202-1208.doi: 10.13229/j.cnki.jdxbgxb201604028

• 论文 • 上一篇    下一篇

时间序列降维及机场噪声中的机型识别

王寅同, 王建东, 陈海燕   

  1. 南京航空航天大学 计算机科学与技术学院,南京 210016
  • 收稿日期:2015-01-27 出版日期:2016-07-20 发布日期:2016-07-20
  • 通讯作者: 王建东 (1945-), 男, 教授,博士生导师.研究方向:人工智能, 数据挖据,网络安全.E-mail:aics@nuaa.edu.cn
  • 作者简介:王寅同 (1987-), 男, 博士研究生.研究方向:数据挖掘, 数据降维.E-mail:wangyintong@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(61139002); 中央高校科研业务费专项资金项目(NS2015091); 江苏省博士后科研资助计划项目(1301013A)

Time series dimensionality reduction and aircraft model recognition in airport-noise

WANG Yin-tong, WANG Jian-dong, CHEN Hai-yan   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2015-01-27 Online:2016-07-20 Published:2016-07-20

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摘要： 为了提高非完整标记的高维机场噪声数据的处理速度和效率,研究了时间序列降维及机场噪声中的机型识别问题。首先采用概率类和不相关判别的半监督局部Fisher方法(SLFisher)得到降维转换矩阵,再将时间序列数据由高维空间映射到低维空间,最后在低维数据上进行k最近邻分类(kNN)。在国内某机场的实测噪声数据上的实验结果表明,SLFisher降维后机场噪声事件数据的机型识别效果取得显著提升。

关键词: 人工智能, 机场噪声, 时间序列, 降维, 机型识别

Abstract: In order to enhance the efficiency and effectiveness of the processing of high dimensional airport-noise data with incomplete labels, the time series dimensionality reduction and aircraft model recognition in airport-noise are investigated. First, we use semi-supervised local fisher method, which is based on probability class and uncorrelated discriminant (SLFisher), to obtain the transformation matrix of dimensionality reduction. Next, the high dimensional time series data are mapped to the low-dimensional space. Finally, we apply the k Nearest Neighbor (kNN) classifier to classify the obtained low-dimensional data. Experimental results on measured airport-noise data demonstrate that the performance of aircraft model recognition is remarkably improved after the dimensionality reduction achieved using SLFisher.

Key words: artificial intelligence, airport-noise, time series, dimensionality reduction, aircraft models recognition

中图分类号: 

  • TP18
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