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

RICH HTML

0   

PDF (PC)

124

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
[1] Fu T. A review on time series data mining[J]. Engineering Applications of Artificial Intelligence,2011,24(1):164-181.
[2] Batista G E,Wang X,Keogh E J. A complexity-invariant distance measure for time series[C]∥Proceedings of the 2011 SIAM International Conference on Data Mining,2011,11:699-710.
[3] Esling P,Agon C,Time-series data mining[J]. ACM Computing Surveys (CSUR),2012,45(1):1-12.
[4] Wang X,Mueen A,Ding H,et al. Experimental comparison of representation methods and distance measures for time series data[J]. Data Mining and Knowledge Discovery,2013,26(2):275-309.
[5] 吴红花,刘国华,王伟. 不确定时间序列的相似性匹配问题[J]. 计算机研究与发展,2014,51(8):1802-1810.
Wu Hong-hua,Liu Guo-hua,Wang Wei. Similarity matching for uncertain time series[J]. Journal of Computer Research and Development,2014,51(8):1802-1810.
[6] Scott S,Varian H. Predicting the present with bayesian structural time series[J]. International Journal of Mathematical Modelling and Numerical Optimisation,2014,5(1):4-23.
[7] Keogh E,Chakrabarti K,Pazzani M,et al. Dimensionality reduction for fast similarity search in large time series databases[J]. Knowledge and Information Systems,2001,3(3):263-286.
[8] Chakrabarti K,Keogh E,Mehrotra S,et al. Locally adaptive dimensionality reduction for indexing large time series databases[J]. ACM Transactions on Database Systems,2002,27(2):188-228.
[9] Xing E P,Jordan M I,Russell S,et al. Distance metric learning with application to clustering with side-information[J].Advances in Neural Information Processing Systems,2003:15:505-512.
[10] 邹朋成,王建东,杨国庆,等. 辅助信息自动生成的时间序列距离度量学习[J]. 软件学报,2013,24(11):2642-2655.
Zou Peng-cheng,Wang Jian-dong,Yang Guo-qing,et al. Distance metric learning based on side information autogeneration for time series[J]. Journal of Software,2013,24(11):2642-2655.
[11] Jolliffe I. Principal Component Analysis[M].USA: John Wiley & Sons,2005.
[12] Abdi H,Williams L J. Principal component analysis[J]. Wiley interdisciplinary reviews:computational statistics,2010,2(4):433-459.
[13] Lin J,Keogh E,Lonardi S,et al. A symbolic representation of time series, with implications for streaming algorithms[C]∥ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,San Diego,USA,2003:2-11.
[14] Lin J,Keogh E,Wei L,et al. Experiencing SAX:a novel symbolic representation of time series[J]. Data Mining and Knowledge Discovery,2007,15(2):107-144.
[15] Ahmed A,Bakar A,Hamdan A. Harmony search algorithm for optimal word size in symbolic time series representation[C]∥IEEE 2011 3rd Conference on Data Mining and Optimization,Bangi,Malaysia,2011:57-62.
[16] Park S,Kim S W,Chu W W. Segment-based approach for subsequence searches in sequence databases[C]∥Proceedings of the 2001 ACM Symposium on Applied Computing,New York,USA,2001:248-252.
[17] 王寅同,王建东,陈海燕,等. 基于概率类和不相关判别的半监督局部Fisher方法[J]. 控制与决策,2015,30(1):32-38.
Wang Yin-tong,Wang Jian-dong,Chen Hai-yan,et al. Semi-supervised local Fisher method based on probability class and uncorrelated discriminant[J]. Control and Decision,2015,30(1):32-38.
[18] Fisher R. The Use of multiple measurements in taxonomic problems[J]. Annals of Eugenics,1936,7(2):179-188.
[19] Sakoe H,Chiba S. Dynamic programming algorithm optimization for spoken word recognition[C]∥IEEE Transactioin on Acoustics,Speech and Signal Processing,1978,26(1):43-49.
[20] Xi X,Keogh E,Shelton C,et al. Fast time series classification using numerosity reduction[C]∥In Process of the International Conference on Machine Learning,New York,USA,2006:1033-1040.
[21] Rakthanmanon T,Campana B J,Mueen A,et al. Searching and mining trillions of time series subsequences under dynamic time warping[C]∥In Process of the International Conference on Knowledge Discovery and Data Mining,New York,USA,2012:262-270.
[22] Keogh E. Exact indexing of dynamic time warping[J].Knowledge and Information Systems,2005:7(3):358-386.
[23] Shimodaira H,Noma K,Nakai M,et al. Dynamic time-alignment kernel in support vector machine[J].Advances in Neural Information Processing Systems,2002,14:921-928.
[24] Cuturi M,Vert J P,Birkenes O,et al. A kernel for time series based on global alignments[C]∥IEEE Computer Society on Acoustics,Speech and Signal Processing,Honolulu,HI,2007:413-416.
[25] Cuturi M. Fast global alignment kernels[C]∥In Process of the International Conference on Machine Learning,ACM,Bellevue,USA,2011:929-936.
[1] 董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.
[2] 顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[3] 王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] 张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] 黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] 李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] 刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] 刘舒, 姜琦刚, 朱航, 李晓东. 基于Hyb-F组合滤波算法的向海自然保护区NDVI时间序列重构[J]. 吉林大学学报(工学版), 2018, 48(3): 957-967.
[9] 王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[10] 杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[11] 刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12] 万平, 吴超仲, 林英姿, 马晓凤. 基于驾驶行为多元时间序列特征的愤怒驾驶状态检测[J]. 吉林大学学报(工学版), 2017, 47(5): 1426-1435.
[13] 曲慧雁, 赵伟, 秦爱红. 基于优化算子的快速碰撞检测算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[14] 李嘉菲, 孙小玉. 基于谱分解的不确定数据聚类方法[J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[15] 邵克勇, 陈丰, 王婷婷, 王季驰, 周立朋. 无平衡点分数阶混沌系统全状态自适应控制[J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发