Optimized KPCA method for chemical vapor class recognition by SAW sensor array response analysis

Sunil Kr Jha, Kenshi Hayashi

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

5 被引用数 (Scopus)

抄録

This paper confirms the suitability of kernel principal component analysis (KPCA) as a robust feature extraction and denoising method in sensor array based vapor detection system (E-nose). Particularly the study focuses on response analysis of surface acoustic wave (SAW) sensor array in chemical class recognition of volatile organic compounds (VOCs). Usually KPCA results deprived performance compare to linear feature extraction methods. However its performance is affected by the proper selection of kernel function and optimization of allied parameters. We have presented the comparative performance analysis of feature vectors extracted by KPCA method using five types of kernel functions in combination with support vector machine (SVM) classifier. Study outcomes are based on analysis of 12 data sets (enclosing different intensity of additive noise and outliers) generated with SAW sensor model simulator. We find that in research of kernel function selection; polynomial kernel achieves persistently maximum class recognition rate of VOCs (average 82 %) even in presence of high level of additive Gaussian noise and outlier and anova kernel results minimum class recognition rate (average 70 %). The class recognition efficiency of feature vectors extracted by rest of the three kernel functions lies in between these two.

本文言語英語
ホスト出版物のタイトルIEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
出版社IEEE Computer Society
ISBN(印刷版)9781479928439
DOI
出版ステータス出版済み - 2014
イベント9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, シンガポール
継続期間: 4月 21 20144月 24 2014

出版物シリーズ

名前IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

その他

その他9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
国/地域シンガポール
CitySingapore
Period4/21/144/24/14

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 情報システム

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