Kullback-Leibler divergence based kernel SOM for visualization of damage process on fuel cells

Ken Ichi Fukui, Kazuhisa Sato, Junichiro Mizusaki, Masayuki Numao

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

The present work developed a basis to explore numerous damage events utilizing Self-Organizing Map (SOM) introducing Kullback-Leibler (KL) divergence as an appropriate similarity for frequency spectra of damage events. Firstly, we validated the use of KL divergence to frequency spectra of damage events. The experiment using the datasets of damage related sounds showed that the kernel SOM using KL kernel generates accurate cluster map compared to using general kernel functions and the standard SOM. Afterward, we demonstrated our approach can clarify damage process of Solid Oxide Fuel Cells (SOFC) from acoustic emission (AE) events observed by damage test of SOFC. The damage process was inferred by occurrence frequency of AE events upon the cluster map of SOM, where the occurrence density change was obtained by kernel density estimation (KDE). The presented approach can be a common foundation for the domain experts to clarify fracture mechanism of SOFC and/or to monitor SOFC operation.

本文言語English
ホスト出版物のタイトルProceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
ページ233-240
ページ数8
DOI
出版ステータスPublished - 2010 12 1
イベント22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010 - Arras, France
継続期間: 2010 10 272010 10 29

出版物シリーズ

名前Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
1
ISSN(印刷版)1082-3409

Other

Other22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
国/地域France
CityArras
Period10/10/2710/10/29

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能
  • コンピュータ サイエンスの応用

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