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Clustering of Pressure Fluctuation Data Using Self-Organizing Map

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Engineering Applications of Neural Networks (EANN 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 43))

Abstract

The batch Self-Organizing Map (SOM) is applied to clustering of pressure fluctuation in liquid-liquid flow inside a microchannel. When time-series data of the static pressure are computed by the SOM, several clusters of pressure fluctuation with different amplitudes are extracted in the visible way. Since the signal composition of the fluctuation is considered to change with flow rates of the water and the organic solvent, the ratio to the each cluster, which is estimated by the recalling, is classified by using the SOM. Consequently, the operating condition of flow rate is classified to three groups, which indicate characteristic behavior of interface between two flows in the microchannel. Furthermore, predictive performance for behavior of the interface is demonstrated to be good by the recalling.

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© 2009 Springer-Verlag Berlin Heidelberg

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Ogihara, M., Matsumoto, H., Marumo, T., Kuroda, C. (2009). Clustering of Pressure Fluctuation Data Using Self-Organizing Map. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_5

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  • DOI: https://doi.org/10.1007/978-3-642-03969-0_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03968-3

  • Online ISBN: 978-3-642-03969-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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