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Personal Peculiarity Classification of Flat Finishing Motion for Skill Training by Using Expanding Self-Organizing Maps

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Distributed Computing and Artificial Intelligence, 13th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 474))

Abstract

The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The classified personal peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. An expanding Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns.

Experimental results of the classification with measured data of an expert and sixteen learners show effectiveness of the proposed method.

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References

  1. Matsumoto, S., Fujimoto, N., Teranishi, M., Takeno, H., Tokuyasu, T.: A brush coating skill training system for manufacturing education at Japanese elementary and junior high schools. Artificial Life and Robotics 21, 69–78 (2016)

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Author information

Authors and Affiliations

  1. Hiroshima Institute of Technology, Hiroshima, Japan

    Masaru Teranishi, Shinpei Matsumoto, Nobuto Fujimoto & Hidetoshi Takeno

Authors
  1. Masaru Teranishi
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  2. Shinpei Matsumoto
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  3. Nobuto Fujimoto
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  4. Hidetoshi Takeno
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Corresponding author

Correspondence to Masaru Teranishi .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Teranishi, M., Matsumoto, S., Fujimoto, N., Takeno, H. (2016). Personal Peculiarity Classification of Flat Finishing Motion for Skill Training by Using Expanding Self-Organizing Maps. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40161-4

  • Online ISBN: 978-3-319-40162-1

  • eBook Packages: EngineeringEngineering (R0)

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