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A Cooperative Unsupervised Connectionist Model to Identify the Optimal Conditions of a Pneumatic Drill

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Soft Computing as Transdisciplinary Science and Technology

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

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Abstract

A novel connectionist method to feature selection is proposed in this paper to identify the optimal conditions to perform drilling tasks. The aim is to extract information from complex high dimensional data sets. The model used is based on a family of cost functions which maximizes the likelihood of identifying a specific distribution in a data set. It employs lateral connections derived from the Rectified Gaussian Distribution to enforce a more sparse representation in each weight vector. The data investigated is obtained from the sensors allocated in a robot used to drill and build industrial warehouses. It is hoped that in classifying this data related with the strength, the water volume for refrigerating, speed and time of each sample, it will help in the search of the best conditions to perform the drilling of reinforce concrete slabs. This would produce a great saving for the company which owns the drilling robot.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Emilio Corchado, Leticia Curiel & Pedro Bravo

Authors
  1. Emilio Corchado
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  2. Leticia Curiel
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  3. Pedro Bravo
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Oklahoma State University, North Greenwood Avenue 700, 74106, Tulsa, USA

    Ajith Abraham

  2. Dept. of Computer Science and Systems Engineering, Muroran Institute of Technology, Mizumoto-cho 27-1, 050-8585, Muroran, Hokkaido, Japan

    Yasuhiko Dote

  3. Dept. of Computational Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi-ken, Japan

    Takeshi Furuhashi

  4. Institut für Sicherheits-/Prüftechnik, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  5. Division of Complex Systems Technology Graduate School of Engineering, University of Hokkaido, N-13, W-8 060 KITA-KU, Sapporo, Japan

    Azuma Ohuchi

  6. PRESTO Japan Science & Technology Corp. GSSM, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 12-0012, Tokyo, Japan

    Yukio Ohsawa

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© 2005 Springer-Verlag Berlin Heidelberg

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Corchado, E., Curiel, L., Bravo, P. (2005). A Cooperative Unsupervised Connectionist Model to Identify the Optimal Conditions of a Pneumatic Drill. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_77

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  • DOI: https://doi.org/10.1007/3-540-32391-0_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

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