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Generalized Net Model of the Network for Automatic Turning and Setting the Lighting in the Room with Intuitionistic Fuzzy Estimations

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Book cover Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

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Abstract

Turning the lights on automatically helps people who enter a current room to turn on the lighting depending on the settings made by a person. In this paper we will take a look how by scanning the iris the system recognizes the person who is entering the room depending on if the the outside light exceeds the set in advance threshold value, the lighting turns on with the settings for lighting of a specific person and it also lets corrections to be made in the set in advance parameters.

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Acknowledgements

This work was supported by the Bulgarian Ministry of Education and Science under the National Research Programme “Information and Communication Technologies for a Digital Single Market in Science, Education and Security” approved by DCM # 577/17.08.2018.

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Authors and Affiliations

  1. “Prof. Dr, Assen Zlatarov” University, “Prof. Yakimov” Blvd, Burgas, 8010, Bulgaria

    Tihomir Videv, Sotir Sotirov & Boris Bozveliev

Authors
  1. Tihomir Videv
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  2. Sotir Sotirov
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  3. Boris Bozveliev
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Corresponding author

Correspondence to Tihomir Videv .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Videv, T., Sotirov, S., Bozveliev, B. (2020). Generalized Net Model of the Network for Automatic Turning and Setting the Lighting in the Room with Intuitionistic Fuzzy Estimations. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_7

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