NAKATA Masaya

Organization

Faculty of Engineering, Division of Intelligent Systems Engineering

Title

Associate Professor

Date of Birth

1988

Research Fields, Keywords

Evolutionary Computation, Machine Learning

Mail Address

E-mail address

Homepage URL

http://www.nkt.ynu.ac.jp/en/



Graduating School 【 display / non-display

  • 2009.04
    -
    2011.03

    The University of Electro-Communications   Faculty of Electro Communications   Department of Human Communication   Graduated

Graduate School 【 display / non-display

  • 2013.04
    -
    2015.09

    The University of Electro-Communications  Graduate School of Informatics and Engineering  Department of Informatics  Doctor Course  Completed

  • 2011.04
    -
    2013.03

    The University of Electro-Communications  Graduate School of Informatics and Engineering  Department of Informatics  Master Course  Completed

Degree 【 display / non-display

  • Doctor (Engineering) - 

  • Master(Engineering) - 

External Career 【 display / non-display

  • 2015.10
    -
    2016.03

    Japan Society for the Promotion of Science   The University of Electro-Communications   Research Fellowship for Young Scientists of the Japan Society for the Promotion of Science  

  • 2013.04
    -
    2015.09

    Japan Society for the Promotion of Science   The University of Electro-Communications   Research Fellowship for Young Scientists of the Japan Society for the Promotion of Science  

Field of expertise (Grants-in-aid for Scientific Research classification) 【 display / non-display

  • Soft computing

  • Intelligent informatics

 

Research Career 【 display / non-display

  • Evolutionary Rule-based Machine Learning: Theory and Algorithm

    Project Year: 2016.04  -   

Thesis for a degree 【 display / non-display

  • Learning Classifier System Design based on Learning Strategy

    Masaya Nakata 

    PhD Thesis, The University of Electro-Communications    2016.09

    Doctoral Thesis   Single Work

Papers 【 display / non-display

  • Theoretical adaptation of multiple rule-generation in XCS

    Masaya Nakata, Will Browne, Tomoki Hamagami

    Proceedings of the Genetic and EVolutionary Computation Conference 2018 (GECCO2018) ( ACM )    482 - 489   2018.07  [Refereed]

    Single Work

    DOI

  • Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem

    Journal of Control, Measurement and System Integration ( Society of Instrument and Control Engineers )  11 ( 3 )   321 - 220   2018.06  [Refereed]

    Joint Work

    DOI

  • An Empirical Analysis of Action Map in Learning Classifier Systems

    Journal of Control, Measurement and System Integration ( Society of Instrument and Control Engineers )  11 ( 3 )   239 - 248   2018.05  [Refereed]

    Joint Work

    DOI CiNii

  • Determination of Temporal and Spatial Origination of Transonic Buffet via Unsteady Data Mining

    Kazuhisa Chiba, Yuhei Umeda, Naoki Hamada, Shinya Watanabe, Masaya Nakata, Kanako Yasue, Koji Suzuki, Takashi Atobe, Shigeru Kuchiishi, Kazuyuki Nakatakita and Ken Ito

    Proceedings of the 56th AIAA Aerospace Sciences Meeting 2018 (AIAA2018)     2018.01  [Refereed]

    Single Work

    DOI

  • Effect of parameter sharing for multimodal deep autoencoders

    Hayato Sasaki,Masaya Nakata,Fumiya Hamatsu,Tomoki Hamagami

    Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics 2017 (SMC2017) ( IEEE )    1966 - 1971   2017.10  [Refereed]

    Joint Work

    DOI

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Academic Awards Received 【 display / non-display

  • IEEE CIS Japan Chapter Young Researcher Award

    2012.12.16   IEEE Computational Intelligence Society Japan Chapter  

    Winner: Masaya Nakata

Grant-in-Aid for Scientific Research 【 display / non-display

  • Grant-in-Aid for Early-Career Scientists

    Project Year: 2018.04  -  2020.03 

Presentations 【 display / non-display

  • Design informatics and its drive:Toward clarifying unsteady physical phenomena

    CHIBA Kazuhisa, ITO Takeshi, WATANABE Shinya, NAKATA Masaya, UMEDA Yuhei, HAMADA Naoki, YASUE Kanako, SUZUKI Koji, KUCHI-ISHI Shigeru, NAKAKITA Kazuyuki

    The Proceedings of Mechanical Engineering Congress, Japan  2017   The Japan Society of Mechanical Engineers

     View Summary

    <p>The transonic buffet degrades the aerodynamic performance of the aircraft during cruise. It is a phenomenon that should be avoided absolutely as it may lead to accidents. However, the mechanism of occurrence has yet to be elucidated. To understand this phenomenon, large-scale unsteady data is accumulated using computational fluid dynamics. In contrast, data mining of time series data such as unsteady data is a topic of the future in that field. In this study, we attempted mining unsteady data with capacity exceeding Tera's order. As a result, the behavior of the physical quantity is suggested to be different from the data just before the transonic buffet occurs. Based on this result, we visualized the data over time, and found that the characteristic change of the viscosity distribution of the wing surface can be seen. This should be a clue to elucidate this phenomenon.</p>

    DOI CiNii