HAMAGAMI Tomoki

Affiliation

Faculty of Engineering, Division of Intelligent Systems Engineering

Job Title

Professor

Date of Birth

1966

Research Fields, Keywords

distributed autonomous system, distributed intelligent system, multiagent, welfare system

Mail Address

E-mail address

Related SDGs




写真a

Graduating School 【 display / non-display

  •  
    -
    1988

    Chiba University   Faculty of Engineering   Graduated

Graduate School 【 display / non-display

  •  
    -
    1999

    Chiba University  Graduate School, Division of Science and Technology    Completed

Degree 【 display / non-display

  • Doctor of Engineering -  Chiba University

Campus Career 【 display / non-display

  • 2008.10
    -
    Now

    Duty   Yokohama National UniversityFaculty of Engineering   Division of Intelligent Systems Engineering   Professor  

  • 2007.04
    -
    2008.09

    Duty   Yokohama National UniversityFaculty of Engineering   Division of Intelligent Systems Engineering   Associate Professor  

  • 2004.04
    -
    2007.03

    Duty   Yokohama National UniversityFaculty of Engineering   Division of Intelligent Systems Engineering   Associate Professor  

  • 2018.04
    -
    Now

    Concurrently   Yokohama National UniversityGraduate school of Engineering Science   Department of Mathematics, Physics, Electrical Engineering and Computer Science   Professor  

  • 2011.04
    -
    Now

    Concurrently   Yokohama National UniversityCollege of Engineering Science   Department of Mathematics, Physics, Electrical Engineering and Computer Science   Professor  

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External Career 【 display / non-display

  • 2001.01
    -
    2004.03

    Chiba University   Graduate School of Science and Technology   Research Assistant  

  • 1988.04
    -
    2000.12

    SECOM IS Laboratory, SECOM Co.,LTD   Researcher  

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

  • Intelligent informatics

  • Control engineering/System engineering

  • Software Design

 

Papers 【 display / non-display

  • Wave-making Resistance Estimation Through Deep Learning Considering the Distribution of Ship Figure

    Li Xin, Arai Hiroshi, Hamagami Tomoki

    IEEJ Transactions on Electronics, Information and Systems ( The Institute of Electrical Engineers of Japan )  140 ( 3 )   391 - 397   2020.03  [Refereed]

    Joint Work

     View Summary

    <p>A method for the estimation of wave-making resistance from the hull form and Froude number through deep learning is proposed. At the same time, this research also gives a solution when the data are skewed, which solves the problem of low generalization performance. The reduction of wave-making resistance is an essential issue in hull form design. However, the estimation of wave-making resistance is a time-consuming task that depends on experimental measurements. To enable direct estimation of the wave resistance from hull form, deep learning, which enables end-to-end learning, is an effective approach. The proposed method has two phases. First, auto-encoders, which reduce the dimension of the offset and the profile data, are generated, while performing to the skewed offset data, use an improved sampling method. Subsequently, after the regularization of these data, a deep neural net for regression estimation of wave-making resistance is generated. The results of evaluation experiments show that the proposed method can estimate wave-making resistance with high precision.</p>

    DOI CiNii

  • Block-Based Neural Network Optimization with Manageable Problem Space

    Lee Kundo, Hamagami Tomoki

    電気学会論文誌C(電子・情報・システム部門誌) ( 一般社団法人 電気学会 )  140 ( 1 )   68 - 74   2020.01  [Refereed]

    Joint Work

     View Summary

    <p>In this paper, a simple method based on Genetic Algorithm (GA) is proposed to evolve Block-Based Neural Network (BbNN) model. A BbNN consists of a 2-D array of memory-based modular component NNs with flexible structures and internal configuration that can be implemented in reconfigurable hardware such as a field programmable gate array (FPGA). The network structure and the weights are encoded in bit strings and globally optimized using the genetic operators. Asynchronous BbNN (ABbNN), which is a new model of BbNN, suggests high-performance BbNN by utilizing parallel computation and pipeline architecture. ABbNN's operating frequency is stable for all scales of the network, while conventional BbNN's is decreasing according to the network size. However, optimization by the genetic algorithm requires more iterations to find a solution with increasing problem space and the memory access in GA operation is one of the causes degrading the performance. ABbNN optimized with the proposed evolutionary algorithm is applied on general classifiers to verify the effectiveness with increasing problem space. The proposed method is confirmed by experimental investigations and compared with the conventional genetic algorithm.</p>

    DOI CiNii

  • Brock-Based Neural Network High Speed Optimization

    Kundo Lee, Tomoki Hamagami

    Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Ebolutionary Systemus   12   79 - 92   2019.12  [Refereed]

    Joint Work

    DOI

  • Sperm Detection with Robustness to Overlap of Distributions by using Adaptive Thresholded Boosting

    Sasaki Hayato, Yamamoto Mizuki, Takeshima Teppei, Yumura Yasushi, Hamagami Tomoki

    IEEJ Transactions on Electronics, Information and Systems ( The Institute of Electrical Engineers of Japan )  139 ( 12 )   1461 - 1467   2019.12  [Refereed]

    Joint Work

     View Summary

    <p>Automatic sperm detection is in high demand for supporting Testicular Sperm Extraction (TESE). On the other hand, detection of sperms in samples of TESE is difficult because there are a lot of germ cells resembling sperms. This paper realizes automatic sperm detection for TESE by using Adaptive Thresholded Boosting (ATBoost) which is robust to overlap of feature distributions between positive samples and negative samples. In this paper, we evaluated our sperm detection method in two stages from the view point of robustness to the overlap. First, we quantitatively evaluated the overlap of the feature distributions in TESE in the metric of Bayes error rate. Second, we evaluated robustness of our sperm detection method as for the overlap. These two results show that our sperm detection method is very effective for TESE.</p>

    DOI CiNii

  • An Improved Auto-encoder Based on 2-Level Prioritized Experience Replay for High Dimension Skewed Data

    Xin Li, Tomoki Hamagami

    Symposium on Intelligent and Evolutionary Systems   12   93 - 105   2019.12  [Refereed]

    Joint Work

    DOI

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

  • Grant-in-Aid for Scientific Research(B)

    Project Year: 2019.04  -  2022.03 

  • Grant-in-Aid for Scientific Research(C)

    Project Year: 2017.04  -  2020.03 

  • Grant-in-Aid for Scientific Research(B)

    Project Year: 2013.04  -  2017.03 

  • Grant-in-Aid for Scientific Research(C)

    Project Year: 2010.04  -  2013.03