Updated on 2025/03/30

写真a

 
KANMA Koji
 
Name of department
Faculty of Systems Engineering, Intelligent Informatics
Job title
Lecturer
Concurrent post
Informatics Division(Lecturer)
External link

Education

  • 2018
    -
    2021

    Wakayama University   Graduate School of Systems Engineering  

  • 2012
    -
    2014

    The University of Tokyo   The Graduate School of Engineering   Department of Chemical System Engineering  

  • 2007
    -
    2012

    The University of Tokyo   The Faculty of Engineering   Department of Chemical System Engineering  

Degree

  • Ph.D. (Engineering)   2021

Academic & Professional Experience

  • 2024.04
    -
    Now

    Wakayama University   Faculty of Systems Engineering   Lecturer

  • 2022.07
    -
    2024.03

    Wakayama University   Faculty of Systems Engineering   Project Assistant Professor

  • 2020.04
    -
    2022.04

    Japan Society for the Promotion of Science   特別研究員

Association Memberships

  • -
    Now

    IEICE

Research Areas

  • Informatics / Perceptual information processing / Machine Learning

  • Informatics / Perceptual information processing / Computer Vision

Research Interests

  • machine learning

  • deep learning

Published Papers

  • Pruning based on Activation Function Outputs for Efficient Neural Networks

    Koji Kamma, Toshikazu Wada (Part: Lead author )

    IEEE International Conference on Systems, Man, and Cybernetics(SMC) ( IEEE )    5040 - 5045   2023.10

    DOI

  • Pruning with Output Error Minimization for Compressing Deep Neural Networks

    Koji Kamma, Toshikazu Wada

    MIRU2023     2023.07

  • Pruning Ratio Optimization with Layer-Wise Pruning Method for Accelerating Convolutional Neural Networks.

    Koji Kamma, Sarimu Inoue, Toshikazu Wada

    IEICE Transactions on Information & Systems   105-D ( 1 ) 161 - 169   2022

    DOI

  • REAP: A Method for Pruning Convolutional Neural Networks with Performance Preservation.

    Koji Kamma, Toshikazu Wada

    IEICE Transactions on Information & Systems   104-D ( 1 ) 194 - 202   2021

    DOI

  • Neural Behavior-Based Approach for Neural Network Pruning.

    Koji Kamma, Yuki Isoda, Sarimu Inoue, Toshikazu Wada

    IEICE Transactions on Information & Systems   103-D ( 5 ) 1135 - 1143   2020

    DOI

  • Reconstruction Error Aware Pruning for Accelerating Neural Networks.

    Koji Kamma, Toshikazu Wada

    Advances in Visual Computing - 14th International Symposium on Visual Computing ( Springer )    59 - 72   2019

    DOI

  • Behavior-Based Compression for Convolutional Neural Networks.

    Koji Kamma, Yuki Isoda, Sarimu Inoue, Toshikazu Wada

    Image Analysis and Recognition - 16th International Conference ( Springer )    427 - 439   2019

    DOI

  • Development of a Novel Spectra Analysis Method to Construct Accurate NIR Models

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Journal of Computer Aided Chemistry ( Division of Chemical Information and Computer Sciences The Chemical Society of Japan )  15   1 - 9   2014

     View Summary

    Near-infrared spectroscopy (NIR) is widely used for non-destructive food quality check. The prediction models are constructed between NIR spectra and quality parameters. However, because of the noise included in spectra and the duplication between the peaks of the target components and those of the other components, the prediction accuracy of the models decreases. To avoid this problem, derivative spectra are used in modeling. Derivation of spectra has an effect to emphasize the small and narrow peaks so that the affection of peak duplication decreases. On the other hand, derivation of spectra also has an effect to enlarge the noise. The impacts of these effects change as the derivative changes, hence it is necessary to select the adequate derivative for each data. Besides, if there are several peaks of the target components, the adequate derivative is different for each peak. In this paper, we therefore construct regression models using the spectra, the first, second and third derivative spectra, and the combinations of them. The accuracy of the models which are constructed with different derivative spectra or the combinations of them changes when the number of the training data changes. Thus, we proposed a method to select the proper model according to the number of the training data. The selection is performed based on the prediction accuracy of each model. A simulation data set that mimics the spectra where three different peaks duplicate was analyzed using the proposed method. Then, the proposed method was applied to sugar content prediction of oranges. The results showed that the most accurate model changed as the number of the training data changed, and that the effectiveness of the proposed method was proved.

    DOI

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Misc

  • Disentangle Learning for Interactive Semiconductor Inspection Robust to Thin-Film Interference

    光定幸嬉, 菅間幸司, 和田俊和, 濱田康弘, 島田佳典, 野口威, 岡山敏之

    電子情報通信学会技術研究報告(Web)   124 ( 281(PRMU2024 8-32) )   2024

  • A method for estimating vanishing points from fisheye camera images for autonomous driving

    藤原竜太, 菅間幸司, 和田俊和

    情報処理学会研究報告(Web)   2024 ( CVIM-238 )   2024

  • DN4Cを用いた薄膜干渉による濃度ムラ虚報へ対応可能な半導体ウェハ検査手法

    濱田康弘, 島田佳典, 野口威, 岡山敏之, 光定幸嬉, 菅間幸司, 和田俊和

    精密工学会大会学術講演会講演論文集(CD-ROM)   2024   2024

  • FOE estimation from fisheye camera images for automated driving.

    重藤瞭介, 菅間幸司, 和田俊和

    情報処理学会研究報告(Web)   2024 ( CVIM-238 )   2024

  • カラーステレオカメラを用いたミニトマトの生育診断手法の開発

    江川紘輝, 田中寿弥, 花田裕美, 菅間幸司, 和田俊和

    電子情報通信学会技術研究報告(Web)   123 ( 409(PRMU2023 51-81) )   2024

  • A context-dependent character sequence recognition based on ambiguous recognition expression

    木本舟, 菅間幸司, 和田俊和

    電子情報通信学会技術研究報告(Web)   123 ( 266(PRMU2023 15-38) )   2023

  • An image based barcode reading using Hough transform and 1-D super resolution

    江川紘輝, 菅間幸司, 和田俊和

    電子情報通信学会技術研究報告(Web)   123 ( 266(PRMU2023 15-38) )   2023

  • DNNモデルの圧縮法-効率的なAIの実現に向けて

    菅間幸司

    電子情報通信学会大会講演論文集(CD-ROM)   2023   2023

  • DN4C: An Interactive Image Segmentation System Combining Deep Neural Network and Nearest Neighbor Classifier

    和田俊和, 菅間幸司

    電子情報通信学会技術研究報告(Web)   122 ( 314(PRMU2022 32-57) )   2022

  • Convolutional Skip Connection for Compressing DNNs with Branched Architectures

    菅間幸司, 和田俊和

    電子情報通信学会技術研究報告(Web)   122 ( 181(PRMU2022 10-21) )   2022

  • GC×GC/MSとQSRR逆解析モデルを用いた未知化合物の構造推定法の開発

    佐藤幸司, 加藤克己, 吉田和之, 船津公人, 金子弘昌, 菅間幸司

    質量分析総合討論会講演要旨集   62nd   2014

  • GC×GC/MSとQSRR逆解析モデルを用いた未知化合物の構造推定法の開発

    佐藤幸司, 加藤克己, 吉田和之, 船津公人, 金子弘昌, 菅間幸司

    日本分析化学会年会講演要旨集   63rd   2014

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Awards & Honors

  • PRMU研究会ベストプレゼンテーション賞

    2022.12    

  • PRMU研究奨励賞

    2021.05    

  • 情報処理学会2020年度研究会推薦論文

    2021.05    

  • ICIAR2019 best paper award

    2019.08    

  • MIRU学生奨励賞

    2019.07    

Conference Activities & Talks

  • DNN モデルの圧縮法 - 効率的な AI の実現に向けて

    菅間幸司

    電子情報通信学会総合大会  2023.03.07  

Patents

  • ニューラルネットワーク処理装置、ニューラルネットワーク処理方法、及びコンピュータプログラム

    Patent no: 特許第7533933号

    Date registered: 2024.08.05 

    Date applied: 2020.07.20 ( 特願2020-123973 )   Publication date: 2022.02.01 ( 特開2022-20464 )  

    Inventor(s)/Creator(s): 和田俊和、菅間幸司  Applicant: 国立大学法人和歌山大学

  • ニューラルネットワーク処理装置、コンピュータプログラム、ニューラルネットワーク製造方法、ニューラルネットワークデータの製造方法、ニューラルネットワーク利用装置、及びニューラルネットワーク小規模化方法

    Patent no: 特許第7438544号

    Date registered: 2024.02.16 

    Date applied: 2019.08.28 ( 特願2020-546831 )   Public disclosure date: 2020.03.19 ( WO2020/054402 )

    Inventor(s)/Creator(s): 和田俊和、菅間幸司、磯田雄基  Applicant: 国立大学法人和歌山大学

  • ニューラルネットワークの圧縮方法、ニューラルネットワーク圧縮装置、コンピュータプログラム、及び圧縮されたニューラルネットワークデータの製造方法

    Patent no: 特許第7438517号

    Date registered: 2024.02.16 

    Date applied: 2019.07.25 ( 特願2019-137019 )   Publication date: 2021.02.18 ( 特開2021-022050 )  

    Inventor(s)/Creator(s): 和田俊和、菅間幸司  Applicant: 国立大学法人和歌山大学

  • ニューラルネットワークの圧縮のためにコンピュータによって実行される方法、ニューラルネットワーク圧縮装置、コンピュータプログラム、及び圧縮されたニューラルネットワークデータの製造方法

    Date applied: 2024.06.14 ( 特願2024-096813 )  

    Inventor(s)/Creator(s): 菅間幸司  Applicant: 国立大学法人和歌山大学

  • 画像処理装置

    Date applied: 2022.12.07 ( 特願2022-195687 )  

    Inventor(s)/Creator(s): 和田俊和、菅間幸司、岡山敏之、野口 威、島田佳典 

KAKENHI

  • DNNアーキテクチャの対称性破れを利用した効率的学習法の開発

    2024.04
    -
    2026.03
     

    Grant-in-Aid for Early-Career Scientists  Principal investigator

Joint or Subcontracted Research with foundation, company, etc.

  • AIを利用した画像解析によるミニトマトの生育診断技術の開発

    2024.06
    -
    2025.02
     

    Joint research  Co-investigator

  • 商品棚画像の解析及び精度向上に関する研究

    2024.04
    -
    2025.03
     

    Joint research  Co-investigator

  • Deep Learningを用いた光学検査装置の新規ソリューションの開発

    2024.04
    -
    2025.03
     

    Joint research  Co-investigator

  • 画像解析によるミニトマトの生育診断手法の開発

    2023.04
    -
    2024.02
     

    Joint research  Co-investigator