油圧ショベルにおける畳み込みオートエンコーダを用いた技量差異分析法の一提案

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/03/01

タイトル(英語): Study on Skill Analysis in a Hydraulic Excavator using Convolutional Autoencoder

著者名: 槇野　泰大（広島大学大学院先進理工系科学研究科），小熊　尚太（広島大学大学院先進理工系科学研究科），大野　修一（大阪公立大学大学院情報学研究科），岩崎　和宏（コベルコ建機(株)）

著者名(英語): Yasuhiro Makino (Graduate School of Advanced Science and Engineering, Hiroshima University), Shota Oguma (Graduate School of Advanced Science and Engineering, Hiroshima University), Shuichi Ohno (Graduate School of Informatics, Osaka Metropolitan University), Kazuhiro Iwasaki (KOBELCO Construction Machinery Co., Ltd.)

キーワード: オートエンコーダ，畳み込みニューラルネットワーク，油圧ショベル，技量評価_x000D_　　autoencoder，convolutional neural network，hydraulic excavator，skill assessment

要約(英語): In the construction industry, it is desirable to develop a system to easily judge skills of workers in order to effectively pass on the skills of skilled workers to unskilled workers. This report proposes a skill analysis method in hydraulic excavator operators using a convolutional autoencoder (CAE) that is capable of nonlinear mapping to low dimensionally space. CAE is trained with the operation data of a skilled operator to acquire characteristics of the skilled operator. Then, the operation data of an unskilled operator is input to the trained CAE to analyze the unskilled operator's skill. CAE detects operations of the unskilled operator containing features that differ from the operation of the skilled operator out of many operations. First, it is confirmed that CAE can save information of the operation data in a low dimensional space than principal component analysis that is a linear mapping for dimensionality reduction. Next, the result of the proposed method for the unskilled operator is shown. Effectiveness of the result is validated by comparing a few operation data of both operators detected by the proposed method.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.3 （2023） 特集：スマートシステムと計測・制御技術　－SDGsへの貢献－

本誌掲載ページ: 258-265 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/3/143_258/_article/-char/ja/