Influence of Complementary Labeled Data on Judgement Results for Loading History of RC Beams by Hammering Sounds Using Neural Network and Its Removal Method

DOI
  • FUKUI Tomohiro
    防衛大学校理工学研究科 装備・基盤工学系専攻
  • MORITO Yuichi
    防衛大学校理工学研究科 地球環境科学専攻
  • KURODA Ichiro
    防衛大学校教授 システム工学群建設環境工学科

Bibliographic Information

Other Title
  • NNを用いた打音によるRC梁部材の載荷履歴判定結果に及ぼす誤ラベルデータの混入の影響とその除去方法

Abstract

<p>The purpose of this study is to investigate the influence of complementary labeled data in the training data set on the judgment results of the load histories of RC beam members by hammering sounds using a neural network model. In addition, the applicability of the method of removing complementary labeled data using the local outlier factor method was examined. As a result, it was confirmed that the true positive rate tended to decrease when complementary labeled data was included compared to when there was no complementary labeled data. It was also indicated that most complementary labeled data can be removed by using the local outlier factor method. Furthermore, it was confirmed that the true positive rate tended to recover to the same level as the case without complementary labeled data in the judgment using the training data set after removal using the local outlier factor method.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390298124265616640
  • DOI
    10.11532/jsceiii.4.3_90
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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