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アイテム

  1. 研究者名(五十音順)
  2. 渡邉 真也(WATANABE Shinya)
  1. 学術雑誌論文

Estimation of floc condition in a dewatering process by image analysis using machine learning

http://hdl.handle.net/10258/0002000367
http://hdl.handle.net/10258/0002000367
aebbf65d-85ae-4bab-87dd-1731ab50140d
名前 / ファイル ライセンス アクション
AROB_journal_revised.pdf AROB_journal_revised.pdf (944 KB)
アイテムタイプ 学術雑誌論文 / Journal Article.(1)
公開日 2025-07-24
書誌情報 en : Artificial Life and Robotics

発行日 2025-03-24
エンバーゴ
日付 2026-03-24
日付タイプ Available
タイトル
タイトル Estimation of floc condition in a dewatering process by image analysis using machine learning
言語 en
言語
言語 eng
キーワード
言語 en
主題Scheme Other
主題 dewatering process
キーワード
言語 en
主題Scheme Other
主題 floc
キーワード
言語 en
主題Scheme Other
主題 texture
キーワード
言語 en
主題Scheme Other
主題 explainable boosting machine
キーワード
言語 en
主題Scheme Other
主題 convolutional neural network
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
アクセス権
アクセス権 embargoed access
アクセス権URI http://purl.org/coar/access_right/c_f1cf
著者 Fukasawa, Atsuki

× Fukasawa, Atsuki

en Fukasawa, Atsuki

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渡邉, 真也

× 渡邉, 真也

ja 渡邉, 真也

en WATANABE, Shinya

ja-Kana ワタナベ, シンヤ


Search repository
抄録
内容記述タイプ Abstract
内容記述 Dewatering is a crucial process in sludge treatment plants, and appropriate mixing of polymer and sludge is an important factor in achieving efficient dewatering. This study focused on the condition of flocs produced by mixing sludge and polymer, and estimated the floc condition through visual analysis of images. In this study, the estimation of floc condition was assumed to be a classification problem of mixer speed, and validation was conducted to classify the appropriate speed based on the images. The proposed methodology involved the development of a machine learning model characterized by high accuracy and transparency. This model was formulated using two features extracted from the images, i.e., the gaps between flocs and their texture, which are the parameters used by human operators to estimate floc condition. Explainable Boosting Machine was used as the machine learning model, which allows interpretation of the model’s contents and can be applied easily. The classification accuracy of this model was validated using both interpolated and extrapolated data, yielding accuracies exceeding 95% in both scenarios. Furthermore, comparative analysis was performed between the proposed transparent box model and a conventional Convolutional Neural Network (CNN) model. Despite its transparent box nature, the proposed approach demonstrated a comparable level of accuracy to the CNN model in this comparative study.
言語 en
出版者
出版者 Springer Nature
言語 en
DOI
識別子タイプ DOI
関連識別子 10.1007/s10015-025-01014-4
ISSN
収録物識別子タイプ PISSN
収録物識別子 1433-5298
ISSN
収録物識別子タイプ EISSN
収録物識別子 1614-7456
権利
権利情報 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10015-025-01014-4
言語 en
著者版フラグ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
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