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Sediment recognition by warp tension monitoring of bottom otter trawling and applying the self-organizing map algorithm
https://oacis.repo.nii.ac.jp/records/2536
https://oacis.repo.nii.ac.jp/records/25366530f6c7-1c5a-4708-a105-0f3bd6f88359
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-09-26 | |||||
タイトル | ||||||
タイトル | Sediment recognition by warp tension monitoring of bottom otter trawling and applying the self-organizing map algorithm | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | Bottom otter trawl, Seabed impact, Off-bottom trawling, Sediment recognition, Self-organizing map | |||||
資源タイプ | ||||||
資源タイプ | journal article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
著者 |
You, Xinxing
× You, Xinxing× Kumazawa, Taisei× Ito, Sho× Hattori, Ren× Yu, Hongyuan× Shiode, Daisuke× HU, Fuxiang |
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書誌情報 |
Ocean Engineering 巻 236, 号 15, p. 109455, 発行日 2021-09-15 |
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抄録 | ||||||
内容記述 | Model towing experiments of a bottom trawl net with hyper-lift trawl door were conducted to investigate the effect of the bottom sediment (concrete, sand, gravel, and rock) on the warp tension of the overall trawl system. The towing speed was from 50 cm/s to 70 cm/s and the ratio of warp length relative to the water depth was within the range of 4–6. Through the signal analysis of time-series warp tension, results reveal that there is a significant dependence of the warp tension on the type of bottom sediment, and the oscillation of warp tension in a frequency range of 1–10 Hz increases in the order of concrete, sand, gravel, and rock. Based on these characterizations, the time-series warp tension is thus represented by the feature vector for the input data of the self-organizing map (SOM) and learning vector quantization (LVQ) neural networks. A clustering method with an unsupervised SOM neural network acting as an updating tool for the bottom sediment database was successfully built using the validation of the prepared sediments. In combination with the output vector of labeled bottom sediment, the supervised LVQ neural network for sediment recognition performed excellently with a high classification accuracy of over 80%. |
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内容記述 | ||||||
内容記述 | 公開日: 2023-07-09 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0029-8018 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA00762322 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1016/j.oceaneng.2021.109455 | |||||
権利 | ||||||
権利情報 | (c) 2021 Elsevier B.V.. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in https://doi.org/10.1016/j.oceaneng.2021.109455 | |||||
情報源 | ||||||
関連識別子 | https://www.sciencedirect.com/science/article/am/pii/S0029801821008593 | |||||
関連名称 | Author's Accepted Manuscript Version/PDF (Available on: 2023-07-09) | |||||
情報源 | ||||||
関連識別子 | https://www.elsevier.com/ja-jp | |||||
関連名称 | Elsevier B.V. | |||||
出版者 | ||||||
出版者 | Elsevier B.V. |