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• Impact of stocking density of largemouth bass on the self-cleaning performance of a circular aquaculture tank

  Xiaoyu Qu, Yi Tao, Dezhen Li, Fuzhi Zu, Yanfei Wu, Fei Wang, Lianhui Wu, Jiajun Hu, Yang Gao, Dejun Feng , 2025年02月

  Aquaculture , 596 , 741770 - 741770

  DOI

• Sloshing-induced evolution of self-cleaning performance and flow characteristics within a tank installed on an offshore aquaculture vessel

  Dezhen Li, Yi Tao, Fuzhi Zu, Yanfei Wu, Fukun Gui, Yang Gao, Fei Wang, Lianhui Wu, Dejun Feng, Xiaoyu Qu , 2024年12月

  Ocean Engineering , 313 , 119438 - 119438

  DOI

• An automatic shoreline extraction method from SAR imagery using DeepLab-v3+ and its versatility

  Lianhui Wu, Sho Ishikawa, Daisuke Inazu, Tsuyoshi Ikeya, Akio Okayasu , 2024年08月

  Coastal Engineering Journal , 1 - 13

  DOI

• Investigation of Long-Term and Large-Scale Shoreline Evolution in West Africa Based on Optical Satellite Data

  Lianhui Wu, Yoshimitsu Tajima, Daiki Tsujio, Soraya Ono , 2024年06月

  Lecture Notes in Civil Engineering , 681 - 690

  DOI

• Medium-Range Forecast of Nearshore Wave Based on Global Wave Ensemble System Using Artificial Neural Network

  Muhong Yu, Lianhui Wu, Hirotaka Okumoto, Tsuyoshi Ikeya , 2024年06月

  Lecture Notes in Civil Engineering , 267 - 275

  DOI

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科研費（文科省・学振）獲得実績 【 表示 ／ 非表示 】

科研費（文科省・学振）獲得実績 【 表示 ／ 非表示 】

• Development of a high-resolution shoreline extraction technique by using convolutional neural network based on X-band SAR imagery

  研究期間:  2022年04月  -  2025年03月  代表者:  吴連慧

  若手研究  研究代表者  22K14327 

  This research aims to develop a high-resolution shoreline extraction technique by using convolutional neural network (CNN) based on X-band SAR images. The developed technique is expected to improve our monitoring ability of the shoreline change for sustainable management of the coastal zone.

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