English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26988/38789
Visitors : 2357793      Online Users : 31
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25462

Title: Wave measurements by pressure transducers using artificial neural networks
Authors: Jen-Chih Tsai;Cheng-Han Tsai
Contributors: NTOU:Department of Marine Environmental Informatics
國立臺灣海洋大學:海洋環境資訊系
Keywords: Artificialneuralnetwork;Pressure transfer function;Wave parameters;Pressure gauge;Ocean wavemeasurement
Date: 2009-11
Issue Date: 2011-10-20T08:23:18Z
Publisher: Ocean Engineering
Abstract: abstract:Underwater ultrasonic acoustic transducers are frequently used in ocean wave measurements, as they measure surface level using acoustic waves. However, their effectiveness could be severely affected in rough sea conditions, in which breaking waves generated bubbles interfere with their acoustic signals. Therefore, when the seas are rough, one often has to rely on pressure transducer, which is generally used as a back-up for the acoustic wave gauge. Then a pressure transfer function is used to obtain the surface wave information. Alternatively, this study employed the artificial neural network to convert the pressure signal into significant wave height, significant wave period, maximum wave height and spectral peakedness parameter using data obtained from various water depths. The results showed that the wave parameters obtained from the artificial neural network were significantly closer to that obtained by the acoustic measurements than that by using linear pressure transfer function for water depth larger than 20 m. Moreover, for a given water depth, the wave height estimated by the network model from pressure data were not as good as that by linear wave theory for large wave height (above 4 m in significant wave height in this study). This can be improved if the training data set has more records of large wave height.
Relation: 36(15-16), pp.1149–1157
URI: http://ntour.ntou.edu.tw/handle/987654321/25462
Appears in Collections:[海洋環境資訊系] 期刊論文

Files in This Item:

File Description SizeFormat
index.html0KbHTML306View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback