Prediction of ship added resistance in waves using an artificial neural network
Ship resistance is one of the major components of the ship which hampers its motion. This resistance value experienced by a ship should be known and overcome for the efficient operation of a vessel. This project presents a model to predict added wave resistance experienced through the aid of a Keras...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2021
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/149799 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| id |
sg-ntu-dr.10356-149799 |
|---|---|
| record_format |
dspace |
| spelling |
sg-ntu-dr.10356-1497992021-05-20T08:31:23Z Prediction of ship added resistance in waves using an artificial neural network Thangamuthu, Vijayazhagan Liu Shukui School of Mechanical and Aerospace Engineering skliu@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering Ship resistance is one of the major components of the ship which hampers its motion. This resistance value experienced by a ship should be known and overcome for the efficient operation of a vessel. This project presents a model to predict added wave resistance experienced through the aid of a Keras sequential artificial neural network. The model is trained with different parameters to enhance its accuracy. A user interface is then linked to this model to attain user inputs and predict the added wave resistance experienced by sea-going vessels. Bachelor of Engineering (Mechanical Engineering) 2021-05-20T08:31:23Z 2021-05-20T08:31:23Z 2021 Final Year Project (FYP) Thangamuthu, V. (2021). Prediction of ship added resistance in waves using an artificial neural network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149799 https://hdl.handle.net/10356/149799 en B304 application/pdf Nanyang Technological University |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering |
| spellingShingle |
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering Thangamuthu, Vijayazhagan Prediction of ship added resistance in waves using an artificial neural network |
| description |
Ship resistance is one of the major components of the ship which hampers its motion. This resistance value experienced by a ship should be known and overcome for the efficient operation of a vessel. This project presents a model to predict added wave resistance experienced through the aid of a Keras sequential artificial neural network. The model is trained with different parameters to enhance its accuracy. A user interface is then linked to this model to attain user inputs and predict the added wave resistance experienced by sea-going vessels. |
| author2 |
Liu Shukui |
| author_facet |
Liu Shukui Thangamuthu, Vijayazhagan |
| format |
Final Year Project |
| author |
Thangamuthu, Vijayazhagan |
| author_sort |
Thangamuthu, Vijayazhagan |
| title |
Prediction of ship added resistance in waves using an artificial neural network |
| title_short |
Prediction of ship added resistance in waves using an artificial neural network |
| title_full |
Prediction of ship added resistance in waves using an artificial neural network |
| title_fullStr |
Prediction of ship added resistance in waves using an artificial neural network |
| title_full_unstemmed |
Prediction of ship added resistance in waves using an artificial neural network |
| title_sort |
prediction of ship added resistance in waves using an artificial neural network |
| publisher |
Nanyang Technological University |
| publishDate |
2021 |
| url |
https://hdl.handle.net/10356/149799 |
| _version_ |
1701270516423196672 |