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Task-Driven Learning Downsampling Network Based Phase-Resolved Wave Fields Reconstruction with Remote Optical Observations.
- Source :
- Journal of Marine Science & Engineering; Jul2024, Vol. 12 Issue 7, p1082, 18p
- Publication Year :
- 2024
-
Abstract
- We develop a phase-resolved wave field reconstruction method by the learning-based downsampling network for processing large amounts of inhomogeneous data from non-contact wave optical observations. The Waves Acquisition Stereo System (WASS) extracts dense point clouds from ocean wave snapshots. We couple learning-based downsampling networks with the phase-resolved wave reconstruction algorithm, and the training task is to improve the wave reconstruction completeness ratio C R . The algorithm first achieves initial convergence and task-optimized performance on numerical ocean waves built by the linear wave theory model. Results show that the trained sampling network can lead to a more uniform spatial distribution of sampling points and improve C R at the observed edge regions far from the optical camera. Finally, we apply our algorithm to a natural ocean wave dataset. The average completeness ratio is improved over 30% at low sampling ratios ( S R ∈ [ 2 − 9 , 2 − 7 ] ) compared to the traditional FPS method and Random sampling method. Moreover, the relative residual between the final reconstructed wave and the natural wave is less than 15%, which provides an efficient tool for wave reconstruction in ocean engineering. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20771312
- Volume :
- 12
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Journal of Marine Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 178697879
- Full Text :
- https://doi.org/10.3390/jmse12071082