Directional wave spectrum estimation with ship motion responses using adversarial networks

Motion measurements from a seven-days voyage across the Pacific Ocean are studied, and it is shown that the wave buoy analogy estimates wave conditions, in terms of sea

In this work, the path integration method and the energy-based stochastic averaging method are introduced in order to study the stochastic responses of ship roll motion in random

To increase the interpretability of the deep learning based sea state estimation models, the raw ship motion data in the time domain is transformed into two-dimensional

A lot of interesting work has been carried out such as ship roll motion time series forecasting (Pena et al., 2011), online ship roll motion prediction (Yin et al., 2014),

In figure 6.4, the estimated spectral hourly fatigue rates from the wave responses is presented and high frequency contribution to the total damage as is estimated

This paper presents a neural-network-based method to predict ship motion and use the prediction to improve active heave compensation (AHC) of offshore crane operationI. A

The wave generation and propagation of steep irregular waves in the numerical model is validated by comparing the numerical wave spectrum with the experimental input wave spectrum..

Adaptive Sampler The adaptive sampling network is fed with the warped previous denoised output (warped using application- provided motion vectors) along with feature buffers for