This paper presents a big data analytics method for the proactive mitigation of grounding
risk. The model encompasses the dynamics of ship motion trajectories while accounting for …

This paper presents a deep learning method for the prediction of ship motions in 6 Degrees
of Freedom (DoF). Big data streams of Automatic Identification System (AIS), now-cast, and …

Despite advancements in science and technology, ship collisions and groundings remain
the most prevalent types of maritime accidents. Recent developments in accident prevention …

A nonparametric identification method based on ν ('nu')-support vector regression (ν-SVR) is
proposed to establish robust models of ship maneuvering motion in an easy-to-operate way …

We present a reinforcement learning-based (RL) model predictive control (MPC) method for
trajectory tracking of surface vessels. The proposed method uses an MPC controller in order …

As a critical step designing the ship controller and the maritime traffic simulator, the system
identification of a ship dynamic model from input-output data is a promising direction …

A novel method, Gaussian process regression optimized by genetic algorithm (GA-GPR), is
proposed for nonparametric modeling of ship maneuvering motion. A genetic algorithm with …

A novel adaptive hybrid-kernel function based Gaussian process regression (AHKGPR) is
proposed for nonparametric modeling of ship maneuvering motion. With the aid of Gaussian …

Abstract System identification (SI) is a research focus in the field of autonomous underwater
vehicles (AUV) which can be considered as the estimation to the hydrodynamic coefficients …

A system identification method based on Gaussian progress regression (GPR) combined
with wavelet threshold denoising (WT) is proposed for identifying the black-box model of …