Marvelocity Pdf ★ Secure
\section{Methodology} \label{sec:method} \subsection{Data Acquisition} \begin{itemize} \item \textbf{AIS}: 2.3 M messages (2018–2023) from the Global Fishing Watch and MarineTraffic APIs. \item \textbf{Oceanographic Reanalysis}: ERA5 \cite{Hersbach2020} providing 10‑m wind vectors, significant wave height, and surface currents at 0.25° resolution. \item \textbf{Ship Catalog}: Technical specifications (length overall, beam, draft, block coefficient, engine power) extracted from the Lloyd’s Register database. \end{itemize} All timestamps are aligned to UTC and interpolated to a 10‑minute cadence.
\subsection{Training Procedure} \begin{itemize} \item \textbf{Train/validation split}: 70 \% ships for training, 15 \% for validation, 15 \% for test (no ship appears in more than one split). \item \textbf{Hyper‑parameter optimisation}: Bayesian optimisation (Optuna \cite{Akiba2019}) over tree depth, learning rate, and number of estimators. \item \textbf{Loss function}: Mean Absolute Error (MAE) on $\Delta V$. \end{itemize} Model training is performed on a single NVIDIA RTX 4090 GPU (≈ 5 min).
\newpage \section{Introduction} \label{sec:intro} The global shipping industry transports over \SI{80}{\percent} of world trade by volume \cite{UNCTAD2022}. Despite advances in hull design and propulsion, a substantial fraction of fuel burn is attributable to sub‑optimal speed choices driven by inaccurate speed forecasts \cite{Mitsui2019}. Conventional approaches—e.g., the Holtrop–Mennen method \cite{Holtrop1972} or the ITTC‑1998 friction line \cite{ITTC1998}—rely on static ship parameters and simplified sea‑state corrections. Such models neglect the complex, nonlinear interaction among wind, waves, currents, and ship trim. marvelocity pdf
\title{MarVelocity:\\A Data‑Driven Metric for Predicting Maritime Vessel Speed} \author{ \textbf{Alexandra T. Liu}$^{1}$, \textbf{Rahul K. Menon}$^{2}$, \textbf{Elena G. Petrova}$^{3}$\\[2mm] $^{1}$Department of Naval Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA\\ $^{2}$Marine Systems Research Group, Indian Institute of Technology, Bombay, India\\ $^{3}$Institute of Ocean Engineering, Technical University of Munich, Munich, Germany\\[2mm] \texttt{atl@mit.edu, rkm@iitb.ac.in, elena.petrova@tum.de} } \date{\today}
The final **MarVelocity** prediction is: \begin{equation} V_{\text{MarV}} = V_{\text{HM}} + \hat{\Delta V}(\mathbf{x}). \end{equation} \end{itemize} All timestamps are aligned to UTC and
\subsection{Limitations} \begin{itemize} \item \textbf{Data sparsity in polar regions}: AIS coverage is lower, leading to higher uncertainties. \item \textbf{Propeller efficiency assumption}: We treat $\eta_p$ as a constant; future work will embed a learnable efficiency model. \item \textbf{Real‑time constraints}: While inference is sub‑millisecond, integrating high‑resolution forecasts (e.g., ECMWF) adds latency; edge‑computing strategies are under investigation. \end{itemize}
\subsection{Machine‑Learning Approaches} Bai et al. \cite{Bai2021} employed deep neural networks to predict fuel consumption from AIS and weather data, achieving a 5 \% error reduction. Chen and Li \cite{Chen2022} introduced a physics‑informed neural network (PINN) to enforce momentum balance, yet their dataset (≈ 200 k samples) limits generalisation. \item \textbf{Loss function}: Mean Absolute Error (MAE) on
\section{Related Work} \label{sec:related} \subsection{Physical Models} The Holtrop–Mennen (HM) and KVLCC2 families remain industry standards for estimating ship resistance \cite{Holtrop1972, KVLCC1992}. Their primary limitation is the assumption of steady, uniform sea conditions and neglect of wind‑induced drag.
