Ship as Wave Buoy: Data-Driven Sea State Estimation Based on Ship Motion Data (Springer Series on Naval Architecture, Marine Engineering, Shipbuilding and Shipping)

Cheng, Xu/ Liu, Mengna/ Shi, Fan

Springer Verlag, Singapore（2026/04発売）

• 製本 Hardcover:ハードカバー版／ページ数 273 p.
• 言語 ENG
• 商品コード 9789819567416

Full Description

This book focuses on a comprehensive investigation into data-driven Sea State Estimation (SSE) by leveraging a vessel's own motion data. It presents a collection of advanced deep learning frameworks designed to overcome critical, real-world challenges inherent in this approach. This book systematically introduces key issues including: the class imbalance of sea state data, where rare but hazardous conditions are difficult to predict; the need for model transferability between different ships and loading conditions; and the crucial demand for security and robustness against adversarial data attacks. To solve these problems, the book introduces a suite of innovative architectures employing techniques such as densely connected convolutional networks, prototype-based classifiers, multi-scale feature learning, adversarial transfer learning, and dynamic graph networks. The efficacy of these models is rigorously validated on both public benchmarks and specialized ship motion datasets, demonstrating superior performance over existing state-of-the-art methods and providing a robust toolkit for enhancing maritime safety and efficiency.

Contents

Introduction.- State of the Art.- Densely connected convolutional neural network for sea-state estimation.- Prototype enhanced convolutional neural network for sea-state estimation.- Graph convolutional neural network for sea state estimation.- Class-imbalanced neural network for sea state estimation.- Secure Sea State Estimation: Adversarial Defense for Robust Maritime AI.- Transferable convolutional neural network for sea state estimation.- Adversarial-robust convolutional neural network for sea state estimation.- Concluding remarks.