Xu Cheng – författare

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.

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.

This book thoroughly explores the realm of data-driven blade-icing detection for wind turbines, focusing on multivariate time series classification to enhance the reliability and efficiency of wind energy utilization. The widespread prevalence of sensor technology in wind turbines, coupled with substantial data collection, has paved the way for advanced data-driven methodologies, which do not require extensive domain knowledge or additional mechanical tools. The interdisciplinary appeal of this study has drawn attention from experts in fields like computer science, mechanical engineering, and renewable energy systems. Adopting a comprehensive approach, the book lays down a foundational framework for blade-icing detection, stressing the critical role of sensor data integration and the profound impact of machine learning techniques in refining the detection processes. The book is designed for undergraduate and graduate students keen on renewable energy technologies, researchers delving into machine learning applications in energy systems, and engineers focusing on sustainable solutions for enhancing wind turbine performance.