Houxiang Zhang – författare

This book presents a bio-inspired hierarchical control scheme step by step toward developing limbless robots capable of 3D locomotion, fast reflex response, as well as sophisticated reaction to environmental stimuli.

This book presents a bio-inspired hierarchical control scheme step by step toward developing limbless robots capable of 3D locomotion, fast reflex response, as well as sophisticated reaction to environmental stimuli.

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 investigates how human mental fatigue (MF) can be objectively measured during demanding maritime operations. The maritime domain is characterized by demanding operations. These operations can be especially complex and dangerous when they require coordination between different maritime vessels and several maritime operators. The best approach to quantify MF is through the use of physiological sensors including electroencephalogram (EEG), electrocardiogram, electromyogram, temperature sensor, and eye tracker can be applied, individually or in conjunction, in order to collect relevant data that can be mapped to an MF scale. More than simpler sensor fusion, this book bridges the gap between relevant sensor data and a quantifiable MF level using both data-driven and model-based approaches.Data-driven part investigates the use of different NNs combined for the MF assessment (MFA) task. Among the different architectures tested, convolutional neural networks (CNN) showed the best performance when dealing with multiple physiological data channels. Optimization was used to improve the performance of CNN in the cross-subject MFA task. Testing different combinations of physiological sensors indicated a setup consisting of EEG sensor only was the best option, due to the trade-off between assessment precision and sensor framework complexity. These two factors are of great importance when considering an MFA system that could be implemented in real-life scenarios. The model-based discussion applies the current knowledge about the use of EEG data to characterize MF to develop an MF approach to quantify the progression of MF in maritime operators.In the research presented in this book, realistic vessel simulators  were used as a platform for experimenting with different operational scenarios and sensor setups.