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 interdisciplinary book introduces how to combine biological concept with locomotion control of limbless robots. The special features of the book include limbless locomotion classification and control, design of biological locomotor and the integration of sensory information into the locomotor using artificial intelligence methods, and on-site demonstrations of limbless locomotion in different scenarios. The book is suitable for readers with engineering background, especially for researchers focused on bio-inspired robots.

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.

This book introduces the development of the digital twin of the marine infrastructure in Norway, which will be a significant scientific and operational achievement for the industry, making efficient and safe offshore operations possible. It enables data exchange safely and easily between different sub-systems, modules, and various applications. A complete digital twin ship will be presented in details. Thus, the twin ship can provide an integrated view of the ship’s various physical and behavioral aspects in different stages, and allow simultaneous optimization of functional performance requirements. In addition, it enables advanced control and optimization, e.g., creating more reliable prediction for flexible objectives (time, output, emissions, fuel consumption), and executing day-ahead and long-term planning for operations.

More importantly, several related applications and case studies are presented in the end to confirm the effectiveness of the digital twin system. The research work is not only interesting for academia, also for industry.