Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results

In this paper, an adaptive trajectory trackingcontrol algorithm for underactuated unmanned surfacevessels (USVs) with guaranteed transient performance isproposed. To meet the realistic dynamical model of USVs,we consider that the mass and damping matrices are notdiagonal and the input saturation problem. Neural Networks(NNs) are employed to approximate the unknown externaldisturbances and uncertain hydrodynamics of USVs. Moreover,both full state feedback control and output feedbackcontrol are presented, and the unmeasurable velocities ofthe output feedback controller are estimated via a highgainobserver. Unlike the conventional control methods,we employ the error transformation function to guaranteethe transient tracking performance. Both simulation andexperimental results are carried out to validate the superiorperformance via comparing with traditional potential integral(PI) control approaches

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Mesh-Free and Finite Element-Based Methods for Structural Mechanics Applications

The problem of solving complex engineering problems has always been a major topic in all industrial fields, such as aerospace, civil and mechanical engineering. The use of numerical methods has increased exponentially in the last few years, due to modern computers in the field of structural mechanics. Moreover, a wide range of numerical methods have been presented in the literature for solving such problems. Structural mechanics problems are dealt with using partial differential systems of equations that might be solved by following the two main classes of methods: Domain-decomposition methods or the so-called finite element methods and mesh-free methods where no decomposition is carried out. Both methodologies discretize a partial differential system into a set of algebraic equations that can be easily solved by computer implementation. The aim of the present Special Issue is to present a collection of recent works on these themes and a comparison of the novel advancements of both worlds in structural mechanics applications

Public Policymaking in Ireland: the Case of the Indigenous Interactive Media Industry, 1994-2004

Ireland’s economic rollercoaster, going from one of western Europe’s poorest countries to one of its wealthiest during the late 1990s and early 2000s, to being bailed-out in 2010 by the EU and IMF, has attracted much international attention. But beneath this story of bust to boom to bust lies an interesting case study of how a small peripheral state attempted to develop an indigenous internationally competitive sector. This study argues that over theperiod the state refined its ‘strategic future grabbing’ policymaking process. This flexible, fast reacting, non-bureaucratic policymaking system was informed by the small agile state’s American-leaning intelligence gathering network and input from private sector consultants. This research had unprecedented access to the state’s enterprise policymakingdocumentation. This access fed into the principal research method - a case study of the Irish state’s policymaking process aimed at developing its indigenous interactive media industry between 1994 and 2004. The majority of indigenous interactive media companies failed. However, those that survived were successful enough to ensure that by the end of this period, Dublin, its capital, had become one of the world’s largest producers for export of interactive educational media produced by indigenous companies. The interaction of the indigenous interactive media industry with the policymaking process is also examined. This policymaking process is placed in the context of previous Irish policymaking towards indigenous enterprise development