204 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Airfoil Optimization using Design-by-Morphing
We present Design-by-Morphing (DbM), a novel design methodology applicable to
creating a search space for topology optimization of 2D airfoils. Most design
techniques impose geometric constraints and sometimes designers' bias on the
design space itself, thus restricting the novelty of the designs created, and
only allowing for small local changes. We show that DbM methodology does not
impose any such restrictions on the design space and allows for extrapolation
from the search space, thus granting truly radical and large search space with
a few design parameters. In comparison to other shape design methodologies, we
apply DbM to create a search space for 2D airfoils. We optimize this airfoil
shape design space for maximizing the lift-over-drag ratio, , and
stall angle tolerance, . Using a bi-objective genetic algorithm
to optimize the DbM space, it is found that we create a Pareto-front of radical
airfoils exhibiting remarkable properties for both objectives
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt.
Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit.
Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte
Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen.
Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt.
Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie
Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert.
Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver
(∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare
Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet
Advanced Materials and Technologies in Nanogenerators
This reprint discusses the various applications, new materials, and evolution in the field of nanogenerators. This lays the foundation for the popularization of their broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics, and artificial intelligence
Identification of flexible structures dynamics.
The pursuit of aerodynamic efficiency and the advances in materials technology, particularly in composite material, has contributed to shifting the paradigm of
wing design to high aspect ratio wings. Increasing the span, for decreasing drag,
and using composite lightweight materials make the new wing very flexible and
prone to nonlinear dynamic behaviour. With nonlinearities, increasing challenges
arise for the identification and modelling of the wing. These challenges cannot
be overlooked for flexible structures as these models are critical for the prediction
of aeroelastic phenomena. Hence, it is fundamental to expand the knowledge
of the behaviour of these structures through the identification and modelling of
sample flexible wing models. In this work, a series of methods and approaches
are proposed and employed for the identification and modelling of a flexible wing.
First, a system identification technique in the frequency domain, the Loewner
Framework, is applied for modal parameters extraction in mechanical systems
for structural health monitoring. This new technique, with a linear reduced order
model, is used to characterise the flutter behaviour of a flexible wing. The results
are compared to similar techniques. A thorough experimental campaign is run on
a flexible wing model to characterise its nonlinear behaviour and the underlying
linear system. In particular, nonlinearities are detected, identified and quantified.
Then, a meta-model technique based on Kriging, the refined Efficient Global Optimisation, is proposed for finite element model updating. First, the technique is
used for damage detection in benchmark structures, then, it is employed for the
validation of component-based strategies for model updating of a flexible wing.Engineering and Physical Sciences (EPSRC)PhD in Aerospac
A Review of Cooperative Actuator and Sensor Systems Based on Dielectric Elastomer Transducers
This paper presents an overview of cooperative actuator and sensor systems based on
dielectric elastomer (DE) transducers. A DE consists of a flexible capacitor made of a thin layer
of soft dielectric material (e.g., acrylic, silicone) surrounded with a compliant electrode, which is
able to work as an actuator or as a sensor. Features such as large deformation, high compliance,
flexibility, energy efficiency, lightweight, self-sensing, and low cost make DE technology particularly
attractive for the realization of mechatronic systems that are capable of performance not achievable
with alternative technologies. If several DEs are arranged in an array-like configuration, new concepts
of cooperative actuator/sensor systems can be enabled, in which novel applications and features
are made possible by the synergistic operations among nearby elements. The goal of this paper is
to review recent advances in the area of cooperative DE systems technology. After summarizing
the basic operating principle of DE transducers, several applications of cooperative DE actuators
and sensors from the recent literature are discussed, ranging from haptic interfaces and bio-inspired
robots to micro-scale devices and tactile sensors. Finally, challenges and perspectives for the future
development of cooperative DE systems are discussed
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