1,059 research outputs found
Implementation of digital twin-enabled virtually monitored data in inspection planning
Marine structures are subjected to cyclic wave loads in ocean environments, leading to progressive forms of structural degradation such as fatigue cracks. To ensure fitness-for-service of these critical assets, there has been increasing interest in the application of digital twin-enabled virtual monitoring techniques. Whilst numerous studies have focused on computational algorithms dedicated to virtual monitoring, little effort has been devoted to establishing a practical digital-to-physical connection and decision-making based on virtually monitored data. This paper bridges this research gap by proposing an approach for implementing digital twin-enabled virtually monitored data in inspection planning for marine structures. The inspection of fatigue-prone structural components plays a crucial role in structural integrity management. Reliability-informed inspection, which employs a probabilistic approach that prioritises inspections based on probability of failure, offers a cost-effective approach by avoiding unnecessary inspections and reducing life-cycle costs. However, conducting a comprehensive structural reliability analysis requires thorough knowledge of the actual operational profile and current state of a structure (e.g. consumed fatigue life) in order to accurately predict its future performance (e.g. remaining fatigue life). Although design specifications and assumptions can serve as guidelines, a high degree of uncertainty may arise due to the discrepancy between the actual operational profile and the design assumptions. The approach developed in this paper consists of four main elements: virtual monitoring, data-driven forecasting, fatigue reliability, and inspection planning. This provides a practical means for establishing a connection between condition monitoring and assessment in the digital world and decision-making in the physical world. An illustrative numerical example is then presented to demonstrate the application of the proposed framework. Finally, avenues for future research and developments in this field are discussed
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
An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization
Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden.
Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning
(RL) aims to produce RL algorithms whose policies generalise well to novel
unseen situations at deployment time, avoiding overfitting to their training
environments. Tackling this is vital if we are to deploy reinforcement learning
algorithms in real world scenarios, where the environment will be diverse,
dynamic and unpredictable. This survey is an overview of this nascent field. We
rely on a unifying formalism and terminology for discussing different ZSG
problems, building upon previous works. We go on to categorise existing
benchmarks for ZSG, as well as current methods for tackling these problems.
Finally, we provide a critical discussion of the current state of the field,
including recommendations for future work. Among other conclusions, we argue
that taking a purely procedural content generation approach to benchmark design
is not conducive to progress in ZSG, we suggest fast online adaptation and
tackling RL-specific problems as some areas for future work on methods for ZSG,
and we recommend building benchmarks in underexplored problem settings such as
offline RL ZSG and reward-function variation
Ditransitives in germanic languages. Synchronic and diachronic aspects
This volume brings together twelve empirical studies on ditransitive constructions in Germanic languages and their varieties, past and present. Specifically, the volume includes contributions on a wide variety of Germanic languages, including English, Dutch, and German, but also Danish, Swedish, and Norwegian, as well as lesser-studied ones such as Faroese. While the first part of the volume focuses on diachronic aspects, the second part showcases a variety of synchronic aspects relating to ditransitive patterns. Methodologically, the volume covers both experimental and corpus-based studies. Questions addressed by the papers in the volume are, among others, issues like the cross-linguistic pervasiveness and cognitive reality of factors involved in the choice between different ditransitive constructions, or differences and similarities in the diachronic development of ditransitives. The volume’s broad scope and comparative perspective offers comprehensive insights into well-known phenomena and furthers our understanding of variation across languages of the same family
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Alternative Power: The Politics of Denmark\u27s Renewable Energy Transition
Global climate change is one of the defining political challenges and opportunities of the current era. Experts widely agree that technical means already exist for making the necessary transition from fossil fuels to renewable energy; the obstacles to doing so are primarily political. Careful observers also recognize that this period of transition creates an opening for political innovation and development. How can the political will be generated to take action to prevent climate catastrophe? And what will the process of transitioning mean for the political systems that have been built on cheap and abundant oil? Political scientists have largely ignored technological development as a lever for political development, or feared that technology could only be a force of domination. Yet renewable energy enthusiasts have often seen democratizing potential in these technologies. What can be accomplished politically by building a wind turbine? As countries like Denmark accumulate decades of experience with renewable energy, it is becoming possible to give such questions close empirical consideration. Denmark generates more of its electricity from renewable sources, and has been doing so longer, than any other industrialized nation, making it a uniquely valuable case for studying an advanced renewable energy transition in progress. This dissertation draws on novel qualitative and quantitative data to present the first comprehensive history of Denmark’s energy transition from its roots in the 1970s until the present, aiming to explain how this tiny nation emerged as the world’s leading wind power producer, and assess whether this process has yielded any democratic dividends. The multi-method analysis sheds new light on internal dynamics of Denmark’s energy transition, and, more generally, on late-stage evolutionary processes in mature technological systems. Many studies have shown an interest in the Danish case, which is usually presented as a relatively unqualified success story, but few have provided the empirical resolution to identify these complicating factors. This dissertation employs an explanatory strategy adapted from the ecological sciences to construct a more holistic and integrative portrait, resulting in a more thorough and accurate account of how Denmark jumped out to such a significant lead in the energy transition, and why that momentum might be flagging today, with implications for other countries hoping to chart a path toward a sustainable future
University of Windsor Graduate Calendar 2023 Spring
https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp
Machine learning in portfolio management
Financial markets are difficult learning environments. The data generation process is time-varying,
returns exhibit heavy tails and signal-to-noise ratio tends to be low. These contribute to the challenge
of applying sophisticated, high capacity learning models in financial markets. Driven by recent
advances of deep learning in other fields, we focus on applying deep learning in a portfolio
management context. This thesis contains three distinct but related contributions to literature. First,
we consider the problem of neural network training in a time-varying context. This results in a neural
network that can adapt to a data generation process that changes over time. Second, we consider
the problem of learning in noisy environments. We propose to regularise the neural network using a
supervised autoencoder and show that this improves the generalisation performance of the neural
network. Third, we consider the problem of quantifying forecast uncertainty in time-series with
volatility clustering. We propose a unified framework for the quantification of forecast uncertainty that results in uncertainty estimates that closely match actual realised forecast errors in cryptocurrencies
and U.S. stocks
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