1,059 research outputs found

    Implementation of digital twin-enabled virtually monitored data in inspection planning

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    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

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    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

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    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

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    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

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    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

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Machine learning in portfolio management

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    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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