3,200 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Glaubwürdigkeit und Einsatz des szenariobasierten X-in-the-Loop-Tests für Fahrerassistenzsysteme

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    Fahrerassistenzsysteme tragen gerade im Bereich der Nutzfahrzeuge zur Verkehrssicherheit bei. Darüber hinaus bietet das hochautomatisierte Fahren neue Geschäftsmodelle. Eine zentrale Herausforderung bei der Entwicklung dieser Systeme ist die steigende Breite und Tiefe der Testfälle. Bereits in heutigen Entwicklungsprojekten stoßen vorhandene Realtestkapazitäten an ihre Grenzen. Daher sind neue Methoden zum Test von Fahrerassistenzsystemen erforderlich. Die Kombination aus szenariobasiertem Testen und X-in-the-Loop-Testumgebungen ist ein vielversprechender Ansatz. In dieser Dissertation werden drei Einsatzmöglichkeiten des szenariobasierten XiL-Tests in einem Serien-Entwicklungsprojekt eingeführt und diskutiert. Als besonders geeignet wird hierbei der Einsatz beim Software-Qualifizierungstest bewertet und in einem Prozessentwurf weiter detailliert. Schwerpunkt ist die Zuordnung von Szenarien auf Testumgebungen mit dem Ziel, die Testabdeckung, die Glaubwürdigkeit der Testergebnisse und die Effizienz der Testdurchführung zu optimieren. Ein entscheidender Prozessschritt ist die sogenannte Glaubwürdigkeitsbewertung. Diese bewertet ein konkretes Szenario in einer spezifischen Testumgebung und besteht je nach Konfidenzanforderung aus den drei Schritten Prädiktion, Plausibilisierung und Validierung. In der Prädiktion werden mithilfe von Unsicherheitsmodellen für drei Subsysteme der XiL-Umgebung und einer Monte-Carlo-Simulation Testergebnis-Verteilungen generiert und mit einem Multinomialansatz Konfidenzintervalle ermittelt. Die Plausibilisierung prüft mithilfe von Pass/Fail-Kriterien und Szenariodistanzmaßen die Äquivalenz einzelner Testausführungen in XiL und Realtest. Bei der Validierung findet ein statistischer Abgleich der Testergebnis-Verteilungen aus XiL und Realtest mithilfe des Barnard-Tests statt. Die Glaubwürdigkeitsbewertung wird auf Basis von Software-in-the-Loop-Daten eines Entwicklungsprojekts für Fahrerassistenzsysteme für insgesamt sieben konkrete Szenarien evaluiert

    Graduate Catalog of Studies, 2023-2024

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    Flood dynamics derived from video remote sensing

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    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

    Undergraduate Catalog of Studies, 2023-2024

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    Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

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    Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Graduate Catalog of Studies, 2023-2024

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Neural Architecture Search for Image Segmentation and Classification

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    Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments. This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs
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