1,660 research outputs found

    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

    Graduate Catalog of Studies, 2023-2024

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    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Libro de Abstracts | VIII Jornadas de Investigación y Doctorado: “Ética en la Investigación Científica”

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    El objetivo de estas Jornadas es promover el intercambio científico entre estudiantes de doctorado, fomentando la participación, el debate y la discusión, de aspectos científicos tan importantes como la ética de la investigación. Para poner en valor el papel de los doctores en la sociedad, no podemos pasar por alto las competencias transversales que estos deben adquirir en su formación como doctores. Si bien la ética es algo fundamental en todas las facetas de la vida, en el caso de los investigadores cobra especial relevancia, ya que son generadores de conocimiento sobre el que se asentarán futuros desarrollos y políticas de interés para toda la sociedad. Por lo tanto, con el fin de incrementar la proyección social de las investigaciones llevadas a cabo y la proyección profesional de los doctores, es importante incidir en su formación ética. La base de la investigación académica está construida sobre la confianza. Los investigadores confían en que los resultados informados por otros son veraces. La sociedad confía en que los resultados de la investigación reflejan un intento honesto por parte de los científicos de describir el mundo de forma precisa. Pero esta confianza sólo perdurará si la comunidad científica transmite los valores asociados a la conducta de la ética de investigación. Por este motivo, la Universidad juega un papel muy importante en la formación de los doctores en cuestiones éticas que son inherentes al método científico y a la generación de conocimiento. Dentro de las universidades, las Escuelas Internacionales de Doctorado, con nuestros recursos, aptitudes y espacio de influencia, nos convertimos en actores clave para promover actitudes éticas entre los doctorandos, y estas Jornadas son una oportunidad muy valiosa para tratar este tema. Las ramas de conocimiento que se incluyen para estas Jornadas son las derivadas de los programas de doctorado de la EIDUCAM: -Ciencias de la Salud -Tecnologías de la Computación e Ingeniería Ambiental -Ciencias Sociales -Ciencias del DeporteActividad Física y DeporteAdministración y Dirección de EmpresasAgricultura y VeterinariaArte y HumanidadesCiencias AmbientalesCiencias de la AlimentaciónCiencias de la ComunicaciónCiencias ReligiosasDerechoEducaciónEnfermeríaFarmaciaIdiomasIngeniería, Industria y ConstrucciónMedicinaOdontologíaPodologíaPsicologíaTerapia y RehabilitaciónTurism

    Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation

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    In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling. We also compared model accuracy through producing the LULC classification using a single pass of a grid of patches and averaging multiple grid passes and three rotated version of each patch. Our results showed: a stratified random sampling approach for producing training patches improved the accuracy of classes with a smaller area while having minimal effect on larger classes; a smaller number of larger patches compared to a larger number of smaller patches improves model accuracy; applying data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced and more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average kappa of 0.84 user accuracy of 0.81 and producer accuracy of 0.87. This study has demonstrated the importance of data pre-processing for developing a generalised deep-learning model for LULC classification which can be applied to a different date and sensor. Future research using CNN and earth observation data should implement the findings of this study to increase LULC model accuracy and transferability

    Numerical resolution of the Navier-Stokes equations with parallel programming for the analysis of heat and mass transfer phenomena.

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    Aquesta tesi analitza mètodes numèrics per resoldre les equacions de Navier-Stokes en dinàmica de fluids computacional (CFD, per les sigles en anglès). La investigació es centra a des- envolupar una visió profunda de diferents mètodes numèrics i la seva aplicació a diversos fenòmens de transport. S’aplica una metodologia pas a pas, que abarca l’anàlisi de volums fi- nits i mètodes espectrals, la validació de models i la verificació de codis a través de l’anàlisi de casos d’estudi de convecció-difusió, flux de fluids i turbulència. La investigació revela l’efecte de diferents esquemes d’aproximació a la solució numèrica i emfatitza la importància d’una representació física precisa juntament amb la solidesa matemàtica. S’examina la convergència del mètode de resolució d’equacions iteratiu pel que fa a la naturalesa de la física de l’estudi, i cal destacar la necessitat de tècniques de relaxació apropiades. A més, s’explora el mètode de passos fraccionats per resoldre el fort acoblament de pressió-velocitat a les equacions de Navier-Stokes, mentre es considera l’addició d’altres fenòmens de transport. L’anàlisi de fluxes turbulents mostra la cascada d’energia a l’espai de Fourier i l’efecte del truncament a causa de la discretització espacial o espectral, abordat per l’aplicació de models simplificats, com ara Large Eddy Simulation (LES), aconseguint una solució aproximada amb un menor cost computacional. A més, s’analitza la implementació de la computació en paral·lel utilitzant l’estàndard MPI, emfatitzant-ne l’escalabilitat i el potencial per abordar les demandes creixents de l’anàlisi CFD en els camps de l’enginyeria. En general, aquesta recerca proporciona informació valuosa sobre els mètodes numèrics per a les equacions de Navier-Stokes, la seva aplicació a CFD i les implicacions pràctiques per als processos d’enginyeriaEsta tesis analiza métodos numéricos para resolver las ecuaciones de Navier-Stokes en dinámica de fluidos computacional (CFD, por sus siglas en Inglés). La investigación se centra en desarrollar una visión profunda de distintos métodos numéricos y su aplicación a diversos fenómenos de transporte. Se aplica una metodología paso a paso, que abarca el análisis de volúmenes finitos y métodos espectrales, validación de modelos y verificación de códigos a través del analisis de casos de estudio de convección-difusión, flujo de fluidos y turbulencia. La investigación revela el efecto de diferentes esquemas de aproximación en la solución numérica y enfatiza la importancia de una representación física precisa junto con la solidez matemática. Se examina la convergencia del método de resolución de equaciones iterativo con respecto a la naturaleza de la física del estudio, destacando la necesidad de técnicas de relajación apropiadas. Además, se explora el método de pasos fraccionados para resolver el fuerte acoplamiento de presión-velocidad en las ecuaciones de Navier-Stokes, mientras se considera la adición de otros fenómenos de transporte. El análisis de flujos turbulentos muestra la cascada de energía en el espacio de Fourier y el efecto del truncamiento debido a la discretización espacial o espectral, abordado por la aplicación de modelos simplificados, como Large Eddy Simulation (LES), logrando una solución aproximada con un menor costo computacional. Además, se analiza la implementación de la computación en paralelo utilizando el estándar MPI, enfatizando su escalabilidad y potencial para abordar las crecientes demandas del análisis CFD en los campos de la ingeniería. En general, esta investigación proporciona información valiosa sobre los métodos numéricos para las ecuaciones de Navier-Stokes, su aplicación a CFD y sus implicaciones prácticas para los procesos de ingenieríaThis thesis analyzes numerical methods for solving the Navier-Stokes equations in computational fluid dynamics (CFD). The research focuses on developing a deep insight into different numerical techniques and their application to various transport phenomena. A step-by-step methodology is applied, encompassing the analysis of finite volume and spectral methods, model validation, and code verification with the study of convection-diffusion, fluid flow, and turbulence study cases. The investigation reveals the effect of different approximation schemes on the numerical solution and emphasizes the importance of accurate physics representation alongside mathematical robustness. The convergence of the numerical solver is examined concerning the nature of the studied physics, highlighting the need for appropriate relaxation techniques. Additionally, the fractional step method is explored to solve the strong pressure-velocity coupling in the Navier-Stokes equations while considering the addition of other transport phenomena. The analysis of turbulent flows showcases the energy cascade in the Fourier space and its truncation effect due to spatial or spectral discretization, addressed by the application of simplified models, such as Large Eddy Simulation (LES), capable of approximating the solution with reduced computational cost. Furthermore, the implementation of parallel computing using the MPI standard is discussed, emphasizing its scalability and potential for addressing the growing demands of CFD analysis in engineering fields. Overall, this research provides valuable insights into numerical methods for the Navier-Stokes equations, their application to CFD, and their practical implications for engineering processe

    Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial

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    On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage and the remaining useful life prediction of turbofan engines
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