800 research outputs found

    Data-driven exact model order reduction for computational multiscale methods to predict high-cycle fatigue-damage in short-fiber reinforced plastics

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    Motiviert durch die Entwicklung energieeffizienterer Maschinen und Transportmittel hat der Leichtbau in den letzten Jahren enorm an Wichtigkeit gewonnen. Eine wichtige Klasse der Leichtbaumaterialien sind die faserverstärkten Kunststoffe. In der vorliegenden Arbeit liegt der Fokus auf der Entwicklung und Bereitstellung von Materialmodellen zur Vorhersage des Ermüdungsverhaltens kurzglasfaserverstärkter Thermoplaste. Diese Materialien unterscheiden sich dabei durch ihre Aufschmelzbarkeit und ihrer damit einhergehenden besseren Recyclebarkeit von thermosetbasierten Materialien. Außerdem erlauben die Kurzglasfasern im Gegensatz zu Langfasern eine einfache und zeiteffiziente Herstellung komplexer Komponenten. Ermüdung ist ein wichtiger Versagensmechanismus in solchen Komponenten, insbesondere für Bauteile z.B. in Fahrzeugen, die vibrationsartigen Belastungen ausgesetzt sind. Durch die inherente Anisotropie des Materials sind die experimentelle Charakterisierung und Vorhersage dieses Versagensmechanismus jedoch äußerst zeitintensiv und stellen somit eine wesentliche Herausforderung im Entwicklungsprozess und für die breitere Anwendung solcher Bauteile dar. Daher ist die Entwicklung komplementärer simulativer Methoden von großem Interesse. Im Rahmen dieser Arbeit werden Methoden zur Vorhersage der Ermüdungsschädigung kurzglasfaserverstärkter Werkstoffe im Rahmen einer Multiskalenmethode entwickelt. Die in der Arbeit betrachteten Multiskalenmodelle bieten die Möglichkeit, allein anhand der experimentellen Charakterisierungen der Materialparameter der Konstituenten, d.h. Faser und Matrix, komplexe anisotrope Effekte des Verbundmaterials vorherzusagen. Der experimentelle Aufwand kann dadurch enorm reduziert werden. Dazu werden zunächst Materialmodelle für die Konstituenten des Komposits entwickelt. Mithilfe FFT-basierter rechnergestützter Homogenisierung wird daraus das Materialverhalten des Komposits für verschiedene Mikrostrukturen und Lastfälle vorhergesagt. Die vorberechneten Lastfälle auf Mikrostrukturebene werden mit datengetriebenen Methoden auf die Makroskala übertragen. Das ermöglicht eine effiziente Berechnung von Bauteilen in wenigen Stunden, wohingegen eine entsprechende Berechnung mit geometrischer Auflösung aller einzelnen Fasern der Mikrostruktur auf heutigen Computern viele Jahre dauern würden. Für die Matrix werden unterschiedliche Schädigungsmodelle untersucht. Ihre Vor- und Nachteile werden analysiert. Die Mikrostruktursimulationen geben einen Einblick in den Einfluss verschiedener statistischer Parameter wie Faserlängen und Faservolumengehalt auf das Kompositverhalten. Ein neues Modellordnungsreduktionsverfahren wird entwickelt und zur Simulation des Ermüdungsschädigungsverhaltens auf Bauteilebene angewandt. Weiter werden Modellerweiterungen zur Berücksichtigung des R-Wert-Verhältnisses und viskoelastischer Effekte in der Evolution der Ermüdungsschädigung entwickelt und mit experimentellen Ergebnissen validiert. Das entstandene Simulationsframework erlaubt nach Vorrechnungen auf einer geringen Menge von Mikrostrukturen und Lastfällen eine effiziente Makrosimulation eines Bauteils vorzunehmen. Dabei können Effekte wie Viskoelastizität und R-Wert-Abhängigkeit je nach gewünschter Modellierungstiefe berücksichtigt oder vernachlässigt werden, um immer das effizientste Modell, das alle relevanten Effekte abbildet, nutzen zu können

    Is attention all you need in medical image analysis? A review

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    Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated

    The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

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    With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs

    Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

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    Deep-learning (DL) methods for the fast inference of the temporal evolution of fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed. A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training. As opposed to the image data-structures processed by CNNs, graphs offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel GNN architectures were designed for fluid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are defined from the original graph, and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales. Advection and fluid flow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the author’s knowledge, the REMuS-GNN architecture was the first rotation-equivariant and multi-scale GNN. The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces

    Data-Driven Exploration of Coarse-Grained Equations: Harnessing Machine Learning

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    In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in this thesis is to develop methodologies that can automatically extract simplified equations by training models using available data. ML algorithms have the ability to learn underlying patterns and relationships within the data, making it possible to extract simplified equations that capture the essential behavior of the system. This study considers three distinct learning categories: black-box, gray-box, and white-box learning. The initial phase of the research focuses on black-box learning, where no prior information about the equations is available. Three different neural network architectures are explored: multi-layer perceptron (MLP), convolutional neural network (CNN), and a hybrid architecture combining CNN and long short-term memory (CNN-LSTM). These neural networks are applied to uncover the non-linear equations of motion associated with phase-field models, which include both non-conserved and conserved order parameters. The second architecture explored in this study addresses explicit equation discovery in gray-box learning scenarios, where a portion of the equation is unknown. The framework employs eXtended Physics-Informed Neural Networks (X-PINNs) and incorporates domain decomposition in space to uncover a segment of the widely-known Allen-Cahn equation. Specifically, the Laplacian part of the equation is assumed to be known, while the objective is to discover the non-linear component of the equation. Moreover, symbolic regression techniques are applied to deduce the precise mathematical expression for the unknown segment of the equation. Furthermore, the final part of the thesis focuses on white-box learning, aiming to uncover equations that offer a detailed understanding of the studied system. Specifically, a coarse parametric ordinary differential equation (ODE) is introduced to accurately capture the spreading radius behavior of Calcium-magnesium-aluminosilicate (CMAS) droplets. Through the utilization of the Physics-Informed Neural Network (PINN) framework, the parameters of this ODE are determined, facilitating precise estimation. The architecture is employed to discover the unknown parameters of the equation, assuming that all terms of the ODE are known. This approach significantly improves our comprehension of the spreading dynamics associated with CMAS droplets

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Demand Response in Smart Grids

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    The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

    Bio-Based Materials: Contribution to Advancing Circular Economy

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    This reprint focuses on studies dealing with bio-based materials and its contribution to a circular economy. Research dealing with recycling, waste conversion to bio-based products, the development of bio-based composites, and surface treatments on cellulose fibres have been included in this reprint

    Bio-Radar: sistema de aquisição de sinais vitais sem contacto

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    The Bio-Radar system is capable to measure vital signs accurately, namely the respiratory and cardiac signal, using electromagnetic waves. In this way, it is possible to monitor subjects remotely and comfortably for long periods of time. This system is based on the micro-Doppler effect, which relates the received signal phase variation with the distance change between the subject chest-wall and the radar antennas, which occurs due to the cardiopulmonary function. Considering the variety of applications where this system can be used, it is required to evaluate its performance when applied to real context scenarios and thus demonstrate the advantages that bioradar systems can bring to the general population. In this work, a bio-radar prototype was developed in order to verify the viability to be integrated in specific applications, using robust and low profile solutions that equally guarantee the general system performance while addressing the market needs. Considering these two perspectives to be improved, different level solutions were developed. On the hardware side, textile antennas were developed to be embedded in a car seat upholstery, thus reaching a low profile solution and easy to include in the industrialization process. Real context scenarios imply long-term monitoring periods, where involuntary body motion can occur producing high amplitude signals that overshadow the vital signs. Non-controlled monitoring environments might also produce time varying parasitic reflections that have a direct impact in the signal. Additionally, the subject's physical stature and posture during the monitoring period can have a different impact in the signals quality. Therefore, signal processing algorithms were developed to be robust to low quality signals and non-static scenarios. On the other hand, the bio-radar potential can also be maximized if the acquired signals are used pertinently to help identify the subject's psychophysiological state enabling one to act accordingly. The random body motion until now has been seen as a noisy source, however it can also provide useful information regarding subject's state. In this sense, the acquired vital signs as well as other body motions were used in machine learning algorithms with the goal to identify the subject's emotions and thus verify if the remotely acquired vital signs can also provide useful information.O sistema Bio-Radar permite medir sinais vitais com precisão, nomeadamente o sinal respiratório e cardíaco, utilizando ondas eletromagnéticas para esse fim. Desta forma, é possível monitorizar sujeitos de forma remota e confortável durante longos períodos de tempo. Este sistema é baseado no efeito de micro-Doppler, que relaciona a variação de fase do sinal recebido com a alteração da distância entre as antenas do radar e a caixa torácica do sujeito, que ocorre durante a função cardiopulmonar. Considerando a variedade de aplicações onde este sistema pode ser utilizado, é necessário avaliar o seu desempenho quando aplicado em contextos reais e assim demonstrar as vantagens que os sistemas bio-radar podem trazer à população geral. Neste trabalho, foi desenvolvido um protótipo do bio radar com o objetivo de verificar a viabilidade de integrar estes sistemas em aplicações específicas, utilizando soluções robustas e discretas que garantam igualmente o seu bom desempenho, indo simultaneamente de encontro às necessidades do mercado. Considerando estas duas perspetivas em que o sistema pode ser melhorado, foram desenvolvidas soluções de diferentes níveis. Do ponto de vista de hardware, foram desenvolvidas antenas têxteis para serem integradas no estofo de um banco automóvel, alcançando uma solução discreta e fácil de incluir num processo de industrialização. Contextos reais de aplicação implicam períodos de monitorização longos, onde podem ocorrer movimentos corporais involuntários que produzem sinais de elevada amplitude que se sobrepõem aos sinais vitais. Ambientes de monitorização não controlados podem produzir reflexões parasitas variantes no tempo que têm impacto direto no sinal. Adicionalmente, a estrutura física do sujeito e a sua postura durante o período de monitorização podem ter impactos diferentes na qualidade dos sinais. Desta forma, foram desenvolvidos algoritmos de processamento de sinal robustos a sinais de baixa qualidade e a cenários não estáticos. Por outro lado, o potencial do bio radar pode também ser maximizado se os sinais adquiridos forem pertinentemente utilizados de forma a ajudar a identificar o estado psicofisiológico do sujeito, permitindo mais tarde agir em conformidade. O movimento corporal aleatório que foi até agora visto como uma fonte de ruído, pode no entanto também fornecer informação útil sobre o estado do sujeito. Neste sentido, os sinais vitais e outros movimentos corporais adquiridos foram utilizados em algoritmos de aprendizagem automática com o objetivo de identificar as emoções do sujeito e assim verificar que sinais vitais adquiridos remotamente podem também conter informação útil.Programa Doutoral em Engenharia Eletrotécnic
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