4,796 research outputs found

    Digitalization and Development

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    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    Backpropagation Beyond the Gradient

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    Automatic differentiation is a key enabler of deep learning: previously, practitioners were limited to models for which they could manually compute derivatives. Now, they can create sophisticated models with almost no restrictions and train them using first-order, i. e. gradient, information. Popular libraries like PyTorch and TensorFlow compute this gradient efficiently, automatically, and conveniently with a single line of code. Under the hood, reverse-mode automatic differentiation, or gradient backpropagation, powers the gradient computation in these libraries. Their entire design centers around gradient backpropagation. These frameworks are specialized around one specific task—computing the average gradient in a mini-batch. This specialization often complicates the extraction of other information like higher-order statistical moments of the gradient, or higher-order derivatives like the Hessian. It limits practitioners and researchers to methods that rely on the gradient. Arguably, this hampers the field from exploring the potential of higher-order information and there is evidence that focusing solely on the gradient has not lead to significant recent advances in deep learning optimization. To advance algorithmic research and inspire novel ideas, information beyond the batch-averaged gradient must be made available at the same level of computational efficiency, automation, and convenience. This thesis presents approaches to simplify experimentation with rich information beyond the gradient by making it more readily accessible. We present an implementation of these ideas as an extension to the backpropagation procedure in PyTorch. Using this newly accessible information, we demonstrate possible use cases by (i) showing how it can inform our understanding of neural network training by building a diagnostic tool, and (ii) enabling novel methods to efficiently compute and approximate curvature information. First, we extend gradient backpropagation for sequential feedforward models to Hessian backpropagation which enables computing approximate per-layer curvature. This perspective unifies recently proposed block- diagonal curvature approximations. Like gradient backpropagation, the computation of these second-order derivatives is modular, and therefore simple to automate and extend to new operations. Based on the insight that rich information beyond the gradient can be computed efficiently and at the same time, we extend the backpropagation in PyTorch with the BackPACK library. It provides efficient and convenient access to statistical moments of the gradient and approximate curvature information, often at a small overhead compared to computing just the gradient. Next, we showcase the utility of such information to better understand neural network training. We build the Cockpit library that visualizes what is happening inside the model during training through various instruments that rely on BackPACK’s statistics. We show how Cockpit provides a meaningful statistical summary report to the deep learning engineer to identify bugs in their machine learning pipeline, guide hyperparameter tuning, and study deep learning phenomena. Finally, we use BackPACK’s extended automatic differentiation functionality to develop ViViT, an approach to efficiently compute curvature information, in particular curvature noise. It uses the low-rank structure of the generalized Gauss-Newton approximation to the Hessian and addresses shortcomings in existing curvature approximations. Through monitoring curvature noise, we demonstrate how ViViT’s information helps in understanding challenges to make second-order optimization methods work in practice. This work develops new tools to experiment more easily with higher-order information in complex deep learning models. These tools have impacted works on Bayesian applications with Laplace approximations, out-of-distribution generalization, differential privacy, and the design of automatic differentia- tion systems. They constitute one important step towards developing and establishing more efficient deep learning algorithms

    Geophysical Characterisation and Monitoring of Earth Embankment Dams

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    Geophysics has become fundamental in characterising earth embankment dams and identifying preferential seepage pathways, problem areas, and structural defects. The issue of non-uniqueness is profound in the interpretation of geophysical data, with features often attributed to multiple potential sources. This project tackles this issue by applying a multidisciplinary approach comprising traditional techniques to a study site in South Wales. These techniques comprised ground conductivity, magnetometry, and Electrical Resistivity Tomography (ERT). The computation of normalised chargeability data from an Induced Polarisation (IP) survey, normally used for mineral exploration, was applied to delineate between clay and moisture rich areas. This eliminated the issue of non-uniqueness between these two subsurface conditions. The application of these techniques led to successful characterisation of the embankment in terms of its engineered and natural components and identified a potential seepage pathway attributed to surface waters.The Self-Potential (SP) method was evolved into a monitoring solution, building on the research and development of TerraDat Ltd’s SPiVolt system. A methodology was developed to efficiently fabricate and install an SP monitoring network. SP monitoring confirmed the presence of the preferential seepage pathway hypothesised through the characterisation survey and identified a second pathway through the dam’s core.Dŵr Cymru Welsh Water have since used the results of this project to design a targeted grouting campaign and install surface drainage at the site. Comprehensive understanding of the material composition and temporal variations of subsurface conditions is considered essential for ensuring dam and reservoir owners achieve their aims of climate resilience and asset protection. The geophysical characterisation and monitoring methodology presented in this thesis provides an effective low-cost solution that can be applied to multiple scenarios such as landslide investigations, coal tip stability assessments and other hydrogeological problems

    Convergence analysis of a spectral-Galerkin-type search extension method for finding multiple solutions to semilinear problems

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    In this paper, we develop an efficient spectral-Galerkin-type search extension method (SGSEM) for finding multiple solutions to semilinear elliptic boundary value problems. This method constructs effective initial data for multiple solutions based on the linear combinations of some eigenfunctions of the corresponding linear eigenvalue problem, and thus takes full advantage of the traditional search extension method in constructing initials for multiple solutions. Meanwhile, it possesses a low computational cost and high accuracy due to the employment of an interpolated coefficient Legendre-Galerkin spectral discretization. By applying the Schauder's fixed point theorem and other technical strategies, the existence and spectral convergence of the numerical solution corresponding to a specified true solution are rigorously proved. In addition, the uniqueness of the numerical solution in a sufficiently small neighborhood of each specified true solution is strictly verified. Numerical results demonstrate the feasibility and efficiency of our algorithm and present different types of multiple solutions.Comment: 23 pages, 7 figures; Chinese version of this paper is published in SCIENTIA SINICA Mathematica, Vol. 51 (2021), pp. 1407-143

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    20th SC@RUG 2023 proceedings 2022-2023

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    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

    A study of BPS and near-BPS black holes via AdS/CFT

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    In the settings of various AdS/CFT dual pairs, we use results from supersymmetric localiza tion to gain insights into the physics of asymptotically-AdS, BPS black holes in 5 dimensions, and near-BPS black holes in 4 dimensions. We first begin with BPS black holes embedded in the known examples of AdS5/CFT4 dualities. Using the Bethe Ansatz formulation, we compute the superconformal index at large N with arbitrary chemical potentials for all charges and angular momenta, for general N = 1 four-dimensional conformal theories with a holographic dual. We conjecture and bring some evidence that a particular universal contribution to the sum over Bethe vacua dominates the index at large N. For N = 4 SYM, this contribution correctly leads to the entropy of BPS Kerr-Newman black holes in AdS5 Ă— S 5 for arbitrary values of the conserved charges, thus completing the microscopic derivation of their microstates. We also consider theories dual to AdS5 Ă— SE5, where SE5 is a Sasaki-Einstein manifold. We first check our results against the so-called universal black hole. We then explicitly construct the near-horizon geometry of BPS Kerr-Newman black holes in AdS5 Ă— T 1,1 , charged under the baryonic symmetry of the conifold theory and with equal angular momenta. We compute the entropy of these black holes using the attractor mechanism and find complete agreement with field theory predictions. Next, we consider the 3d Chern-Simons matter theory that is holographically dual to massive Type IIA string theory on AdS4 Ă— S 6 . By Kaluza-Klein reducing on S 2 with a background that is dual to the asymptotics of static dyonic BPS black holes in AdS4, we construct a N = 2 supersymmetric gauged quantum mechanics whose ground-state degener acy reproduces the entropy of BPS black holes. We expect its low-lying spectrum to contain information about near-extremal horizons. Interestingly, the model has a large number of statistically-distributed couplings, reminiscent of SYK models

    Mineral snowflakes on exoplanets and brown dwarfs

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    The diversity of exoplanets and brown dwarfs provides ideal atmospheric laboratories to investigate novel physico-chemical regimes. Furthermore, the atmospheres of exoplanets act as the history books of planetary system. However, as observational data improves, the contributions of cloud particles in exoplanet and brown dwarf atmospheres must be adequately accounted for. Microphysical modelling of cloud formation provides the best method to investigate the potentially observable properties of clouds in these atmospheres. Most observed gas-giant exoplanets have been suggested to host mineral clouds which could form `snowflake-like' structures through condensation and constructive collisions. Cloud particle porosity, size and number density are influenced by constructive and destructive collisions. In this thesis, we expand our kinetic non-equilibrium cloud formation model to explore the effects of non-compact, non-spherical cloud particles on cloud structure and their spectroscopic properties. Additionally, we investigate the effects on clouds of collisional growth and fragmentation. The impact of these affects is assessed on prescribed 1D (Tgas-Pgas) profiles in DRIFT-PHOENIX model atmospheres of brown dwarfs and exoplanets. We utilise Mie theory and effective medium theory to study cloud optical depths, where we additionally represent non-spherical cloud particles with a statistical distribution of hollow spheres. We find that micro-porosity can affect the distribution of cloud particles in an exoplanet atmosphere, and that irregular particle shape impacts the optical depth in the near- and mid-infrared. However, we also find that cloud particle collisions driven by turbulence result in fragmentation of cloud particles for exoplanet atmospheres, which also impacts optical depths in the optical and mid-infrared regions. The global distribution and properties of clouds is also important as observations begin to allow for treating exoplanets in their full 3D nature. We therefore apply a hierarchical approach to global cloud formation modelling. We also apply our 1D cloud formation model to profiles extracted from results of 3D General Circulation Models (GCM) for the gas-giant exoplanet WASP-43b and the ultra-hot Jupiter HAT-P-7b, revealing a dramatic difference in the distribution of clouds between these types of exoplanets as a result of stellar radiation heating the day-side of the ultra-hot planets. This results in an asymmetry in cloud structures for the terminators of WASP-43b and more significantly for HAT-P-7b, observable in the optical depth of the clouds at these points, further complicating retrieval of cloud properties from spectra."This work was supported by the Science and Technology Facilities Council (STFC), UK [grant number 2093954]; and the Ă–sterreichische Akademie der Wissenschaften."--Fundin

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
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