6,814 research outputs found

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Unstable Periodic Orbits: a language to interpret the complexity of chaotic systems

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    Unstable periodic orbits (UPOs), exact periodic solutions of the evolution equation, offer a very powerful framework for studying chaotic dynamical systems, as they allow one to dissect their dynamical structure. UPOs can be considered the skeleton of chaotic dynamics, its essential building blocks. In fact, it is possible to prove that in a chaotic system, UPOs are dense in the attractor, meaning that it is always possible to find a UPO arbitrarily near any chaotic trajectory. We can thus think of the chaotic trajectory as being approximated by different UPOs as it evolves in time, jumping from one UPO to another as a result of their instability. In this thesis we provide a contribution towards the use of UPOs as a tool to understand and distill the dynamical structure of chaotic dynamical systems. We will focus on two models, characterised by different properties, the Lorenz-63 and Lorenz-96 model. The process of approximation of a chaotic trajectory in terms of UPOs will play a central role in our investigation. In fact, we will use this tool to explore the properties of the attractor of the system under the lens of its UPOs. In the first part of the thesis we consider the Lorenz-63 model with the classic parameters’ value. We investigate how a chaotic trajectory can be approximated using a complete set of UPOs up to symbolic dynamics’ period 14. At each instant in time, we rank the UPOs according to their proximity to the position of the orbit in the phase space. We study this process from two different perspectives. First, we find that longer period UPOs overwhelmingly provide the best local approximation to the trajectory. Second, we construct a finite-state Markov chain by studying the scattering of the trajectory between the neighbourhood of the various UPOs. Each UPO and its neighbourhood are taken as a possible state of the system. Through the analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a different interpretation of the mixing processes occurring in the system by taking advantage of the concept of quasi-invariant sets. In the second part of the thesis we provide an extensive numerical investigation of the variability of the dynamical properties across the attractor of the much studied Lorenz ’96 dynamical system. By combining the Lyapunov analysis of the tangent space with the study of the shadowing of the chaotic trajectory performed by a very large set of unstable periodic orbits, we show that the observed variability in the number of unstable dimensions, which shows a serious breakdown of hyperbolicity, is associated with the presence of a substantial number of finite-time Lyapunov exponents that fluctuate about zero also when very long averaging times are considered

    Emerging Power Electronics Technologies for Sustainable Energy Conversion

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    This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches

    Modified Theories of Gravity and Cosmological Applications

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    This reprint focuses on recent aspects of gravitational theory and cosmology. It contains subjects of particular interest for modified gravity theories and applications to cosmology, special attention is given to Einstein–Gauss–Bonnet, f(R)-gravity, anisotropic inflation, extra dimension theories of gravity, black holes, dark energy, Palatini gravity, anisotropic spacetime, Einstein–Finsler gravity, off-diagonal cosmological solutions, Hawking-temperature and scalar-tensor-vector theories

    Intelligent computing : the latest advances, challenges and future

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    Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Methods and Results of Power Cycling Tests for Semiconductor Power Devices

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    This work intends to enhance the state of the research in power cycling tests with statements on achievable measurement accuracy, proposed test bench topologies and recommendations on improved test strategies for various types of semiconductor power devices. Chapters 1 and 2 describe the current state of the power cycling tests in the context of design for reliability comprising applicable standards and lifetime models. Measurement methods in power cycling tests for the essential physical parameters are explained in chapter 3. The dynamic and static measurement accuracy of voltage, current and temperature are discussed. The feasibly achievable measurement delay tmd of the maximal junction temperature Tjmax, its consequences on accuracy and methods to extrapolate to the time point of the turn-off event are explained. A method to characterize the thermal path of devices to the heatsink via measurements of the thermal impedance Zth is explained. Test bench topologies starting from standard setups, single to multi leg DC benches are discussed in chapter 4. Three application-closer setups implemented by the author are explained. For tests on thyristors a test concept with truncated sinusoidal current waveforms and online temperature measurement is introduced. An inverter-like topology with actively switching IGBTs is presented. In contrast to standard setups, there the devices under test prove switching capability until reaching the end-of-life criteria. Finally, a high frequency switching topology with low DC-link voltage and switching losses contributing significantly to the overall power losses is presented providing new degrees of freedom for setting test conditions. The particularities of semiconductor power devices in power cycling tests are thematized in chapter 5. The first part describes standard packages and addressed failure mechanisms in power cycling. For all relevant power electronic devices in silicon and silicon carbide, the devices’ characteristics, methods for power cycling and their consequences for test results are explained. The work is concluded and suggestions for future work are given in chapter 6.:Abstract 1 Kurzfassung 3 Acknowledgements 5 Nomenclature 10 Abbreviations 10 Symbols 12 1 Introduction 19 2 Applicable Standards and Lifetime Models 25 3 Measurement parameters in power cycling tests 53 4 Test Bench Topologies 121 5 Semiconductor Power Devices in Power Cycling 158 6 Conclusion and Outlook 229 References 235 List of Publications 253 Theses 257Diese Arbeit bereichert den Stand der Wissenschaft auf dem Gebiet von Lastwechseltests mit Beiträgen zu verbesserter Messgenauigkeit, vorgeschlagenen Teststandstopologien und verbesserten Teststrategien für verschiedene Arten von leistungselektronischen Bauelementen. Kurzgefasst der Methodik von Lastwechseltests. Das erste Themengebiet in Kapitel 1 und Kapitel 2 beschreibt den aktuellen Stand zu Lastwechseltests im Kontext von Design für Zuverlässigkeit, welcher in anzuwendenden Standards und publizierten Lebensdauermodellen dokumentiert ist. Messmethoden für relevante physikalische Parameter in Lastwechseltests sind in Kapitel 3. erläutert. Zunächst werden dynamische und statische Messgenauigkeit für Spannung, Strom und Temperaturen diskutiert. Die tatsächlich erreichbare Messverzögerung tMD der maximalen Sperrschichttemperatur Tjmax und deren Auswirkung auf die Messgenauigkeit der Lastwechselfestigkeit wird dargelegt. Danach werden Methoden zur Rückextrapolation zum Zeitpunkt des Abschaltvorgangs des Laststroms diskutiert. Schließlich wird die Charakterisierung des Wärmepfads vom Bauelement zur Wärmesenke mittels Messung der thermischen Impedanz Zth behandelt. In Kapitel 4 werden Teststandstopologien beginnend mit standardmäßig genutzten ein- und mehrsträngigen DC-Testständen vorgestellt. Drei vom Autor umgesetzte anwendungsnahe Topologien werden erklärt. Für Tests mit Thyristoren wird ein Testkonzept mit angeschnittenem sinusförmigem Strom und in situ Messung der Sperrschichttemperatur eingeführt. Eine umrichterähnliche Topologie mit aktiv schaltenden IGBTs wird vorgestellt. Zuletzt wird eine Topologie mit hoch frequent schaltenden Prüflingen an niedriger Gleichspannung bei der Schaltverluste signifikant zur Erwärmung der Prüflinge beitragen vorgestellt. Dies ermöglicht neue Freiheitsgrade um Testbedingungen zu wählen. Die Besonderheiten von leistungselektronischen Bauelementen werden in Kapitel 5 thematisiert. Der erste Teil beschreibt Gehäusetypen und adressierte Fehlermechanismen in Lastwechseltests. Für alle untersuchten Bauelementtypen in Silizium und Siliziumkarbid werden Charakteristiken, empfohlene Methoden für Lastwechseltests und Einflüsse auf Testergebnisse erklärt. Die Arbeit wird in Kapitel 6 zusammengefasst und Vorschläge zu künftigen Arbeiten werden unterbreitet.:Abstract 1 Kurzfassung 3 Acknowledgements 5 Nomenclature 10 Abbreviations 10 Symbols 12 1 Introduction 19 2 Applicable Standards and Lifetime Models 25 3 Measurement parameters in power cycling tests 53 4 Test Bench Topologies 121 5 Semiconductor Power Devices in Power Cycling 158 6 Conclusion and Outlook 229 References 235 List of Publications 253 Theses 25
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