Browsing by Subject "Machine Learning"

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  • Analysis of Gravitational-Wave Signals from Binary Neutron Star Mergers Using Machine Learning 
    Schäfer, Marlin Benedikt (Hannover : Gottfried Wilhelm Leibniz Universität, 2019)
    Gravitational waves are now observed routinely. Therefore, data analysis has to keep up with ever improving detectors. One relatively new tool to search for gravitational wave signals in detector data are machine learning ...
  • Bayessches Transferlernen für die Semantische Segmentierung von Luftbildern 
    Vogt, Karsten (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2019)
    Diese Arbeit beschäftigt sich mit dem Problem der semantischen Segmentierung von Luftbildern in Landbedeckungsklassen. Maschinelle Lernverfahren bieten dabei sehr robuste Methoden zur Erzeugung von hochgenauen Klassifikatoren, ...
  • Bias Assessments of Benchmarks for Link Predictions over Knowledge Graphs 
    Sawischa, Sammy Fabian (Hannover : Gottfried Wilhelm Leibniz Universität, 2023-04-26)
    Link prediction (LP) aims to tackle the challenge of predicting new facts by reasoning over a knowledge graph (KG). Different machine learning architectures have been proposed to solve the task of LP, several of them ...
  • A Cognitive Assistance System To Support The Implementation Of Machine Learning Applications In Manufacturing 
    Rosemeyer, Jannik; Beck, Laurenz; Metternich, Joachim (Hannover : publish-Ing., 2023)
    Despite the increasing spread of digitalisation in manufacturing, humans will still play an important role in future production environments. Evidently, their role will change from physical to rather cognitive tasks, such ...
  • Computer-Aided Analysis of Video Comments for Requirements Analysis 
    Kristo, Eklekta (Hannover : Gottfried Wilhelm Leibniz Universität Hannover, Institut für Praktische Informatik, 2021-02-12)
    In dieser Arbeit werden Anforderungen für die Anforderungsanalyse aus den Youtube Kommentaren von vision videos extrahiert. Der Prozess der Erstellung und Vorbereitung eines Datensatzes wird beschrieben und die Güte von ...
  • Conceptualization of an AI-based Skills Forecasting Model for Small and Medium-Sized Enterprises (SMEs) 
    Frierson, Charlotte; Wrobel, Jana; Senderek, Roman; Stich, Volker (Hannover : publish-Ing., 2023)
    Forecasting-based skills management, which is oriented to the respective corporate goals, is gaining enormous importance as a central management tool. The aim is to predict future skills requirements and match them with ...
  • Implementation of Machine Learning to Improve the Decision-Making Process of End-of-Usage Products in the Circular Economy 
    Diem, Michael; Braun, Anja; Louw, Louis (Hannover : publish-Ing., 2020)
    Rising consumption due to a growing world population and increasing prosperity, combined with a linear economic system have led to a sharp increase in garbage collection, general pollution of the environment and the threat ...
  • Intelligent signal processing for digital healthcare monitoring 
    Conte Alcaraz, Javier (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022)
    Ein gesunder Gang ist ein komplexer Prozess und erfordert ein Gleichgewicht zwischen verschiedenen neurophysiologischen Systemen im Körper und gilt als wesentlicher Indikator für den physischen und kognitiven Gesundheitszustand ...
  • Interpreting Text Classification with Human-Understandable Counterfactual Instances 
    Li, Teng (Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2022)
    As the omnipresent machine learning models play increasingly important roles in our society, powerful interpretation tools to uncover their black boxes are needed. On the other hand, proven by psychological study, we ...
  • Machine learning-based available bandwidth estimation 
    Khangura, Sukhpreet Kaur (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2019)
    Today’s Internet Protocol (IP), the Internet’s network-layer protocol, provides a best-effort service to all users without any guaranteed bandwidth. However, for certain applications that have stringent network performance ...
  • Managing Disruptions in Production with Machine Learning 
    Samsonov, Vladimir; Enslin, Christmarie; Lütkehoff, Ben; Steinlein, Felix; Lütticke, Daniel; Stich, Volker (Hannover : publish-Ing., 2020)
    Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly ...
  • Meshfree modelling of metal cutting using phenomenological and data-driven material models 
    Huang, Dengpeng (Hannover : Institut für Kontinuumsmechanik, 2020)
    Numerical modelling of chip formation is important for a better understanding thus for improvement of the high speed metal cutting process. The objective of this work is to develop a meshfree numerical simulation framework ...
  • Numerical Methods for Algorithmic Systems and Neural Networks 
    Kinnewig, Sebastian; Kolditz, Leon; Roth, Julian; Wick, Thomas (Hannover : Institut für Angewandte Mathematik, Leibniz Universität Hannover, 2022-03-21)
    These lecture notes are devoted to numerical concepts and solution of algorithmic systems and neural networks. The course is divided into four parts: traditional AI (artificial intelligence), deep learning in neural networks, ...
  • Part Based Mold Quotation With Methods Of Machine Learning 
    Brede, Sebastian; Küster, Benjamin; Stonis, Malte; Mücke, Mike; Overmeyer, Ludger (Hannover : publish-Ing., 2020)
    The dominating mass-manufacturing process of today is plastic injection molding. This production process uses economies of scale because parts are produced in seconds at marginal cost of plastics. However, upfront investment ...
  • Self-optimizing cutting process using learning process models 
    Denkena, Berend; Dittrich, Marc-André; Uhlich, Florian (Amsterdam : Elsevier, 2016)
    The continuous integration of manufacturing systems and sensory components leads to an increasing amount of available process data. In addition, new database systems and the parallelization of data processing enable to ...
  • Solar irradiance forecast from all-sky images using machine learning 
    Crisosto González, Cristian Daniel (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2023)
    The novel method presented here comprises techniques for cloud coverage percentage forecasts, cloud movement forecast and the subsequently prediction of the global horizontal irradiance (GHI) using all-sky images and ...
  • Supporting Explainable AI on Semantic Constraint Validation 
    Gercke, Julian Alexander (Hannover : Gottfried Wilhelm Leibniz Universität, 2022-06-14)
    There is a rising number of knowledge graphs available published through various sources. The enormous amount of linked data strives to give entities a semantic context. Using SHACL, the entities can be validated with ...
  • Synthesis of Artificial Coating Images and Parameter Data Sets in Electrode Manufacturing 
    Kampker, Achim; Heimes, Heiner Hans; Dorn, Benjamin; Clever, Henning; Drescher, Marcel; Ludwigs, Robert (Hannover : publish-Ing., 2023)
    Driven by continuous cost pressure and increasing market requirements, the optimisation of the lithium-ion battery production is focus of attention. In order to save time and costs, machine learning (ML) represent a promising ...
  • Thinking outside the graph: scholarly knowledge graph construction leveraging natural language processing 
    Jaradeh, Mohamad Yaser (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022-11-23)
    Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. The document-oriented workflows in science publication have reached the limits of ...
  • Transmissibility-based monitoring and combination of damage feature decisions within a holistic structural health monitoring framework 
    Tsiapoki, Stavroula (Hannover : Institut für Statik und Dynamik, Leibniz Universität Hannover, 2018)
    Over the past forty years, intensive research has been carried out in the field of structural health monitoring (SHM), since the identification of damage at an early stage contributes to avoiding structural failure and ...