Search for dissertations about: "synthetic models"
Showing result 1 - 5 of 279 swedish dissertations containing the words synthetic models.
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1. Regional Geoid Determination Methods for the Era of Satellite Gravimetry : Numerical Investigations Using Synthetic Earth Gravity Models
Abstract : It is the purpose of this thesis to investigate different regional geoid determination methods with respect to their feasibility for use with a future GOCE satellite-only Earth Gravity Model (EGM). This includes investigations of various techniques, which involve different approximations, as well as the expected accuracy. READ MORE
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2. Probabilistic Sequence Models with Speech and Language Applications
Abstract : Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. READ MORE
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3. The synthetic chromosphere : Results and techniques with a numerical approach
Abstract : Realistic numerical simulations of the solar atmosphere can be used to interpret different phenomena observed on the solar surface. To gain insight into the atmospheric physical conditions, we compare the observations with 3D radiative magnetohydrodynamic models combined with forward modeling (radiative transfer). READ MORE
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4. Transport, Mobility, and Workplace Location : Models and Applications
Abstract : Travel demand analysis is one of the core constituents of transportation studies. Therequired insight to maintain and develop a sustainable transportation system, in additionto learning from previous research globally and locally, is generated from studyingthe effects of previous policies, investigating future possibilities and potential outcomes,and describing the current situation. READ MORE
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5. Synthetic data for visual machine learning : A data-centric approach
Abstract : Deep learning allows computers to learn from observations, or else training data. Successful application development requires skills in neural network design, adequate computational resources, and a training data distribution that covers the application do-main. READ MORE