Sökning: "kernel transformation"

Hittade 3 uppsatser innehållade orden kernel transformation.

  1. 1. Nonlinear Methods of Aerodynamic Data-driven Reduced Order Modeling

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för beräkningsvetenskap

    Författare :Arvid Forsberg; [2022]
    Nyckelord :machine learning; aerodynamics; autoencoder; kernel transformation; principal component analysis; nonlinear; regression; modeling; surrogate model; reduced order modeling; neural network;

    Sammanfattning : Being able to accurately approximate outputs of computationally expensive simulations for arbitrary input parameters, also called missing points estimation, is central in many different areas of research and development with applications ranging from uncertainty propagation to control system design to name a few. This project investigates the potential of kernel transformations and nonlinear autoencoders as methods of improving the accuracy of the proper orthogonal decomposition method combined with regression. LÄS MER

  3. 2. Parallel Simulation of SystemC Loosely-Timed Transaction Level Models

    Master-uppsats, KTH/Skolan för informations- och kommunikationsteknik (ICT)

    Författare :Konstantinos Sotiropoulos Pesiridis; [2017]
    Nyckelord :parallel discrete event simulation; conservative synchronization algorithms; transaction level models; SystemC TLM 2.0;

    Sammanfattning : Parallelizing the development cycles of hardware and software is becoming the industry’s norm for reducing time to market for electronic devices. In the absence of hardware, software development is based on a virtual platform; a fully functional software model of a system under development, able to execute unmodified code. LÄS MER

  4. 3. Statistical Parametric Mapping of fMRI data using Spectral Graph Wavelets

    Master-uppsats, Medicinsk informatik; Tekniska högskolan

    Författare :Hamid Behjat; [2012]
    Nyckelord :Statistical parametric mapping; fMRI; Spectral graph theory; Graph wavelet transform; Wavelet thresholding;

    Sammanfattning : In typical statistical parametric mapping (SPM) of fMRI data, the functional data are pre-smoothed using a Gaussian kernel to reduce noise at the cost of losing spatial specificity. Wavelet approaches have been incorporated in such analysis by enabling an efficient representation of the underlying brain activity through spatial transformation of the original, un-smoothed data; a successful framework is the wavelet-based statistical parametric mapping (WSPM) which enables integrated wavelet processing and spatial statistical testing. LÄS MER