Sökning: "Component Decomposition"

Visar resultat 1 - 5 av 47 uppsatser innehållade orden Component Decomposition.

  1. 1. Advancements in Dependability Analysis of Safety-Critical Systems : Addressing Specification Formulation and Verification Challenges

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Zelin Yu; [2023]
    Nyckelord :Specification theory; Contracts; Automata theory; Refinement; Specifikationsteori; Kontrakt; Automatteori; Förfina;

    Sammanfattning : Safety-critical systems have garnered increasing attention, particularly regarding their dependability analysis. In modern times, these systems comprise numerous components, making it crucial to verify that lower-level components adhere to their specifications will ensure the overall system’s compliance with its top-level specification. LÄS MER

  3. 2. Decomposing Import Price Inflation in the EU

    Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

    Författare :Isak Skaghammar; Johannes Reuterwall; [2023]
    Nyckelord :Import price inflation; decomposition; supply and demand; Business and Economics;

    Sammanfattning : This thesis examines what drives import price inflation in the EU by decomposing it into supply and demand driven inflation. The decomposition is done by using product level import data retrieved from Eurostat. The paper examines the period from 2019-01 to 2023-01 which captures events such as the Covid-19 and the war in Ukraine. LÄS MER

  4. 3. 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

  5. 4. Accelerating MCR-ALS decomposition of hyperspectral images using k-means clustering

    Kandidat-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

    Författare :Eric Kull; [2022]
    Nyckelord :k-means; clustering; MCR-ALS; hyperspectral image; Physics and Astronomy;

    Sammanfattning : A hyperspectral image may be decomposed into component spectra and their distri- bution in the image to simplify analysis by revealing underlying patterns and reducing the dimensionality of the image; this may be achieved by the algorithm MCR-ALS. However, the algorithm is time consuming, but could be accelerated by a data re- duction. LÄS MER

  7. 5. Semi-Automatic Analysis and Visualization of Cardiac 4D Flow CT

    Master-uppsats, Linköpings universitet/Institutionen för hälsa, medicin och vård

    Författare :Anthony van Oosten; [2022]
    Nyckelord :4D CT; CT; 4D flow; Flow Component Analysis; Singular value decomposition; left atrial appendage; LAA; CFD; 3 chamber view; computed tomography; maximum outflow velocity; volume rendered CT; semi-automatic; visualization; flow visualization; cardiac views; semi-automatic analysis; semi-automatic visualization; visualization of 4D flow;

    Sammanfattning : The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. LÄS MER