Essays about: "inverse problem"

Showing result 1 - 5 of 109 essays containing the words inverse problem.

  1. 1. Limited angle reconstruction for 2D CT based on machine learning

    University essay from KTH/Skolan för teknikvetenskap (SCI)

    Author : Eric Oldgren; Knut Salomonsson; [2023]
    Keywords : CT; computed tomography; limited angle; machine learning; limited data; ill posed problem; inverse problem;

    Abstract : The aim of this report is to study how machine learning can be used to reconstruct 2 dimensional computed tomography images from limited angle data. This could be used in a variety of applications where either the space or timeavailable for the CT scan limits the acquired data.In this study, three different types of models are considered. READ MORE

  3. 2. Approach for frequency response-calibration for microphone arrays

    University essay from KTH/Hälsoinformatik och logistik

    Author : Jacob Drotz; [2023]
    Keywords : microphone array; frequency response; calibration; sine sweep; inverse filter; digital signal processing DSP ; convolution; Fast Fourier Transform FFT ; Discrete Fourier Transform DFT ; acoustic measurements; audio engineering; mikrofonarray; frekvenssvar; kalibrering; sinussvep; inverterat filter; digital signalbehandling; faltning; Fast Fourier Transform FFT ; Discrete Fourier Transform DFT ; akustiska mätningar; ljudteknik;

    Abstract : Matched frequency responses are a fundamental starting point for a variety ofimplementations for microphone arrays. In this report, two methods for frequencyresponse-calibration of a pre-assembled microphone array are presented andevaluated. READ MORE

  4. 3. An Empirical Investigation of The Effect of Proxy Response and The Merits of Its Remedial Measures

    University essay from Högskolan Dalarna/Institutionen för information och teknik

    Author : Charles Edward Okon; Randi Kalanika Assalaarachchi; [2023]
    Keywords : missing data; proxy response; missingness mechanism; error-in-variable; logistic regression; Monte-Carlo simulation; missing at random; complete case analysis; proxy substitution; multiple imputation; inverse probability treatment weighting;

    Abstract : In the event of missing data, substitution of data from proxy sources are usually considered a very useful alternative when available to avoid the problem of missingness. Nonetheless, research has also shown that this approach often induces “response bias”. READ MORE

  5. 4. Short-horizon Prediction of Indoor Temperature using Low-Order Thermal Networks : A case study of thermal models for heat-system control applications

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jonas Cederberg; [2023]
    Keywords : Lumped Parameter Thermal Network; Resistance-Capacitance model; Parameter estimation; Modelling of Dynamical Systems; Thermal modelling; Temperature Prediction; Termiskt nätverk med klumpade parametrar; Mostånds-kapacitans modell; Parameterestimering; Modellering av dynamiska system; Termisk modellering; Temperaturprediktion.;

    Abstract : Optimizing and controlling the heating systems in buildings is one way to decrease their load on the power grid, as well as introduce load flexibility to be used in Demand Response (DR) applications. A requirement in occupied buildings is that the thermal comfort of the residents is guaranteed, making the optimization of heating systems a constrained problem with respect to indoor temperature. READ MORE

  6. 5. Solving Partial Differential Equations With Neural Networks

    University essay from Uppsala universitet/Matematiska institutionen

    Author : Håkan Karlsson Faronius; [2023]
    Keywords : Partial differential equations; neural networks; physics-informed neural networks; deep ritz method; fourier neural operator; importance sampling; inverse problems;

    Abstract : In this thesis three different approaches for solving partial differential equa-tions with neural networks will be explored; namely Physics-Informed NeuralNetworks, Fourier Neural Operators and the Deep Ritz method. Physics-Informed Neural Networks and the Deep Ritz Method are unsupervised machine learning methods, while the Fourier Neural Operator is a supervised method. READ MORE