Browsing NTNU Open by Title
Now showing items 56298-56317 of 100943
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Machine Learning and First Principles Modeling Applied to Multiphase Flow Estimation
(Doctoral theses at NTNU;2020:407, Doctoral thesis, 2020)Accurate knowledge of multiphase flowrates produced by each well in an oil and gas production system is important for performing production optimization, flow assurance and reservoir management. Among the alternatives, ... -
Machine Learning and Image Processing for the Study of Fluid Particle Breakage in Turbulent Flow
(Master thesis, 2020)Hovedformålet med denne oppgaven er å lage maskinlæringsmodeller for bruk i en allerede eksisterende bildeanalysekode for å kunne spore oljedråper i en turbulent strøm av vann. I tillegg, har prosjektet som mål å beskrive ... -
Machine learning applied to GPU test generation
(Master thesis, 2023)Denne oppgaven har undersøkt hvordan finne effektiv tester og stimuli for simulerings basert verifikasjons av digital kretser. Nåværende metoder er baseres på avgrenset tilfeldig simulering og menneskelig involvering. Dette ... -
A Machine Learning Approach for Determining Reference Wells in the Norwegian Continental Shelf
(Master thesis, 2018)An unsupervised learning solution for selecting relevant reference wells for a new well that is to be drilled in the Norwegian Continental Shelf (NCS), shall be made with readily available software tools such as Python and ... -
Machine Learning Approach for Hydrogen Safety in Confined Spaces: Forecasting Pressure Peaking Phenomenon
(Master thesis, 2023)Hydrogen is the most abundant element in the universe and has gained significant attention as a clean and renewable energy source. However, hydrogen also poses significant safety risks, especially when it is stored, ... -
A Machine Learning Approach to Analyze Natural Hazards Accidents Scenarios
(Peer reviewed; Journal article, 2022)Climate change has contributed to an increasing frequency and severity of natural hazards accidents over recent years, and the increasing trend is expected to continue and escalate. Globally, demographics are changing and ... -
A Machine Learning Approach to Investigate Fronto-Parietal Neural Ensemble Dynamics During Complex
(Chapter, 2020)Brain circuits exhibit very complex dynamics, where individual neurons fire action potentials determining coordinated activity patterns. During behavior, a multitude of brain areas are engaged in planning and execution. A ... -
A machine learning approach to predict chattering alarms
(Peer reviewed; Journal article, 2020)The alarm system plays a vital role to ensure safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only and provide guidance to a set of corrective actions ... -
A Machine Learning Approach to Predict the Materials' Susceptibility to Hydrogen Embrittlement
(Journal article; Peer reviewed, 2023) -
Machine Learning Approaches to Automatically Detect Glacier Snow Lines on Multi-Spectral Satellite Images
(Peer reviewed; Journal article, 2022)Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers ... -
Machine learning assisted low-frequency model building for AVO inversion
(Master thesis, 2018)AVO inversion is a valuable tool to estimate absolute reservoir properties from prestack seismic data. The bandwidth limitation of the seismic requires that the missing low frequencies must be added to the inversion in ... -
Machine learning augmented reduced-order models for FFR-prediction
(Peer reviewed; Journal article, 2021)Computational predictions in cardiovascular medicine have largely relied on explicit models derived from physical and physiological principles. Recently, the application of artificial intelligence in cardiovascular medicine ... -
Machine Learning based Acoustic Anomaly Detection of Gas Leakages in Industrial Environments
(Master thesis, 2022)Målet med denne masteroppgaven var å utvikle en akustisk gasslekkasjedetektor basert på maskinlæring. Gasslekkasjer kan være frustrerende for industrielle anleggseiere da de fører til energiforbruk, høyere kostnader, økt ... -
Machine learning based digital twin framework for aquaculture net cage system
(Master thesis, 2022)I denne master oppgaven er det presentert et rammeverk for digital tvilling for fiskeoppdrettsanlegg ved å bruke numerisk simulering, maskin læring og sensor data. De numeriske simuleringene er utført med programvaren FhSim ... -
Machine Learning Based Digital Twins for Temperature and Power Dynamics of a Household
(Master thesis, 2023)Utfordringer med kraftproduksjonen i Europa og den stadig økende elektrifiseringen av samfunnet har resultert i økte kostnader ved strømforbruk de siste par årene. Dette har stor innvirkning på økonomien til husholdninger ... -
Machine Learning Based Heuristic Technique for Multi-response Machining Process
(Peer reviewed; Journal article, 2020)Manufacturing process variables influence the quality of products substantially. It is unquestionably difficult to model the manufacturing processes that include a large number of variables and responses. Development of ... -
Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces
(Peer reviewed; Journal article, 2021)It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) ... -
A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living
(Journal article; Peer reviewed, 2021)Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity ... -
A machine learning enabled ultra-fine grain design strategy of Mg–Mn-based alloys
(Peer reviewed; Journal article, 2023)Grain size is the critical characteristic of ultra-fine grain Magnesium (Mg), which is a concrete representation of the whole heat deformation procedure. In this paper, a design strategy was proposed to quantitatively ... -
Machine Learning for Adverse Event Analysis in Healthcare: Identifying Opportunities and Conducting a Classification Study
(Master thesis, 2023)Uønskede hendelser er en nøkkelindikator for pasientsikkerhet på sykehus. Slike hendelser innebærer utilsiktede pasientskader, og selv om noen av disse er uunngåelige, viser forskning at mange uønskede hendelser kan ...