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Dead Reckoning of a Fixed-Wing UAV with Inertial Navigation Aided by Optical Flow
(Chapter; Peer reviewed, 2017)This paper provides experimental results for dead reckoning of a fixed-wing UAV using a nonlinear observer (NLO) and a more recent tool called eXogenous Kalman Filter (XKF), which uses the NLO itself as a first-stage filter. ... -
Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network
(Journal article; Peer reviewed, 2019)When a ship experiences a loss of position reference systems, its navigation system typically enters a mode known as dead reckoning (DR) to maintain an estimate of its position. Commercial systems perform this task using ... -
Dead Reckoning System for UAV Using RSSI and Extremum Seeking Control
(Master thesis, 2016)Motivated by the vulnerability of Unmanned Aerial Vehicles (UAVs) to loss of GNSS, this master thesis investigates the development of a backup navigation system. The system can be used by the UAV to navigate back to the ... -
Decentralized Decision Making in Multi-Agent Systems
(Master thesis, 2011)The literature in the feld of multi-agent manufacturing control predom-inantly presents qualitative arguments to motivate its usage. This workpresents quantitative meaning to some of these arguments. The work alsodiscusses ... -
Decentralized Energy Management Concept for Urban Charging Hubs with Multiple V2G Aggregators
(Peer reviewed; Journal article, 2022)This work introduces a decentralized management concept for the urban charging hubs (UCHs) where electric vehicles (EVs) can access multiple charger clusters, each controlled by an aggregator. The given day ahead schedules ... -
Decentralized Fuzzy-Based Voltage Control for LV Distribution Systems
(Chapter, 2017)With the inclusion of Information and Communication Technology (ICT) components into the lowvoltage (LV) distribution grid, some measurement data from smart meters are available for the control of the distribution networks ... -
Decentralized Model Predictive Control for Increased Autonomy in Fleets of ROVs
(Bachelor thesis, 2023)Denne oppgaven dokumenterer design og implementering av en desentralisert modellprediktiv reguleringsarkitektur i et sett med BlueROV2 Heavy. Oppgaven dekker matematisk modellering av undervannsfarkosten, design av MPC, ... -
Decoding Human Emotions From Video-Elicited EEG Responses With Simple Machine Learning Techniques
(Master thesis, 2023)Potensialet til EEG-basert automatisk gjenkjenning av følelser er enormt, fra å kunne forbedre behandlingen av mentale lidelser og utvide kunnskapen om følelsesprossesering hos mennesker til fremskritt innen underholdning ... -
Deep Convolutional Networks for Steering an Off-Road Unmanned Ground Vehicle - End-To-End Learning and Sensor Fusion
(Master thesis, 2018)Autonomous vehicles have numerous advantages compared to standard vehicles. They can reduce fuel consumption, reduce injuries and death, optimize mobility, and reduce traffic congestion. Most lane assists used in consumer ... -
Deep convolutional neural network recovers pure absorbance spectra from highly scatter‐distorted spectra of cells
(Peer reviewed; Journal article, 2020)Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state‐of‐the‐art Mie extinction extended multiplicative signal correction (ME‐EMSC) ... -
Deep learning applications in medical imaging - Deep Convolutional Generative adversarial networks
(Master thesis, 2018)Medical imaging Medical imaging makes it possible to examine internal human tissue without performing surgery. Common medical imaging techniques include magnetic resonance (MR), computer tomography (CT), X-ray and ultrasound ... -
Deep learning assisted physics-based modeling of aluminum extraction process
(Peer reviewed; Journal article, 2023)Modeling complex physical processes such as the extraction of aluminum is mainly done using pure physics-based models derived from first principles. However, the accuracy of these models can often suffer due to a partial ... -
Deep learning based decomposition for visual navigation in industrial platforms
(Peer reviewed; Journal article, 2021)In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS ... -
Deep learning based keypoint rejection system for underwater visual ego-motion estimation
(Peer reviewed; Journal article, 2020)Most visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) systems rely heavily on robust keypoint detection and matching. With regards to images taken in the underwater environment, phenomena like ... -
Deep Learning for Autonomous Navigation in Crop Fields
(Master thesis, 2020)Abstract will be available on 2023-01-27 -
Deep Learning for the Classification of EEG Time-Frequency Representations
(Master thesis, 2018)This thesis is a report on the implementation and evaluation of a new method classifying EEG signals. The method involves applying either the Short-time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) or ... -
Deep Learning-Based Multi-Camera Situational Awareness and Global Localization for Autonomous Ships
(Master thesis, 2019)Jeg presenterer design, implementasjon og testing av et ikke-driftende lokaliseringssystem i seks frihetsgrader for skip, basert på semantisk bildesegmentering ved bruk av et dypt neuralt nettverk. Kun skipsmonterte ... -
Deep Monocular Depth Estimation for Autonomous Underwater Vehicles
(Master thesis, 2022)Å utforske og utnytte verdenshavene på en bærekraftig måte er avgjørende for å løse utfordringer som global oppvarming. Autonome undervannsroboter har som hensikt å erstatte menneskelige operatører i farlige undervannsmiljøer, ... -
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
(Peer reviewed; Journal article, 2022)In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)—a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and ... -
Deep Reinforcement Learning Applied to Managed Pressure Drilling
(Chapter, 2020)During drilling operations, maintaining a desired downhole pressure between pressure margins is crucial to avoid damage to the formation and the well. The process is highly nonlinear, changing with depth, and every section ...