Now showing items 181-200 of 1407

    • A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet 

      Chen, Hao; Birkelund, Yngve; Ricaud, Benjamin; Zhang, Qixia (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      As renewable energy sources offshore wind energy develop quickly, countries like Norway with long coastlines are exploring their potential. However, the diverse wind resources across different regions of Norway present challenges for study for effective utilization of offshore wind energy. This study proposes a novel method that utilizes transfer learning techniques to analyse the resource differences ...
    • Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation 

      Tomasetti, Luca; Hansen, Stine; Khanmohammadi, Mahdieh; Engan, Kjersti; Høllesli, Liv Jorunn; Kurz, Kathinka Dæhli; Kampffmeyer, Michael Christian (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-01)
      Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate. While numerous deep neural network approaches have recently been proposed to tackle this problem, these methods require large amounts of annotated regions during training, which can be impractical in the medical ...
    • Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat-2 Sea Ice Thickness Observations 

      Zhang, Yong-Fei; Bushuk, Mitchell; Winton, Michael; Hurlin, Bill; Gregory, William; Landy, Jack Christopher; Jia, Liwei (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-15)
      Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from CryoSat-2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the ...
    • A Contextually Supported Abnormality Detector for Maritime Trajectories 

      Olesen, Kristoffer Vinther; Boubekki, Ahcene; Kampffmeyer, Michael Christian; Jenssen, Robert; Christensen, Anders Nymark; Hørlück, Sune; Clemmensen, Line H. (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-31)
      The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising ...
    • Mapping the extent of giant Antarctic icebergs with deep learning 

      Braakmann-Folgmann, Anne Christina; Shepherd, Andrew; Hogg, David; Redmond, Ella (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-09)
      Icebergs release cold, fresh meltwater and terrigenous nutrients as they drift and melt, influencing the local ocean properties, encouraging sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, changes in their area and thickness have to be monitored along their trajectories. While the locations of large icebergs are operationally tracked ...
    • ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement 

      Hansen, Stine; Gautam, Srishti; Salahuddin, Suaiba Amina; Kampffmeyer, Michael Christian; Jenssen, Robert (Journal article; Tidsskriftartikkel, 2023-08-02)
      A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential ...
    • GNSS Scintillations in the Cusp, and the Role of Precipitating Particle Energy Fluxes 

      Ivarsen, Magnus Fagernes; Jin, Yaqi; Spicher, Andres; St-Maurice, Jean-Pierre; Park, Jaeheung; Billett, Daniel (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-16)
      Using a large data set of ground-based GNSS scintillation observations coupled with in situ particle detector data, we perform a statistical analysis of both the input energy flux from precipitating particles, and the observed occurrence of density irregularities in the northern hemisphere cusp. By examining trends in the two data sets relating to geomagnetic activity, we conclude that observations ...
    • Automated tilt compensation in acoustic microscopy 

      Gupta, Shubham Kumar; Habib, Anowarul; Kumar, Prakhar; Melandsø, Frank; Ahmad, Azeem (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-12)
      Scanning acoustic microscopy (SAM) is a potent and nondestructive technique capable of producing three-dimensional topographic and tomographic images of specimens. This is achieved by measuring the differences in time of flight (ToF) of acoustic signals emitted from various regions of the sample. The measurement accuracy of SAM strongly depends on the ToF measurement, which is affected by tilt in ...
    • View it like a radiologist: Shifted windows for deep learning augmentation of CT images 

      Østmo, Eirik Agnalt; Wickstrøm, Kristoffer; Radiya, Keyur; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-23)
      Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning models rely on augmentation techniques that treat medical images as natural images. For contrast-enhanced Computed Tomography (CT) images in particular, the signals producing the ...
    • On mechanisms for high-frequency pump-enhanced optical emissions at 557.7 and 630.0gnm from atomic oxygen in the high-latitude F-region ionosphere 

      Leyser, Thomas B.; Sergienko, Tima; Brändström, Urban; Gustavsson, Björn Johan; Rietveld, Michael T. (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-13)
      The EISCAT (European Incoherent Scatter Scientific Association) Heating facility was used to transmit powerful high-frequency (HF) electromagnetic waves into the Fregion ionosphere to enhance optical emissions at 557.7 and 630.0 nm from atomic oxygen. The emissions were imaged by several stations of ALIS (Auroral Large Imaging System) in northern Sweden, and the EISCAT UHF incoherent scatter radar ...
    • Impact of the Nares Strait sea ice arches on the long-term stability of the Petermann Glacier ice shelf 

      Prakash, Abhay; Zhou, Qin; Hattermann, Tore; Kirchner, Nina (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-12)
      One of the last remaining floating tongues of the Greenland ice sheet (GrIS), the Petermann Glacier ice shelf (PGIS), is seasonally shielded from warm Atlantic water (AW) by the formation of sea ice arches in the Nares Strait. However, continued decline of the Arctic sea ice extent and thickness suggests that arch formation is likely to become anomalous, necessitating an investigation into ...
    • Discriminative multimodal learning via conditional priors in generative models 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael Christian; Aas, Kjersti; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-02)
      Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all ...
    • Sub-ppm Methane Detection with Mid-Infrared Slot Waveguides 

      Yallew, Henock Demessie; Vlk, Marek; Datta, Anurup; Alberti, Sebastian; Zakoldaev, Roman A.; Høvik, Jens; Aksnes, Astrid; Jagerska, Jana (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-21)
      Hybrid integration of photonic chips with electronic and micromechanical circuits is projected to bring about miniature, but still highly accurate and reliable, laser spectroscopic sensors for both climate research and industrial applications. However, the sensitivity of chip-scale devices has been limited by immature and lossy photonic waveguides, weak light–analyte interaction, and etalon effects ...
    • Machine learning assisted multifrequency AFM: Force model prediction 

      Elsherbiny, Lamiaa; Santos Hernandez, Sergio; Gadelrab, Karim; Olukan, Tuza; Font, Josep; Barcons, Victor; Chiesa, Matteo (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-05)
      Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, ...
    • A comparison of constant false alarm rate object detection algorithms for iceberg identification in L- and C-band SAR imagery of the Labrador Sea 

      Færch, Laust; Dierking, Wolfgang Fritz Otto; Hughes, Nick; Doulgeris, Anthony Paul (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-15)
      In this study, we pursue two objectives: first, we compare six different “constant false alarm rate” (CFAR) algorithms for iceberg detection in SAR images, and second, we investigate the effect of radar frequency by comparing the detection performance at C- and L-band. The SAR images were acquired over the Labrador Sea under melting conditions. In an overlapping optical Sentinel-2 image, 492 icebergs ...
    • Data science in wind energy: a case study for Norwegian offshore wind 

      Chen, Hao; Birkelund, Yngve; Zhang, Qixia (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-17)
      In the digital and green transitions, rapidly growing renewable energies are accumulating more and more data. Big data gives room to apply emerging data science to solve challenges in the energy sector. Offshore wind power receives accelerating attention due to its sufficient resources and cleanness. This paper uses data science, including statistical analysis and machine learning, to systematically ...
    • Inferring neutral winds in the ionospheric transition region from atmospheric-gravity-wave traveling-ionospheric-disturbance (AGW-TID) observations with the EISCAT VHF radar and the Nordic Meteor Radar Cluster 

      Günzkofer, Florian; Pokhotelov, Dimitry; Stober, Gunter; Mann, Ingrid Brigitte; Vadas, Sharon L.; Becker, Erich; Tjulin, Anders; Kozlovsky, Alexander; Tsutsumi, Masaki; Gulbrandsen, Njål; Nozawa, Satonori; Lester, Mark; Belova, Evgenia; Kero, Johan; Mitchell, Nicholas J.; Borries, Claudia (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-18)
      Atmospheric gravity waves and traveling ionospheric disturbances can be observed in the neutral atmosphere and the ionosphere at a wide range of spatial and temporal scales. Especially at medium scales, these oscillations are often not resolved in general circulation models and are parameterized. We show that ionospheric disturbances forced by upward-propagating atmospheric gravity waves can be ...
    • Ionospheric Flow Vortex Induced by the Sudden Decrease in the Solar Wind Dynamic Pressure 

      Jin, Yaqi; Moen, Jøran Idar; Spicher, Andres; Liu, Jianjun; Clausen, Lasse; Miloch, Wojciech Jacek (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-30)
      Abrupt changes in the solar wind dynamic pressure can greatly affect the Earth's magnetosphere-ionosphere system. We present an ionospheric flow vortex in the morning sector during the sudden decrease in the solar wind dynamic pressure. The flow vortex was clearly observed by both the Hankasalmi radar and the azimuthal scan mode of the European Incoherent Scatter (EISCAT) Svalbard Radar (ESR). The ...
    • 3D refractive index reconstruction from phaseless coherent optical microscopy data using multiple scattering-based inverse solvers - a study 

      Qin, Yingying; Butola, Ankit; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-27)
      Reconstructing 3D refractive index profile of scatterers using optical microscopy measurements presents several challenges over the conventional microwave and RF domain measurement scenario. These include phaseless and polarization-insensitive measurements, small numerical aperture, as well as a Green's function where spatial frequencies are integrated in a weighted manner such that far-field angular ...
    • Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy 

      Wickstrøm, Kristoffer Knutsen; Løkse, Sigurd Eivindson; Kampffmeyer, Michael Christian; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-06-03)
      Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability. However, it is by no means obvious how to estimate the mutual information (MI) between each hidden layer and the input/desired output to construct the IP. For instance, hidden layers with many neurons require MI estimators ...