Now showing items 261-280 of 483

    • Up-to-the-minute Data Policy Updates for Participatory Studies 

      Sharma, Aakash (Conference object; Konferansebidrag, 2021-06-14)
    • Privacy Perceptions and Concerns in Image-Based Dietary Assessment Systems: Questionnaire-Based Study 

      Sharma, Aakash; Czerwinska, Katja P; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-10-15)
      Background: Complying with individual privacy perceptions is essential when processing personal information for research. Our specific research area is performance development of elite athletes, wherein nutritional aspects are important. Before adopting new automated tools that capture such data, it is crucial to understand and address the privacy concerns of the research subjects that are to be ...
    • Criteria for Assessing and Recommending Digital Diabetes Tools: A Delphi Study 

      Larbi, Dillys; Randine, Pietro; Årsand, Eirik; Bradway, Meghan; Antypas, Konstantinos; Gabarron, Elia (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Diabetes self-management, an integral part of diabetes care, can be improved with the help of digital self-management tools such as apps, sensors, websites, and social media. The study objective was to reach a consensus on the criteria required to assess and recommend digital diabetes self-management tools targeting those with diabetes in Norway. Healthcare professionals working with diabetes care ...
    • Flexible time aggregation for energy systems modelling 

      van Greevenbroek, Koen; Bordin, Chiara; Mishra, Sambeet (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-09-24)
      With high shares of renewable generation and a reliance on storage, modelling large scale energy systems is computationally challenging. One factor driving the complexity of these models is the need for a high temporal resolution over a long period; a typical baseline is modelling all 8760 hours in a year. While simple methods such as down-sampling and segmentation are effective at reducing the ...
    • Dynamic path finding method and obstacle avoidance for automated guided vehicle navigation in Industry 4.0 

      Dündar, Yigit Can (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-01)
      Within the scope of Industry 4.0, Automated Guided Vehicles (AGVs) are used to streamline logistics through the usage of efficient path finding methods. The current path finding methods in the industry rely on excessive usage of guidance in the shape of magnets, tapes or QR codes on the floor that the AGVs follow to reach their destinations. However, the current methods lack operational flexibility ...
    • Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review 

      Sadanandan Anand, Akhil; Seel, Katrine; Gjærum, Vilde Benoni; Håkansson, Anne; Robinson, Haakon; Saad, Aya (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-01)
      Real-world autonomous systems are often controlled using conventional model-based control methods. But if accurate models of a system are not available, these methods may be unsuitable. For many safety-critical systems, such as robotic systems, a model of the system and a control strategy may be learned using data. When applying learning to safety-critical systems, guaranteeing safety during learning ...
    • Emotionally charged text classification with deep learning and sentiment semantic 

      Huan, Jeow Li; Sekh, Arif Ahmed; Quek, Chai; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-09-28)
      Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional ...
    • Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments 

      Håkansson, Anne; Saad, Aya; Sadanandan Anand, Akhil; Gjærum, Vilde Benoni; Robinson, Haakon; Seel, Katrine (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can ...
    • Prediction of cloud fractional cover using machine learning 

      Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-03)
      Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually ...
    • Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (Journal article; Tidsskriftartikkel, 2021-11-27)
      Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. ...
    • A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging 

      Jha, Debesh; Ali, Sharib; Hicks, Steven; Thambawita, Vajira L B; Borgli, Hanna; Smedsrud, Pia H.; de Lange, Thomas; Pogorelov, Konstantin; Wang, Xiaowei; Harzig, Philipp; Tran, Minh-Triet; Meng, Wenhua; Hoang, Trung-Hieu; Dias, Danielle; Ko, Tobey H.; Agrawal, Taruna; Ostroukhova, Olga; Khan, Zeshan; Tahir, Muhammed Atif; Liu, Yang; Chang, Yuan; Kirkerød, Mathias; Johansen, Dag; Lux, Mathias; Johansen, Håvard D.; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-19)
      Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. ...
    • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning 

      Jha, Debesh; Ali, Sharib; Tomar, Nikhil Kumar; Johansen, Håvard D.; Johansen, Dag; Rittscher, Jens; Riegler, Michael A.; Halvorsen, Pal (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-04)
      Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking ...
    • PyPSA meets Africa: Developing an open source electricity network model of the African continent 

      Kirli, Desen; Hampp, Johannes; van Greevenbroek, Koen; Grant, Rebecca; Mahmood, Matin; Parzen, Maximilian; Kiprakis, Aristides (Chapter; Bokkapittel, 2021-10-25)
      Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System ...
    • File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments 

      Ovesen, Aril Bernhard; Nordmo, Tor-Arne Schmidt; Johansen, Håvard D.; Riegler, Michael Alexander; Halvorsen, Pål; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-18)
      In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates ...
    • Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence 

      Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-15)
      A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, ...
    • Smart contract formation enabling energy-as-a-service in a virtual power plant 

      Mishra, Sambeet; Crasta, Cletus John; Bordin, Chiara; Mateo-Fornés, Jordi (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-22)
      Energy as a service (EaaS) is an emerging business model that enables the otherwise passive energy consumers to play an active role and participate in the energy utility services. This platform is formed through smart contracts registering peer-to-peer (P2P) transactions of energy through price and quantity. Many industries, including finance, have already leveraged smart contracts to introduce ...
    • IRON-MAN: An Approach to Perform Temporal Motionless Analysis of Video Using CNN in MPSoC 

      Dey, Somdip; Singh, Amit Kumar; Prasad, Dilip K.; McDonald-Maier, Klaus Dieter (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-07-20)
      This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work ...
    • Experiences Building and Deploying Wireless Sensor Nodes for the Arctic Tundra 

      Murphy, Michael J.; Tveito, Øystein; Kleiven, Eivind Flittie; Rais, Issam; Soininen, Eeva M; Bjørndalen, John Markus; Anshus, Otto (Journal article; Tidsskriftartikkel, 2021-08-02)
      The arctic tundra is most sensitive to climate change. The change can be quantified from observations of the fauna, flora and weather conditions. To do observations at sufficient spatial and temporal resolution, ground-based observation nodes with sensors are needed. However, the arctic tundra is resource-limited with regards to energy, data networks, and humans. There are also regulatory and practical ...
    • Opportunities for thermal energy storage in Longyearbyen 

      van Greevenbroek, Koen; Klein, Lars-Stephan (Research report; Forskningsrapport, 2021-07-02)
      Energy storage is needed in Longyearbyen to enable a transition to local renewable energy sources. As heating accounts for more than half the energy use in Longyearbyen, affordable large-scale thermal storage is a good option. We investigate the opportunities for hot water, molten salt and hot rocks storage systems using a techno-economic optimisation model for the Longyearbyen energy system.
    • Accountable Human Subject Research Data Processing using Lohpi 

      Sharma, Aakash; Bye Nilsen, Thomas; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Conference object; Konferansebidrag, 2021-06)
      In human subject research, various data about the studied individuals are collected. Through re-identification and statistical inferences, this data can be exploited for interests other than the ones the subjects initially consented to. Such exploitation must be avoided to maintain trust with the researched population. We argue that keeping data-access policies up-to-date and building accountability ...