Viser treff 351-370 av 389

    • Ubiquitous digital health-related data: clarification of concepts 

      Johannessen, Erlend; Henriksen, André; Hartvigsen, Gunnar; Horsch, Alexander; Årsand, Eirik; Johansson, Jonas (Chapter; Bokkapittel, 2022-08-22)
      The increased development and use of ubiquitous digital services reinforce the trend where health-related data is generated everywhere. Data usage in different areas introduces different terms for the same or similar concepts. This adds to the confusion of what these terms represent. We aim to provide an overview of concepts and terms used in connection with digital twins and in a healthcare context.
    • Uncertainty Estimation and Visualization of Wind in Weather Forecasts 

      Fjukstad, Bård; Bjørndalen, John Markus; Anshus, Otto (Chapter; Bokkapittel, 2014-01)
      The Collaborative Symbiotic Weather Forecasting system, CSWF, let individual users do on-demand small region, short-term, and very high-resolution forecasts. When the regions have some overlap, a symbiotic forecast can be produced based on the individual forecasts from each region. Small differences in where the center of the region is located when there is complex terrain in the region, leads to ...
    • Uni- and triaxial accelerometric signals agree during daily routine, but show differences between sports 

      Smith, Maia P; Horsch, Alexander; Standl, Marie; Heinrich, Joachim; Schulz, Holger (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-10-10)
      Accelerometers objectively monitor physical activity, and ongoing research suggests they can also detect patterns of body movement. However, different types of signal (uniaxial, captured by older studies, vs. the newer triaxial) and or/device (validated Actigraph used by older studies, vs. others) may lead to incomparability of results from different time periods. Standardization is desirable. We ...
    • Unified Power Control of Permanent Magnet Synchronous Generator Based Wind Power System with Ancillary Support during Grid Faults 

      Ramachandran, Vijayapriya; Sendraya Perumal, Angalaeswari; Lakshmaiya, Natrayan; Paramasivam, Prabhu; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-08)
      A unified active power control scheme is devised for the grid-integrated permanent magnet synchronous generator-based wind power system (WPS) to follow the Indian electricity grid code requirements. The objective of this paper is to propose control schemes to ensure the continuous integration of WPS into the grid even during a higher percentage of voltage dip. In this context, primarily a constructive ...
    • 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, ...
    • Unsupervised and supervised learning for the reliability analysis of complex systems 

      Gámiz, María Luz; Navas-Gómez, Fernando; Nozal Cañadas, Rafael; Raya-Miranda, Rocío (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-18)
      In this paper, a strategy to deal with high-dimensional reliability systems with multiple correlated components is proposed. The goal is to construct a state function that enables the classification of the states of components in one of two categories, that is, failure and operative, in case of dealing with a large number of units in the system. To this end, it is proposed a new algorithm that ...
    • Up-to-the-minute Data Policy Updates for Participatory Studies 

      Sharma, Aakash (Conference object; Konferansebidrag, 2021-06-14)
    • Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies 

      Sharma, Aakash; Nilsen, Thomas Bye; Czerwinska, Katja P; Onitiu, Daria; Brenna, Lars; Johansen, Dag; Johansen, Håvard D. (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-13)
      Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. ...
    • Usage and perceptions of a mobile self-management application for people with type 2 diabetes: qualitative study of a five-month trial 

      Tatara, Naoe; Årsand, Eirik; Bratteteig, Tone; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
    • Use of a Data-Sharing System During Diabetes Consultations 

      Bradway, Meghan; Muzny, Miroslav; Årsand, Eirik (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      Patient-gathered self-management data and shared decision-making are touted as the answer to improving an individual’s health situation as well as collaboration between patients and their providers leading to more effective treatment plans. However, there is a gap between this ideal and reality – a lack of data-sharing technology. Here, we present the impact that the FullFlow System for sharing ...
    • Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis 

      Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-11)
      Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way ...
    • User Expectations and Willingness to Share Self-Collected Health Data 

      Woldaregay, Ashenafi Zebene; Henriksen, André; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Richard, Aude; Lovis, Christian; Årsand, Eirik; Rochat, Jessica; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      The rapid improvement in mobile health technologies revolutionized what and how people can self-record and manage data. This massive amount of information accumulated by these technologies has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate people to share this data for the benefit of all. The purpose of this study is ...
    • User Expectations and Willingness to Share Self-collected Health Data 

      Woldaregay, Ashenafi Zebene; Henriksen, André; Issom, David-Zacharie; Pfuhl, Gerit; Sato, Keiichi; Richard, Aude; Lovis, Christian; Årsand, Eirik; Rochat, Jessica; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      The rapid improvement in mobile health technologies revolutionized what and how people can self-record and manage data. This massive amount of information accumulated by these technologies has potentially many applications beyond personal need, i.e. for public health. A challenge with collecting this data is to motivate people to share this data for the benefit of all. The purpose of this study is ...
    • Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events 

      Rongved, Olav Andre Nergård; Hicks, Steven; Thambawita, Vajira L B; Stensland, Håkon Kvale; Zouganeli, Evi; Johansen, Dag; Riegler, Michael Alexander; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06)
      Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present ...
    • Using a virtual event space to understand parallel application communication behavior 

      Bongo, Lars Ailo; Anshus, Otto J.; Bjørndalen, John Markus (Research report; Forskningsrapport, 2003)
      We have developed EventSpace, a configurable data collecting, management and observation system for monitoring low-level synchronization and communication events with the purpose of understanding the behavior of parallel applications on clusters and multi-clusters. Applications are instrumented by adding data collecting code in the form of event collectors to an applications communication paths. ...
    • Using a waiting protocol to separate concerns in the mutual exclusion problem 

      Fjeld, Frode V. (Research report; Forskningsrapport, 2003-11-21)
      How to implement process synchronization in a general-purpose software library while incurring a minimum of policy decisions on the system as a whole? We propose that in dealing with the problem of mutual exclusion in concurrent systems, a separation of concerns between the mechanism of detecting contention and the policy decision of what to do when such contention is detected is appropriate. We ...
    • Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables 

      Henriksen, André; Mikalsen, Martin Haugen; Woldaregay, Ashenafi Zebene; Muzny, Miroslav; Hartvigsen, Gunnar; Hopstock, Laila Arnesdatter; Grimsgaard, Sameline (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-03-22)
      Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected ...
    • Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic 

      Thom, Håvard; Bjørndalen, John Markus; Kleiven, Eivind Flittie; Soininen, Eeva M; Killengreen, Siw Turid; Ehrich, Dorothee; Ims, Rolf Anker; Anshus, Otto; Horsch, Alexander (Chapter; Bokkapittel, 2017)
      The arctic tundra is considered the terrestrial biome expected to be most impacted by climate change, with temperatures projected to increase as much as 10 °C by the turn of the century. The Climate-ecological Observatory for Arctic Tundra (COAT) project monitors the climate and ecosystems using several sensor types. We report on results from projects that automate image annotations from two of the ...
    • Using satellite execution to reduce latency for mobile/cloud applications 

      Pettersen, Robert; Valvåg, Steffen; Kvalnes, Åge Andre; Johansen, Dag (Peer reviewed; Journal article; Tidsskriftsartikkel, 2016-02-03)
      We demonstrate a practical way to reduce latency for mobile .NET applications that interact with cloud services, without disrupting application architectures. We provide a programming abstraction for location-independent code, which has the potential to execute either locally or at a satellite execution environment in the cloud, where other cloud services can be accessed with low latency. This ...
    • Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System 

      Singh, Raushan Kumar; Singh, Pradeep Kumar; Singh, Juginder Pal; Singh, Akhilesh Kumar; Dhanasekaran, Seshathiri (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-17)
      The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative ...