Artikler, rapporter og annet (informatikk): Nye registreringer
Viser treff 221-240 av 453
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Next frontiers in energy system modelling: A review on challenges and the state of the art
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-02-26)Energy Systems Modelling is growing in relevance on providing insights and strategies to plan a carbon-neutral future. The implementation of an effective energy transition plan faces multiple challenges, spanning from the integration of the operations of different energy carriers and sectors to the consideration of multiple spatial and temporal resolutions. In this review, we outline these challenges ... -
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events
(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 ... -
Detection of ground contact times with inertial sensors in elite 100-m sprints under competitive field conditions
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-04)This study describes a method for extracting the stride parameter ground contact time (GCT) from inertial sensor signals in sprinting. Five elite athletes were equipped with inertial measurement units (IMU) on their ankles and performed 34 maximum 50 and 100-m sprints. The GCT of each step was estimated based on features of the recorded IMU signals. Additionally, a photo-electric measurement ... -
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-23)Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable ... -
Northeast Arctic Cod and Prey Match-Mismatch in a High-Latitude Spring-Bloom System
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-20)By combining an ocean model, a nutrient-phytoplankton-zooplankton-detritus-model and an individual-based model for early life stages of Northeast Arctic cod we systematically investigate food limitations and growth performance for individual cod larvae drifting along the Norwegian coast from spawning grounds toward nursery areas in the Barents Sea. We hypothesize that there is food shortage for ... -
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-29)In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures ... -
Exploring Real-World mHealth Use for Diabetes Consultations: Pros and Pitfalls of a Pragmatic Mixed-Methods Approach
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021)Intervention research is often highly controlled and does not reflect real-world situations. More pragmatic approaches, albeit less controllable and more challenging, offer the opportunity of identifying unexpected factors and connections. As the introduction of mHealth into formal diabetes care settings is relatively new and less often explored from the perspectives of patients and providers together, ... -
Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-07-05)While we know that motivated students learn better than non-motivated students but detecting motivation is challenging. Here we present a game-based motivation detection approach from the EEG signals. We take an original approach of using EEG-based brain computer interface to assess if motivation state is manifest in physiological EEG signals as well, and what are suitable conditions in order to ... -
What motivates patients with NCDs to follow up their treatment?
(Conference object; Konferansebidrag, 2021-05)The increasing use of mobile health (mHealth) tools for self-management is considered to be important to improve health effects for patients with chronic NCDs (noncommunicable diseases). This development is supported by an increasing number of available mHealth apps. The apps range from disease management apps (e.g., diabetes diary) to health and fitness apps (e.g., dietary apps and workout ... -
Compliant Sharing of Sensitive Data with Dataverse and Lohpi
(Conference object; Konferansebidrag, 2021-06) -
Up-to-the-minute Data Policy Updates for Participatory Studies
(Conference object; Konferansebidrag, 2021-06-14) -
Privacy Perceptions and Concerns in Image-Based Dietary Assessment Systems: Questionnaire-Based Study
(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
(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
(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
(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
(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
(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
(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
(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
(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. ...