Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Paal Engelstad"'
Publikováno v:
Energies, Vol 16, Iss 20, p 7179 (2023)
With the rapid adoption of wind energy globally, there is a need for accurate short-term forecasting systems to improve the reliability and integration of such energy resources on a large scale. While most spatio-temporal forecasting systems comprise
Externí odkaz:
https://doaj.org/article/6e0df39ea7cd44999e26a5cba18c1322
Publikováno v:
Sensors, Vol 23, Iss 8, p 3958 (2023)
We propose and investigate a bidirectional device-to-device (D2D) transmission scheme that exploits cooperative downlink non-orthogonal multiple access (NOMA) (termed as BCD-NOMA). In BCD-NOMA, two source nodes communicate with their corresponding de
Externí odkaz:
https://doaj.org/article/52b91e928a4a4869897d0e6e8829bb37
Publikováno v:
Sensors, Vol 18, Iss 10, p 3254 (2018)
Amidst the rapid development of the fifth generation (5G) networks, Internet of Things (IoT) is considered as one of the most important part of 5G next generation networks as it can support massive object communications. These massive object communic
Externí odkaz:
https://doaj.org/article/79127b2c0357481bb451e12f180d7531
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b57bcbdf5ff4a1dfed060e21b17f5652
https://doi.org/10.1109/bigdata55660.2022.10020815
https://doi.org/10.1109/bigdata55660.2022.10020815
Autor:
Roza Abolghasemi, Rabindra Khadka, Pedro G. Lind, Paal Engelstad, Enrique Herrera Viedma, Anis Yazidi
Publikováno v:
Knowledge-Based Systems
In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost importance, since many experts provide incomplete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3c3d7304e2e295e756ec71a1bcce6db
https://hdl.handle.net/10481/77378
https://hdl.handle.net/10481/77378
Publikováno v:
IEEE Transactions on Games (TG)
Authors accepted manuscript Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55ac2efe0fabcfeb742286ef5b78d373
https://hdl.handle.net/11250/3047297
https://hdl.handle.net/11250/3047297
Publikováno v:
Applied Energy
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local win
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a621a8832e83b0983056724cc11aa4c
https://hdl.handle.net/11250/3042606
https://hdl.handle.net/11250/3042606
Autor:
Martin Holen, Else-Line Malene Ruud, Narada Dilp Warakagoda, Morten Goodwin, Paal Engelstad, Kristian Muri Knausgård
Publikováno v:
Engineering Applications of Neural Networks ISBN: 9783031082221
Providing full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38d50dee031ae2abba102c75cd44edea
http://hdl.handle.net/10852/100544
http://hdl.handle.net/10852/100544
Publikováno v:
Journal of Physics: Conference Series (JPCS)
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51cada1b8c28d8c10037a296f02d17cf
http://hdl.handle.net/10852/98426
http://hdl.handle.net/10852/98426