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pro vyhledávání: '"Eidsvik A"'
Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tra
Externí odkaz:
http://arxiv.org/abs/2410.16278
Autor:
Christensen, Michael F., Eidsvik, Jo
Models for areal data are traditionally defined using the neighborhood structure of the regions on which data are observed. The unweighted adjacency matrix of a graph is commonly used to characterize the relationships between locations, resulting in
Externí odkaz:
http://arxiv.org/abs/2407.02684
Distributed acoustic sensing through fiber-optical cables can contribute to traffic monitoring systems. Using data from a day of field testing on a 50 km long fiber-optic cable along a railroad track in Norway, we detect and track cars and trains alo
Externí odkaz:
http://arxiv.org/abs/2405.01140
Stochastic reservoir characterization, a critical aspect of subsurface exploration for oil and gas reservoirs, relies on stochastic methods to model and understand subsurface properties using seismic data. This paper addresses the computational chall
Externí odkaz:
http://arxiv.org/abs/2403.03656
Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas sector. Traditional two-point geostatistics, while foundational, ofte
Externí odkaz:
http://arxiv.org/abs/2311.01968
For oceanographic applications, probabilistic forecasts typically have to deal with i) high-dimensional complex models, and ii) very sparse spatial observations. In search-and-rescue operations at sea, for instance, the short-term predictions of drif
Externí odkaz:
http://arxiv.org/abs/2302.07197
One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is proposed in computer vision and it learns by pulling representations of rand
Externí odkaz:
http://arxiv.org/abs/2204.02697
Autor:
Folkvord, Synnøve Mari Eidsvik, Mykkeltveit, Ida Helene, Risa, Eva Christina Furskog, Dyrstad, Dagrunn Nåden
Publikováno v:
In Nurse Education in Practice October 2024 80
Publikováno v:
In Computers and Geosciences January 2025 194
Autor:
Gebrihet, Hafte Gebreselassie1,2 (AUTHOR) erlend.eidsvik@hvl.no, Eidsvik, Erlend1 (AUTHOR)
Publikováno v:
Social Sciences (2076-0760). Aug2024, Vol. 13 Issue 8, p429. 24p.