Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Sari, Lasanen"'
Publikováno v:
Data-Centric Engineering, Vol 1 (2020)
X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, as well as chemical, biomedical, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstr
Externí odkaz:
https://doaj.org/article/52fee3226ad6487bafa0ed87de6c850c
Publikováno v:
Journal of Inverse and Ill-posed Problems. 27:225-240
We consider inverse problems in which the unknown target includes sharp edges, for example interfaces between different materials. Such problems are typical in image reconstruction, tomography, and other inverse problems algorithms. A common solution
Publikováno v:
Journal of Geophysical Research: Space Physics. 126
Incoherent scatter (IS) radars are invaluable instruments for ionospheric physics, since they observe altitude profiles of electron density (Ne), electron temperature (Te), ion temperature (Ti) and line‐of‐sight plasma velocity (Vi) from ground.
Electron precipitation and ion frictional heating events cause rapid variations in electron temperature, ion temperature and F1 region ion composition of the high-latitude ionosphere. Four plasma parameters: electron density, electron temperature, io
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3eefaebdf89bcfbc14cbaf0db4719f08
https://doi.org/10.5194/egusphere-egu2020-13374
https://doi.org/10.5194/egusphere-egu2020-13374
Funding Information: This work has been funded by Academy of Finland (project numbers 326240, 326341, 314474, 321900, 313708) and by European Regional Development Fund (ARKS project A74305). Publisher Copyright: © The Author(s), 2020. X-ray tomograp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38ef8173decad93c9f15a363a20c9858
In this article, we study Bayesian inverse problems with multi-layered Gaussian priors. The aim of the multi-layered hierarchical prior is to provide enough complexity structure to allow for both smoothing and edge-preserving properties at the same t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b5bd52f7451371744738c1717806954
Autor:
Shin-ichiro, Oyama, Anita, Aikio, Mark, Conde, Heikki, Vanhamaki, Ilkka, Virtanen, Thomas, Ulich, Urban, Brondstrom, Pekka, Verronen, Monika, Szelag, Niilo, Kalakoski, Lassi, Roininen, Sari, Lasanen, Abiyot, Workayehu, Kazuo, Shiokawa, Xu, Heqiucen, Mamoru, Ishii, Masafumi, Hirahara, Takeshi, Sakanoi, Masato, Kagitani, Juha, Sorri, Tomi, Teppo
The Tenth Symposium on Polar Science/Ordinary sessions: [OS] Space and upper atmospheric sciences, Wed. 4 Dec. / Institute of Statistics and Mathematics (ISM) Seminar room 2 (D304) (3rd floor)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=jairo_______::320d6994d4c71aadcfed0c216a8dc5f9
http://id.nii.ac.jp/1291/00015752/
http://id.nii.ac.jp/1291/00015752/
We introduce non-stationary Matern field priors with stochastic partial differential equations, and construct correlation length-scaling with hyperpriors. We model both the hyperprior and the Matern prior as continuous-parameter random fields. As hyp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df8e287aa9d0a25133d8df59407e2f3b
http://hdl.handle.net/10044/1/66155
http://hdl.handle.net/10044/1/66155
Publikováno v:
Inverse Problems & Imaging. 8:561-586
We study flexible and proper smoothness priors for Bayesian statistical inverse problems by using Whittle-Matern Gaussian random fields. We review earlier results on finite-difference approximations of certain Whittle-Matern random field in $\mathbb{
Linear second order elliptic boundary value problems (BVP) on bounded Lipschitz domains are studied in the case of Gaussian white noise loads. Especially, Neumann and Robin BVPs are considered. The main obstacle for applying the usual variational app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10ad736c93c471a926a15fc929c2778a