Zobrazeno 1 - 10 of 45 pro vyhledávání: '"Genta Ueno"'
1
Akademický článek
First application of data assimilation-based control to fusion plasma
Autor: Yuya Morishita, Sadayoshi Murakami, Naoki Kenmochi, Hisamichi Funaba, Ichihiro Yamada, Yoshinori Mizuno, Kazuki Nagahara, Hideo Nuga, Ryosuke Seki, Masayuki Yokoyama, Genta Ueno, Masaki Osakabe
Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Magnetic fusion plasmas, which are complex systems comprising numerous interacting elements, have large uncertainties. Therefore, future fusion reactors require prediction-based advanced control systems with an adaptive system model and cont
Externí odkaz: https://doaj.org/article/63e455de52fe42378329afec9f5375d4
Zobrazit plný text záznamu
2
Nonlinear Filtering Method Using a Switching Error Model for Outlier-Contaminated Observations
Autor: Akio Nakabayashi, Genta Ueno
Publikováno v: IEEE Transactions on Automatic Control. 65:3150-3156
This article considers a nonlinear filtering method for handling outliers. The presence of outliers that are gross observation errors can greatly reduce the accuracy of filtering methods that assume Gaussian distributed errors. There are some existin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::13dab63444e5b4dfba93fd84aa982509
https://doi.org/10.1109/tac.2019.2947649
Zobrazit plný text záznamu
3
Non-Gaussian Probability Densities of Convection Initiation and Development Investigated Using a Particle Filter with a Storm-Scale Numerical Weather Prediction Model
Autor: Takuya Kawabata, Genta Ueno
Publikováno v: Monthly Weather Review. 148:3-20
Non-Gaussian probability density functions (PDFs) in convection initiation (CI) and development were investigated using a particle filter with a storm-scale numerical prediction model and an adaptive observation error estimator (NHM-RPF). An observin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3ddd892a814c22d825fc8a8cceba7ebd
https://doi.org/10.1175/mwr-d-18-0367.1
Zobrazit plný text záznamu
4
Development of Rapid Simulation Code for NBI Heating Analysis in LHD
Autor: Yuya Morishita, Ryosuke Seki, Sadayoshi Murakami, Masayuki Yokoyama, H. Nuga, Genta Ueno, Masaki Osakabe
Publikováno v: Journal of Fusion Energy. 41
We develop a rapid simulation code for neutral beam injection (NBI) heating analysis, FIT3D-RC, to evaluate the power deposition in NBI-heated plasmas of the Large Helical Device (LHD). This code evaluates the beam ion birth profile using the Gaussia
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::38fbdf6cd3d24317c0ad21471806f68a
https://doi.org/10.1007/s10894-021-00313-5
Zobrazit plný text záznamu
5
Introducing Quantile Mapping to a Regression Model Using a Multi-Model Ensemble to Improve Probabilistic Projections of Monthly Precipitation
Autor: Koji Dairaku, Genta Ueno, Noriko N. Ishizaki
Publikováno v: Journal of Disaster Research. 13:873-878
A new method was proposed for the probabilistic projection of future climate that introduced quantile mapping to a regression method using a multi-model ensemble (QM_RMME). Results of this method were then compared with those of the traditional regre
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::677400ebf1a5e434ecb412372de55ad2
https://doi.org/10.20965/jdr.2018.p0873
Zobrazit plný text záznamu
7
Akademický článek
An Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes's Method.
Autor: AKIO NAKABAYASHI1 nakio@ism.ac.jp, GENTA UENO1
Publikováno v: Monthly Weather Review. Jan2017, Vol. 145 Issue 1, p199-213. 15p. 5 Charts, 7 Graphs.
Zobrazit plný text záznamu
9
An Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes’s Method
Autor: Akio Nakabayashi, Genta Ueno
Publikováno v: Monthly Weather Review. 145:199-213
This paper presents an extension of the ensemble Kalman filter (EnKF) that can simultaneously estimate the state vector and the observation error covariance matrix by using the variational Bayes’s (VB) method. In numerical experiments, this capabil
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::255b070eba5484e49ceb3fbcd2a2991c
https://doi.org/10.1175/mwr-d-16-0139.1
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje