Zobrazeno 1 - 10
of 368
pro vyhledávání: '"Nakamura, Taichi"'
In this study, we develop a dialogue system for a dialogue robot competition. In the system, the characteristics of sightseeing spots are expressed as "attribute vectors" in advance, and the user is questioned on the different attributes of the two c
Externí odkaz:
http://arxiv.org/abs/2210.08703
Autor:
Oka, Tomoharu, Uruno, Asaka, Enokiya, Rei, Nakamura, Taichi, Yamasaki, Yuto, Watanabe, Yuto, Tokuyama, Sekito, Iwata, Yuhei
Publikováno v:
Astrophysical Journal Supplement Series, 261:13 (2022 August)
This study developed an automated identification procedure for compact clouds with broad velocity widths in the spectral line data cubes of highly crowded regions. The procedure was applied to the CO J=3-2 line data, obtained using the James Clerk Ma
Externí odkaz:
http://arxiv.org/abs/2209.12395
Publikováno v:
In Microporous and Mesoporous Materials October 2024 378
Autor:
Nakamura, Taichi, Fukagata, Koji
Publikováno v:
Int. J. Heat Fluid Flow 96 (2022) 108997
State estimation from limited sensor measurements is ubiquitously found as a common challenge in a broad range of fields including mechanics, astronomy, and geophysics. Fluid mechanics is no exception -- state estimation of fluid flows is particularl
Externí odkaz:
http://arxiv.org/abs/2112.02751
Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows
Autor:
Moriya, Naoki, Fukami, Kai, Nabae, Yusuke, Morimoto, Masaki, Nakamura, Taichi, Fukagata, Koji
We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved large-eddy simulation (LES). Our consideration is made on LES of turbulent channel flows with a first grid point set relatively far from the wall ($\sim$ 10 wall
Externí odkaz:
http://arxiv.org/abs/2106.09271
Publikováno v:
Scientific Reports 12, 3726 (2022)
Neural networks (NNs) and linear stochastic estimation (LSE) have widely been utilized as powerful tools for fluid-flow regressions. We investigate fundamental differences between them considering two canonical fluid-flow problems: 1. the estimation
Externí odkaz:
http://arxiv.org/abs/2105.00913
Publikováno v:
SN Comput. Sci. 5, 306 (2024)
The recent development of high-performance computing enables us to generate spatio-temporal high-resolution data of nonlinear dynamical systems and to analyze them for a deeper understanding of their complex nature. This trend can be found in a wide
Externí odkaz:
http://arxiv.org/abs/2103.09020
Publikováno v:
SN Comput. Sci. 2, 467 (2021)
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of a convolutional neural network and multi-layer perceptrons is considered in this study
Externí odkaz:
http://arxiv.org/abs/2011.10277
Autor:
Ikawa, Yasuhiro1 (AUTHOR) yasuhiro.ikawa@staff.kanazawa‐u.ac.jp, Nakamura, Taichi1 (AUTHOR), Fujino, Noboru2 (AUTHOR), Uchiyama, Toru3 (AUTHOR), Ishiguro, Akira4 (AUTHOR), Takenaka, Mika1 (AUTHOR), Sakai, Yuta1 (AUTHOR), Noguchi, Kazuhiro1 (AUTHOR), Fujiki, Toshihiro1 (AUTHOR), Wada, Taizo1 (AUTHOR)
Publikováno v:
Clinical Case Reports. Feb2024, Vol. 12 Issue 2, p1-4. 4p.
Publikováno v:
Phys. Fluids 33, 025116 (2021)
We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of $Re_\tau=110$ in a minimum domain which
Externí odkaz:
http://arxiv.org/abs/2010.13351