Zobrazeno 1 - 10
of 440
pro vyhledávání: '"Fukui, Ken-ichi"'
The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this paper, we show that using the UBIC is equivalent to employing the conve
Externí odkaz:
http://arxiv.org/abs/2404.16881
Recently, growing health awareness, novel methods allow individuals to monitor sleep at home. Utilizing sleep sounds offers advantages over conventional methods like smartwatches, being non-intrusive, and capable of detecting various physiological ac
Externí odkaz:
http://arxiv.org/abs/2404.10299
Publikováno v:
IEEE Access 12 (2024) 13165-13182
We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms.
Externí odkaz:
http://arxiv.org/abs/2308.10283
Publikováno v:
Mach. Learn.: Sci. Technol. 4 015009 (2023)
This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy
Externí odkaz:
http://arxiv.org/abs/2206.12901
Publikováno v:
JMIR Medical Informatics, Vol 3, Iss 1, p e16 (2015)
BackgroundNon-medical professionals (consumers) are increasingly using the Internet to support their health information needs. However, the cognitive effort required to perform health information searches is affected by the consumer’s familiarity w
Externí odkaz:
https://doaj.org/article/1e95aa38ba074e35a92f93a5820b8e8c
Autor:
Suzuki, Anna, Shi, Shuokun, Sakai, Taro, Fukui, Ken-ichi, Onodera, Shinya, Ishizaki, Junichi, Hashida, Toshiyuki
Publikováno v:
In Renewable Energy April 2024 224
Recently, researchers have utilized neural networks to accurately solve partial differential equations (PDEs), enabling the mesh-free method for scientific computation. Unfortunately, the network performance drops when encountering a high nonlinearit
Externí odkaz:
http://arxiv.org/abs/2104.14320
In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous
Externí odkaz:
http://arxiv.org/abs/2003.05586
Autor:
Morino, Yusuke, Sano, Hikaru, Kawamoto, Koji, Fukui, Ken-ichi, Takeuchi, Masato, Sakuda, Atsushi, Hayashi, Akitoshi
Publikováno v:
In Solid State Ionics April 2023 392
Autor:
Kantavat, Pittipol, Kijsirikul, Boonserm, Songsiri, Patoomsiri, Fukui, Ken-ichi, Numao, Masayuki
We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classi- fication. In each node of the tree, we select an appropriate binary classifier using entropy and generalization error estimation, then group the
Externí odkaz:
http://arxiv.org/abs/1708.08231