Zobrazeno 1 - 10
of 300
pro vyhledávání: '"Numao, Masayuki"'
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:
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
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
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
Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain. In this work, a study of CNN and its spatiotemporal feature e
Externí odkaz:
http://arxiv.org/abs/1910.09719
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
Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals captured f
Externí odkaz:
http://arxiv.org/abs/1611.10120