705 research outputs found
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with
biosignal generative models based on generative adversarial networks (GANs),
which are a type of deep learning technique, was demonstrated in our previous
paper. GAN-based generative models only learn the projection between a random
distribution as input data and the distribution of training data.Therefore, the
relationship between input and generated data is unclear, and the
characteristics of the data generated from this model cannot be controlled.
This study proposes a method for generating time-series data based on GANs and
explores their ability to generate biosignals with certain classes and
characteristics. Moreover, in the proposed method, latent variables are
analyzed using canonical correlation analysis (CCA) to represent the
relationship between input and generated data as canonical loadings. Using
these loadings, we can control the characteristics of the data generated by the
proposed method. The influence of class labels on generated data is analyzed by
feeding the data interpolated between two class labels into the generator of
the proposed GANs. The CCA of the latent variables is shown to be an effective
method of controlling the generated data characteristics. We are able to model
the distribution of the time-series data without requiring domain-dependent
knowledge using the proposed method. Furthermore, it is possible to control the
characteristics of these data by analyzing the model trained using the proposed
method. To the best of our knowledge, this work is the first to generate
biosignals using GANs while controlling the characteristics of the generated
data
Low m/n Mode Behavior of MHD Plasma in LHD
Behaviors of low poloidal (m) and toroidal (n) Fourier modes in the Large Helical Device (LHD) are investigated by means of direct numerical simulations (DNS) of fully three-dimensional, nonlinear and compressiblemagnetohydrodynamics (MHD) equations. Starting from an ideal equilibrium with the position of vacuum magneticaxis Rax = 3.6 m and β0 = 4% finite pressure, a m/n = 2/1 mode grows in the DNS. Fluid motions on poloidal sectionsare governed by the two pairs of anti-parallel vortex pairs associated with the m/n = 2/1 modes. The vortex pairstransport plasma pressure from the core to edge region and bring about large pressure deformations. It is also shown that the toroidal part in the kinetic energy and the enstrophy are comparable to the poloidal parts of them. The numerical results demonstrate importance of investigating three-dimensional behaviors of MHD plasmas in LHD
ベトナム中部の伝統的木造建築の設計方法の特質
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Nonlinear simulation of resistive ballooning modes in the Large Helical Device
Nonlinear simulations of a magnetohydrodynamic (MHD) plasma in full three-dimensional geometry of the Large Helical Device (LHD) [O. Motojima et al., Phys. Plasmas 6, 1843 (1999)] are conducted. A series of simulations shows growth of resistive ballooning instability, for which the growth rate is seen to be proportional to the one-third power of the resistivity. Nonlinear saturation of the excited mode and its slow decay are observed. Distinct ridge/valley structures in the pressure are formed in the course of the nonlinear evolution. The compressibility and the viscous heating, as well as the thermal conduction, are shown to be crucial to suppress the pressure deformations. Indication of a pressure-driven relaxation phenomenon that leads to an equilibrium with broader pressure profile is observed
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