5 papers
Learning a quantitative criterion for distinguishing chaos from noise
Jaesung Choi, Athokpam Langlen Chanu, Jong-Min Park
Distinguishing chaos from noise using time-series data is fundamentally challenging because both exhibit irregular fluctuations and share many statistical and dynamical characteris…
Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks
Jaesung Choi
Machine learning of Hamiltonian dynamics has driven growing interest in Hamiltonian neural networks (HNNs), which encode Hamilton's equations of motion into the learning architectu…
Human brain state classification via permutation entropy of EEG phase dynamics across consciousness levels and inattentive-type ADHD
Athokpam Langlen Chanu, Youngjai Park, Jaesung Choi +4
We analyze electroencephalography (EEG) signals using the ordinal pattern framework to investigate whether different human brain states can be distinguished based on the disorder o…
Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals
Jaesung Choi, Pilwon Kim
The interdependence and high dimensionality of multivariate signals present significant challenges for denoising, as conventional univariate methods often struggle to capture the c…
Signal-noise separation using unsupervised reservoir computing
Jaesung Choi, Pilwon Kim
Removing noise from a signal without knowing the characteristics of the noise is a challenging task. This paper introduces a signal-noise separation method based on time series pre…