C
Cheolsoo Park
Researcher at Kwangwoon University
Publications - 117
Citations - 2379
Cheolsoo Park is an academic researcher from Kwangwoon University. The author has contributed to research in topics: Computer science & Noise. The author has an hindex of 22, co-authored 100 publications receiving 1695 citations. Previous affiliations of Cheolsoo Park include Seoul National University & Imperial College London.
Papers
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Journal ArticleDOI
A review of uncertainty analysis in building energy assessment
Wei Tian,Yeonsook Heo,Pieter de Wilde,Zhanyong Li,Da Yan,Cheolsoo Park,Xiaohang Feng,Godfried Augenbroe +7 more
TL;DR: The data sources of uncertainty in building performance analysis are described to provide a firm foundation for specifying variations of uncertainty factors affecting building energy, and several applications of uncertainty analysis in building energy assessment are discussed.
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Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition
TL;DR: It is shown that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation.
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The In-the-Ear Recording Concept: User-Centered and Wearable Brain Monitoring
David Looney,Preben Kidmose,Cheolsoo Park,Michael Ungstrup,Mike Lind Rank,K. Rosenkranz,Danilo P. Mandic +6 more
TL;DR: The potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and user-centered approach to brain monitoring, and promises a number of advantages, including fixed electrode positions, user comfort, robustness to electromagnetic interference, feedback to the user, and ease of use.
Journal ArticleDOI
Emd via memd: multivariate noise-aided computation of standard emd
TL;DR: It is shown that the noise-assisted MEMD (NA-MEMD) approach, which utilizes the dyadic filter bank property of MEMD, provides a solution to the above problems when used to calculate standard EMD.
Journal ArticleDOI
Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition
TL;DR: Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG).