Sunhui Kim

One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality att

Countries around the world are expanding their investment in the new and renewable energy industry for strengthening energy security, improving air pollution, responding to climate change, and tackling energy poverty. In Korea, with the nuclear phase-out declaration in 2017, the government has announced a policy to expand the ratio of new and renewable energy from 4.7% to 20% by 2030. This study examines a floating photovoltaic power generation system, which is a new and renewable energy source.

In expressive arts therapy, visual art, movement, music, poetry, and creative writing offer clients opportunities to explore their hidden feelings expressed in the art forms. The colors, lines, motions, or sounds expressed during the therapy session promote better understanding of the self with support of the therapist. It is crucial to have a creative connection, not only between the self and its inner world but also between the client and the therapist for the healing process to unfold. This a

With recent advancements in artificial intelligence technologies and human–computer interaction, strategies to identify the inner emotional states of humans through physiological signals such as electroencephalography (EEG) have been actively investigated and applied in various fields. Thus, there is an increasing demand for emotion analysis and recognition via EEG signals in real time. In this paper, we proposed a new framework, emotion Recognition-Assisted Deep learning Framework from eeg sign

Memristor devices are considered to have the potential to implement unsupervised learning, especially spike timing-dependent plasticity (STDP), in the field of neuromorphic hardware research. In this study, a neuromorphic hardware system for multilayer unsupervised learning was designed, and unsupervised learning was performed with a memristor neural network. We showed that the nonlinear characteristic memristor neural network can be trained by unsupervised learning only with the correlation bet

An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal correlations. Therefore, new methods that can clearly distinguish emotional states in EEG data are required. In this paper, we propose a new emotion recognition method, named AsEmo. The proposed method ext

The adsorption of triethylenediamine (TEDA) at 300 K is observed to occur via hydrogen bonding to isolated Al-OH groups on the surface of partially dehydroxylated high area gamma-Al(2)O(3) powder. This form of bonding results in +0.3 to +0.4% blue shifts in the CH(2) scissor modes at 1455 cm(-1) and a -0.4% red shift in the CN skeletal mode at 1060 cm(-1), compared to the gas-phase frequencies. Other modes are red shifted less than 0.1%. The isolated OH modes are red shifted by -200 to -1000 cm(

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disor

The hydrogen bonding of the triethylenediamine (TEDA) molecule to isolated Al-OH groups on partially dehydroxylated high area gamma-Al(2)O(3) powder has been studied using transmission IR spectroscopy. It has been found that TEDA adsorbs both singly and as multiple species to single Al-OH groups in clearly separable equilibrium stages of adsorption at 300 K. The reversible adsorption of a single TEDA molecule to Al-OH fits the Langmuir adsorption isotherm well, and the enthalpy of adsorption is

In the Republic of Korea, efficient biogas-fuelled power systems are needed to use the excess biogas that is currently burned due to a lack of suitable power technology. We examined the performance of a biogas-fuelled micro-gas turbine (MGT) system and a bottoming organic Rankine cycle (ORC). The MGT provides robust operation with low-grade biogas, and the exhaust can be used for heating the biodigester. Similarly, the bottoming ORC generates additional power output with the exhaust gas. We sele

Model parameters' estimation is one of the most important tasks in the analysis and design process of a nonlinear dynamic system in real time, especially in the presence of noise. This article presents a novel approach in estimating important parameters of gray-box model for such a system on real nonlinear EEG to simulate efficiently the dynamic characteristics of neurons. Specifically, the proposed methodology exploits unscented Kalman filter (UKF) that is combined with chaos neural population