• Journal of Korea Port Economic Association
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• 제36권3호
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• pp.115-136
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• 2020

This study aims to analyze the impact of China's iron ore imports and exports on the tramper freight rate of China. The import volume of iron ore in China, the export volume of steel products in China, and exogenous variables were used as independent variables. The dependent variables were BDI, BCI, C5, C10, C14, BSI, S2, and S3. Correlation analysis and regression analysis were conducted. The correlation analysis showed that China's iron ore imports were not related to the remaining BDI, BCI, BSI, C5, C10, S2, and S3, except for the C14 index. However, there was a positive correlation between the ship's space and international oil prices, and it was not related to China's Purchasing Managers Index (PMI). The export volume of steel products was negatively correlated with BDI, BCI, BSI, C5, C10, C14, S2, S3, and international oil prices, and was not related to iron ore imports, ship space, and China's PMI. In the verification of the hypothesis between China's iron ore imports and exogenous variables, China's PMI was rejected within the hypothesis. However, the hypothesis on international oil prices and ship space was adopted. In the verification of the hypothesis between China's steel export volume and exogenous variables, the hypothesis on BDI and the S3 index was adopted, and the hypothesis on BSI and S2 was rejected. In the analysis results of this study, the ship space and oil prices were adopted in all the hypothesis results. Domestic companies participating in the tramper shipping market will need to be prepared through continuous monitoring of related indicators.

• East Asian mathematical journal
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• 제23권1호
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• pp.75-82
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• 2007

In this paper we give a concrete computational method to characterize $S_3$-algebras.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• 제11권10호
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• pp.804-808
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• 1998

The inductively coupled plasma(ICP) etching of platinum with BCl$_3$/Cl$_2$ gas chemistry has been studied. X-ray photoelectron spectroscopy (XPS) was used to investigate the chemical binding states of the etched surface. The plasma characteristics was extracted from optical emission spectroscopy (OES) and a single Langmuir probe. In this case of Pt etching using BCl$_3$/Cl$_2$ gas chemistries, the result of OES and Langmuir probe showed the increase of Cl radicals and ion current densities in the plasmas with increasing Cl$_2$ gas ratio. At the same time, XPS results indicated that the intensities of Pt 4f decreased with increasing Cl$_2$ gas ratio. The decrease of Pt 4f intensities implies the increase of residue layer thickness on the etched Pt surface.

• Journal of Biomedical Engineering Research
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• 제31권5호
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• pp.359-364
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• 2010

The purpose of the brain-computer (machine) interface (BCI or BMI) is to provide a method for people with damaged sensory and motor functions to use their brain to control artificial devices and restore lost ability via the devices. Functional electrical stimulation (FES) is a method of applying low level electrical currents to the body to restore or to improve motor function. The purpose of this study was to develop a SSVEP-based BCI rehabilitation training system with FES for spinal cord injured individuals. Six electrodes were attached on the subjects' scalp ($PO_Z$, $PO_3$, $PO_4$, $O_z$, $O_1$ and $O_2$) according to the extended international 10-20 system, and reference electrodes placed at A1 and A2. EEG signals were recorded at the sampling rate of 256Hz with 10-bit resolution using a BIOPAC system. Fast Fourier transform(FFT) based spectrum estimation method was applied to control the rehabilitation system. FES control signals were digitized and transferred from PC to the microcontroller using Bluetooth communication. This study showed that a rehabilitation training system based on BCI technique could make successfully muscle movements, inducing electrical stimulation of forearm muscles in healthy volunteers.

• Journal of the Korean Institute of Intelligent Systems
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• 제23권2호
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Journal of the Ergonomics Society of Korea
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• 제21권3호
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• pp.1-12
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• 2002

Recently, EEG gains much interests due to its applicability for people to communicate directly with computers without detouring motor output. This study was designed to address this issue if EEG can be successfully used to predict limb movement. It was found that ordinary people appeared to show significant difference in brainwaves between right hand (foot) and left hand (foot) movement. Lateral asymmetry was also found to interact significantly with EEG. Further research is urged with refined method to provide more useful insights into EEG-based BCI.

• International Journal of Contents
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• 제12권3호
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• Anatomy and Cell Biology
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• 제57권1호
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• pp.45-60
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• 2024

Morphologic changes in the brain through aging, as a physiologic process, may involve a wide range of variables including ventricular dilation, and sulcus widening. This study reports normal ranges of these changes as standard criteria. Normal brain computed tomography scans of 400 patients (200 males, 200 females) in every decade of life (20 groups each containing 20 participants) were investigated for subcortical/cortical atrophy (bicaudate width [BCW], third ventricle width [ThVW], maximum length of lateral ventricle at cella media [MLCM], bicaudate index [BCI], third ventricle index [ThVI], and cella media index 3 [CMI3], interhemispheric sulcus width [IHSW], right hemisphere sulci diameter [RHSD], and left hemisphere sulci diameter [LHSD]), ventricular symmetry. Distribution and correlation of all the variables were demonstrated with age and a multiple linear regression model was reported for age prediction. Among the various parameters of subcortical atrophy, BCW, ThVW, MLCM, and the corresponding indices of BCI, ThVI, and CMI3 demonstrated a significant correlation with age (R²≥0.62). All the cortical atrophy parameters including IHSW, RHSD, and LHSD demonstrated a significant correlation with age (R²≥0.63). This study is a thorough investigation of variables in a normal brain which can be affected by aging disclosing normal ranges of variables including major ventricular variables, derived ventricular indices, lateral ventricles asymmetry, cortical atrophy, in every decade of life introducing BW, ThVW, MLCM, BCI, ThVI, CMI3 as most significant ventricular parameters, and IHSW, RHSD, LHSD as significant cortical parameters associated with age.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2004년도 학술대회지
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of rehabilitation welfare engineering & assistive technology
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• 제10권3호
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.