• Journal of Korea Port Economic Association
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• v.27 no.2
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• pp.217-240
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• 2011

In this study, vector autoregressive and vector error correction models in the short-run dynamics are considered to analyze the effect of the changes in international crude oil prices on Baltic dry index, Baltic Capesize index and Baltic Panamax index, and the intercorrelations between Capesize and Panamax prices, respectively. First, using the vector autoregressive model, the changes in international crude oil price have a statistically significant positive effect for Capesize at lag 1, for Panamax a significant negative effect at lag 3 and a significant positive effect for Baltic dry index at lag 1. From the impulse response analysis, the international crude oil price causes Baltic dry index to increase in the sort-run and the effect converges on the mean after 3 months. Second, using the vector error correction model, the empirical results for the spillover effects between Capesize and Panamax markets provide that in the case of the deviation from a long-run equilibrium the Panamax price is adjusted toward decreasing. The increases in freight rates of the Capesize market at lag 1 lead to increase the freight rates in Panamax market at present. The Panamax responses from the Capesize shocks increase rapidly for 3 months and the effect converges on the mean after 5 months. The Capesize responses from the Panamax shocks are relatively small, and increase weakly for 3 months and the effect disappears thereafter.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.41-46
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• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We develop concentration wireless transmission system for controlling hardware by using this signal. Two channels are used for measuring EEG signal on front head and Biopac system with MP100 and EEG100C was used for measuring EEG signal, amplifying and filtering the signal. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the measured EEG signals. As a result, SMR wave, Mid-Bata wave, $\Theta$ wave classified. We extracted the concentration index by adapting concentration extraction algorithm. This concentration uldex was transferred into logo automobile device by wireless module and applied for BCI application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.695-701
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• 2008

This study has been made to research on the brain wave characteristics of baduk expert by BCI(Brain Computer Interface). The test was based on the researches from 1th September, 2005 to 30th December, 2005, compared with the ones of the standardized general public. The number of the general public are 695 (elementary school students 423, middle and high school students 161, adults 111) and the number of the baduk players are 57 (researchstudents 15, Korean baduk club students 16, professional baduk players 26). The research data show that the baduk players have the higher indexes than the general public in Self Regulation quotient p=.002, Attention Quotient(left) p=.002, Emotion Quotient p=.027, Stress Quotient(left) p=.002 and Brain Quotient p=.006. There are some differences in brain functions between baduk players and the ordinary people. Difference in functions of the brain among baduk experts is also analyzed. That result shows that there is no different brain function between professional baduk player.

• Proceedings of the ESK Conference
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• 2004.10a
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• pp.59-59
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• 2004

• Journal of IKEEE
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• v.18 no.3
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• pp.368-375
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• 2014

Brain computer interface(BCI) technology has been continuously developed due to the continuous development of interface technology and the promotion of brain wave research. In this paper, we propose a driving habit management system by adopting BCI to transportation. The proposed system consists of the electroencephalogram(EEG) measuring unit, the EEG analysis unit, the memory section for storing the state information of drivers, the speed controller unit and the alarming section for generating warnings. Our proposed system can reduce the drowsy driving, improve the driving habits of users and help to prevent traffic accidents.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1171-1180
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• 2019

Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.156-166
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• 2012

Over the past 15 years productive BCI research programs have arisen. Current mainstream EEG electrode setups permit efficient recordings but most of electrodes has the disadventages of need for skin preparation and gel application to correctly record signals. The new gel-free probe was adapted for EEG recording and it can be fixed to the scalp with the micro needle without neuro-gel. It use standard EEG cap for wearing electrodes on scalp so it is compatible with standard EEG electrodes. A comparison between electrode characteristics is achieved by performing simultaneous recordings with the gel electrodes and gel-free probe placed in parallel scalp positions on the same anatomical regions. The quality of EEG recordings for all two types of experimental conditions is similar for gel-electrodes and gel-free probe. Subjects also reported not having special tactile sensations associated with wearing of gel-free probes. According to our results, it is expected that gel-free probe can be adapted to BCI, BMI(Brain Machine Interface), HMI(Human Machine Interface) because of its simple application and comfortable wearing process.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.451-456
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• 2019

Recently, various bioinformation technologies have been proposed, and research and development on the collection and analysis of the human body related bioinformation have been continuously conducted to support the human life environment and healthcare. These biomedical research and development processes add to the redundancy and complexity of the R&D elements and put a heavy burden on the follow-up research developers. Therefore, this study utilizes an open bioinformation platform that effectively supports the collection and analysis of bioinformation to improve the redundancy and complexity of bioinformatics R&D based on the bioinformatics platform. In addition, I propose an open interface that supports acquisition, processing, analysis, and application of bio-signals. In particular, we propose a biometric information normalization pattern model through data analysis modeling of brain wave information based on an open interface.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.373-376
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• 2004

최근에 뇌파를 이용하여 컴퓨터와 통신하거나 기기를 제어할 수 있는 이른바 뇌-컴퓨터 인터페이스BCI(Brain-Computer Interface)에 대한 연구가 대두되고 있다. 이러한 BCI 연구의 궁극적 목표는 다양한 정신상태에 따른 뇌파의 특성을 파악하여 컴퓨터나 기기 등을 제어하는 것이다. 본 논문에서는 움직임과 관련 있는 10～12Hz의 mu파 영역에서의 ERD/ERS를 계산하였고, 그 결과 왼쪽과 오른쪽 손의 움직임을 상상할 때에 운동과 관련된 기능이 집중되어 있는 일차운동영역(primary motor area)의 mu파에서 ERD/ERS의 차이가 나타남을 발견하였다 또한, RLS방법을 사용한 Adaptive Autoregressive Model 계수의 특징을 추출을 하였으며, 이를 신경망으로 학습시켜 인식률을 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.151-154
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• 2008

본 논문은 Bayesian Networks를 이용해서 EEG 신호를 분석해서 사람의 감정을 분석하는 방법을 제안하였다. 현제 연구자들은 Electroencephalogram(EEG) 신호를 기반으로 사람의 두뇌와 컴퓨터의 인터페이스에 관한 연구를 하고 있다. 기존에는 간질이나 발작 등을 의학 분야와 사람의 정서에 따라 뇌파분석을 하는 심리학의 영역에서 연구가 되어져 왔다. 최근에는 사람의 두뇌와 컴퓨터 간의 인터페이스를 통한 여러 가지 공학적인 접근이 이루어지고 있다. 본 논문에서는 사람의 감정에 따라 Brain-Computer Interface (BCI)를 통해서 EEG 신호를 분석하고 잡음을 제거해서 보다 정확한 신호를 추출한 다음 각각의 주파수 영역으로 분류를 하였다. 분류된 값들은 Bayesian Networks를 이용해서 피 실험자가 어떠한 감정을 나타내는지 확률 값으로 나타낸다. 확률 값에 의해서 피 실험자가 어떠한 감정인지를 인식하게 되는 것이다.