• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.63.3-64
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• 2020

수소 라이먼 알파선은 관측이 어려운 외부은하의 성간 물질이나 성운 주위의 물질의 운동학적, 기하학적 상태를 알려주는 지표이다. 특히 라이먼 알파 방출 스펙트럼의 두 최고점에서 측정한 선속도 차이는 물질의 수축, 팽창 여부에 영향을 받기 때문에 은하의 역학적 특성을 연구하는 데에 있어 새로운 도구로서 각광받고 있다. 관측에서 얻어지는 은하들의 선속도 차이는 100km/s에서 800km/s까지 넓은 영역에서 존재한다. 선행 분자구름 규모의 연구에서 얻어진 선속도 차이는 상대적으로 작은 선속도 차이(148.54km/s)를 가진다. 그래서 이 연구에서는 더 큰 규모인 은하에서 라이먼 알파 선속도 차이를 확인하고 은하내 물리량의 영향을 알아보았다. 이 연구에서는 복사유체역학 시뮬레이션 코드 RAMSES-RT를 활용한, 각각 다른 물리량을 가진 은하 시뮬레이션 결과를 활용하였다. 은하 내 가스의 비율, 금속함량비를 다르게 하였으며, 각 시뮬레이션들은 몬테- 카를로 공진선 복사전달 코드 RASCAS를 이용하여 라이먼 알파선의 복사 과정을 계산하였다. 첫 번째로 기준 은하 시뮬레이션과 분자구름 시뮬레이션(Kimm+19)의 결과를 비교한 결과 148.54km/s에서 221.76km/s로 선속도 차이의 평균 값이 상승한 것을 확인하였다. 이는 성간 물질의 존재 유무의 차이로 인한 것이다. 은하 내 가스의 금속함량비를 증가시킨 경우, 은하 내 먼지량과 젊은 별들이 별 생성 구름에 머무는 시간이 증가하기 때문에 기준 은하와 비교하여 선속도 차이가 작아졌다.(206.9km/s) 반면 은하의 가스량을 증가시켯을 때는 산란 횟수 증가로 인한 상대적으로 큰 선속도 차이(298.51km/s)를 확인할 수 있었다. 또한 기준은하에 대해, 난류의 효과를 포함하여 선속도 차이를 비교한 결과, 선속도 차이는 (308.8km/s)상승하였다. 이를 통해 성간 물질의 물리량 차이만으로는 400km/s 이상의 큰 선속도 차이를 만드는 것은 어렵다. 관측에서 보이는 400km/s 이상의 몇몇 큰 선속도 차이의 은하를 위해서는 이 시뮬레이션에 포함되지 않은 성운 주위의 물질과 같은 부분이나, 은하 합병과 같은 극한의 상황이 필요할 것이다.

• Sleep Medicine and Psychophysiology
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• v.28 no.2
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• pp.78-85
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• 2021

Objectives: When elderly patients show depressive symptoms, discrimination between depressive disorder and prodromal phase of Alzheimer's disease is important. We tested whether a quantitative electroencephalogram (qEEG) marker was associated with cerebral amyloid-β (Aβ) deposition in older adults with depression. Methods: Non-demented older individuals (≥ 55years) diagnosed with depression were included in the analyses (n = 63; 76.2% female; mean age ± standard deviation 73.7 ± 6.87 years). The participants were divided into Aβ+ (n = 32) and Aβ- (n = 31) groups based on amyloid PET assessment. EEG was recorded during the 7min eye-closed (EC) phase and 3min eye-open (EO) phase, and all EEG data were analyzed using Fourier transform spectral analysis. We tested interaction effects among Aβ positivity, condition (EC vs. EO), laterality (left, midline, or right), and polarity (frontal, central, or posterior) for EEG alpha band power. Then, the EC-to-EO alpha reactivity index (ARI) was examined as a neurophysiological marker for predicting Aβ+ in depressed older adults. Results: The mean power spectral density of the alpha band in EO phase showed a significant difference between the Aβ+ and Aβ- groups (F = 6.258, p = 0.015). A significant 3-way interaction was observed among Aβ positivity, condition, and laterality on alpha-band power after adjusting for age, sex, educational years, global cognitive function, medication use, and white matter hyperintensities on MRI (F = 3.720, p = 0.030). However, post-hoc analyses showed no significant difference in ARI according to Aβ status in any regions of interest. Conclusion: Among older adults with depression, increased power in EO phase alpha band was associated with Aβ positivity. However, EC-to-EO ARI was not confirmed as a predictor for Aβ+ in depressed older individuals. Future studies with larger samples are needed to confirm our results.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.2
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• pp.310-320
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• 2022

This study aimed to ascertain the most efficient level of compression to be applied on a particular area along the upper extremities whilst developing functionality to prevent musculoskeletal disorders among workers who frequently use their arms. The compression areas were divided into upper arm, forearm, and wrist. The compression levels were applied using three types of bands. Comparisons on these bands' usability were based on evaluations of recorded brainwaves and subjective sensation response. Nine men in their twenties were selected for the experiment. The results revealed that when compression was applied, the left and right occipital lobes, plus the parietal lobe, were activated. Also, the alpha wave activity tended to increase, thereby exemplifying the compression's positive effects. The most physiologically efficient and subjectively preferred compression levels were 1.3 and 2.6 kPa for the upper arm and wrist. Furthermore, the compression level at the forearm should be at least 2.0 kPa.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.549-549
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• 2013

고전압 펄스 플라즈마를 액체 속에서 발생시켜 수소 스펙트럼의 광학적 특성을 연구하였다. 고전압 펄스 발생 장치인 막스 제네레이터는 용량이 $0.5{\mu}F$인 축전기 5개로 이루어져 있다. 각각의 축전기는 전원 장치를 이용하여 저항을 통해 병렬로 충전되며, 방전 시에는 불꽃 방전 스위치에 의해 동시에 직렬로 연결되어 고전압을 발생시킨다. 따라서, 출력 전압과 전류는 40kV, 3 kA이며 총 에너지는 약 125 J이다. 직육면체 모양의 폴리카보네이트 용기 내부의 양쪽면에는 탐침 모양의 전극이 구성되어 있으며 전극 사이에서 고전압을 가진 플라즈마가 형성된다. 실험에서 액체로는 증류수를 사용하였다. 액체 방전 시 발생하는 수소 스펙트럼을 관측하기 위해 초점거리 30 cm의 monochromator를 이용하였고, 수소 알파선의 656.3 nm와 수소 베타선의 434.1 nm를 관측하였다. 전자 밀도의 측정법으로는 Stark broadening법을 이용하여 측정하였으며, 전자 온도는 Stark profile의 상대적인 전자 밀도의 비를 이용하여 계산하였다. 전자밀도는 실험조건에서 약 $3{\times}10^{15}cm^{-3}$, 전자온도는 약 2.5 eV가 측정되었다.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05b
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• pp.223-230
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• 1997

최근들어 그 활용도가 점점 증대되고 있는 DLU(Diamond-like Carbon) /Diamond 박막(thin film)의 합성기술을 개발하여 원자력분야에 응용하고자 시도하였다. 이를 위하여 13.56 MHz의 고주파(RF: radio-frequency)를 사용하는 플라즈마 화학증착(PECVD: Plasma Enhanced Chemical Vapor Deposition) 장치를 직접 제작하여 탄소함유(CH$_4$, $CO_2$...등) 기체로부터 기본적인 DLC 박막증착시험을 수행하였다. 실험은 진공증착기(vacuum chamber)내의 압력(pressure), 탄소함유 기체의 조성비, 그리고 바이어스전압(negative self-bias voltage)둥을 변화시키면서 수행하였다. 증착속도(deposition rate)는 증착층의 두께를 알파스템($\alpha$-step)으로 측정하여 결정하였으며, 이로부터 증착속도가 압력 및 바이어스 전압의 증가에 따라 증가함을 알 수 있었다. 또한 바이어스 전압 300V 이상에서 $CO_2$량 증가가 증착속도를 촉진시킨다는 사실도 확인하였다. 그리고 EPMA(electron probe micro-analyser) 및 Raman 스펙트럼분석을 통하여 증착층의 구조가 DLC 임을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Sleep Medicine and Psychophysiology
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• v.10 no.2
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• pp.100-107
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• 2003

Objectives: Periodic leg movements in sleep (PLMS) might be subdivided based upon whether or not they are associated with visible EEG microarousals (MA). MA is considered to be responsible for nonrestorative sleep and daytime fatigue. The American Sleep Disorders Association's (ASDA) scoring rules for MA based on visual analysis of the EEG changes suggest that MA should last more than 3 seconds. However, it has been suggested that visual analysis may not detect some changes in EEG activity. This study is aimed at measuring changes in EEG spectra during PLMS without MA in order to better understand the arousing response of PLMS. Methods: Ten drug-free patients (three men and seven women) diagnosed with PLMS by polysomnography were studied. Spectral analysis of the EEG was performed in each patient on 30 episodes of PLMS without MA, chosen randomly across the night in stage 2 non-REM sleep. We applied stricter criteria for MA compared to ASDA, by defining it as a return to alpha and theta frequency lasting at least 1 second. Results: The mean PLMS index was $16.7{\pm}10.0$. The mean PLMS duration was $1.3{\pm}0.7$ seconds. Comparison of 4-second EEG activity both before and after the onset of PLMS without MA using independent t-test showed that the movements were associated with significant increase of relative activity in the delta band (p=0.000) and significant decrease of activity in the alpha (p=0.01) and sigma (p=0.000) bands. No significant decrease in the theta (p=0.05), beta (p=0.129), or gamma (p=0.062) bands was found. Conclusion: PLMS without MA was found to be associated with EEG change characterized by increase in the delta frequency band. This finding seems to be compatible with the hypothesis of an integrative hierarchy of arousal responses of Sforza's. Considering that the subjects had lower PLMS index and shorter PLMS duration than those of the previous study, it is suggested that an even less severe form of PLMS without MA could induce neurophysiologic change, which may potentially be of clinical significance.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.5
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• pp.487-493
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• 2011

Alpha fetoprotein(AFP), which is serological marker for hepatocellular carcinoma, was quantitatively measured by its normal concentration, 10 ng/ml, with a label-free piezoelectric microcantilever mass sensor. The principle of detection is based on changes in the resonant frequency of the piezoelectric microcantilever before and after target molecules are attached to it, and its resonant frequency is measured electrically using a conductance spectrum. The resonant frequency of the developed sensor is approximately 1.34 MHz and the mass sensitivity is approximately 175 Hz/pg. The sensor has high reliability as mass sensor by reducing the effect of surface stress on resonant frequency due to attached proteins. 'Dip and dry' technique was used to react the sensor with reagents for immobilizing AFP antibody on the sensor and detecting AFP antigen. The measured mass of the detected AFP antigen was 6.02 pg at the concentration of 10 ng/ml, and 10.67 pg at 50 ng/ml when the immunoreaction time was 10 min.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.33-38
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• 2008

Introduction: It has been proposed that narcolepsy and REM sleep behavior disorder (RBD) have overlapped symptom profile and pathophysiology. This study was aimed at measuring and comparing changes in EEG frequency band of REM sleep in narcolepsy and RBD, applying EEG spectral analysis method. Methods: Nine patients diagnosed as narcolepsy and the same number of RBD patients were studied. Spectral analysis of the REM sleep EEG was performed in each patient on 9 epochs selected evenly from the first, second, and third REM periods. Then, we compared frequency band percentages of REM sleep EEG in narcolepsy and RBD. Results: Narcolepsy patients had significantly higher delta frequency ratio than RBD ones (p=0.00). In alpha and beta2 frequency bands, RBD patients showed higher percentage than narcolepsy ones. Slow wave sleep was more prevalent in narcolepsy patients. But, no difference of REM sleep percentage was found between the two groups (p=0.93). Conclusion: Higher delta frequency ratio in REM sleep of narcolepsy patients than RBD ones reflects that sleep-promoting mechanism is more dominant in narcolepsy than in RBD.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.295-301
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• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.