• Journal of Korean Academy of Nursing
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• v.48 no.2
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• pp.208-220
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• 2018

Purpose: The aim of this study was to analyze the effects of neurofeedback training for reducing stress and enhancing self-regulation in late adolescence to identify the possibility of use for nursing intervention. Methods: A nonequivalent control group pre-post quasi-experimental design was used. Participants were 78 late adolescents assigned to the experimental group (n=39) that received the neurofeedback training and the control group (n=39). Data were collected on heart rate variability (HRV) and skin conductance level (SCL) to assess stress-biomarker response. The questionnaire contained 164 items from: Positive and Negative Affect Schedule (PANAS), Symptom Checklist-90-Revised (SCL-90-R) and Self-regulatory Ability scale. The neurofeedback training was based on the general adaptation syndrome and body-mind medicine. The intervention was conducted in a total of 10 sessions for 30 minutes per session with high-beta, theta and sensory motor rhythm training on scalp at central zero. Results: There were significant difference in standard deviation of normal to normal interval (p=.036) in HRV and SCL (p=.029) of stress-biomarker response between the two groups. Negative affect (p=.036) in PANAS and obsessive compulsive (p=.023) and depression (p<.001) in SCL-90-R were statistically significant. Self-regulation mode (p=.004) in self-regulation ability scale showed a significant difference between the two groups. Conclusion: The results indicated that the neurofeedback training is effective in stress-biomarkers, psychoemotional stress response and self-regulation. Therefore, neurofeedback training using neuroscientific approach based on brain-mind-body model can be used as an effective nursing intervention for late adolescents in clinics and communities for effective stress responses.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.595-597
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• 2015

LMC X-4 is an eclipsing high-mass X-ray binary exhibiting a superorbital modulation with a period of ~ 30:5 days. We present a detailed study of the variations of the superorbital modulation period with a time baseline of ~ 18 years. The period determined in the light curve collected by the Monitor of All-sky X-ray Image (MAXI) significantly deviates from that observed by the All Sky Monitor (ASM) onboard the Rossi X-ray Timing Explorer (RXTE). Using the data collected by RXTE/ASM, MAXI, and the Burst Alert Telescope (BAT) onboard Swift, we found a significant period derivative, $\dot{P}=(2.08{\pm}0.12){\times}10^{-5}$. Furthermore, the O{C residual shows complex short-term variations indicating that the superorbital modulation of LMC X-4 exhibits complicated unstable behaviors. In addition, we used archive data collected by the Proportional Counter Array (PCA) on RXTE to estimate the orbital and spin parameters. The detected pulse frequencies obtained in small time segments were fitted with a circular orbital Doppler shift model. In addition to orbital parameters and spin frequency for each observation, we found a spin frequency derivative of $\dot{v}=(6.482{\pm}0.011){\times}10^{-13}Hz{\cdot}s^{-1}$. More precise orbital and spin parameters will be evaluated by the pulse arrival time delay technique in the future.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.365-365
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• 2012

이수 및 치수를 위한 수공구조물 설계 및 하천기본계획 수립의 요점은 설계홍수량의 산정에 있으며, 통계적으로 유의성을 가지는 설계홍수량을 산정하기 위해서는 일반적으로 30년 이상 관측된 홍수자료가 요구된다. 우리나라의 경우 대부분의 유역이 미계측 유역이거나 관측년수가 비교적 작은 경우가 많으므로, 상대적으로 자료 연한이 긴 강우자료를 빈도분석한 후 이를 강우-유출 모형에 입력하여 확률홍수량을 추정하는 간접적인 방법이 주로 이용되며 사용된 강우의 빈도가 홍수의 빈도와 동일하다는 가정을 기본으로 한다. 그러나 동일한 강우량이 발생하더라도 강우의 강도, 지속시간, 유역의 선행함수조건 등과 같은 유역 특성에 따라 유출의 특성은 현저히 다르게 나타나며 결국 이러한 특성은 입력자료, 강우-유출 모형, 기후변동성 등과 같은 불확실성 요소로 인식될 수 있다. 따라서 본 연구에서는 이러한 불확실성을 고려할 수 있는 강우-유출 모의기법을 개발하여 이를 통해 홍수빈도곡선을 유도할 수 있는 방법론을 제시하고자 한다. 불확실성 분석을 위해 기존 HEC-1 강우-유출 모형에서 Bayesian MCMC 기법을 적용하여 매개변수들의 사후분포를 추정하여 매개변수들의 최적화 및 불확실성 분석을 수행하였다. 마지막으로 기후변화 영향을 통합한 홍수빈도곡선을 유도하기 위해서 극치강수를 모의하는 것이 필요하며, 본 연구에서는 극치값 재현에 있어서 우수한 성능을 발휘하는 Kernel-Pareto Piecewise분포 기반의 강우모의발생 기법을 적용하여 HEC-1모형과 연동되도록 모형을 개발하였다. 본 연구에서 제안하는 방법론은 기존 홍수빈도곡선 유도 방법에서 불확실성을 분석하기 위해 모든 변수들을 독립사상으로 간주하고 Monte Carlo Simulation을 수행함으로서 매개변수들간의 상호연관성, 상관성, 조건부 확률들을 고려할 수 없었던 점을 Bayesian 모형을 통해 매개변수들간의 조건부 확률을 고려한 매개변수의 사후분포 도출을 가능하게 하여 보다 현실적인 강우-유출 관계 도출이 가능하고 불확실성 구간이 자연적으로 도출됨으로서 향후, 신뢰성 있는 수자원 계획수립에 유용한 자료로 활용이 가능할 것으로 판단된다.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• Journal of Korea Water Resources Association
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• v.39 no.4 s.165
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• pp.335-346
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• 2006

Among various input data to hydrologic models, rainfall measurements arguably have the most critical influence on the performance of hydrologic model. Traditionally, hydrologic models have relied on point gauge measurements to provide the area-averaged rainfall information. However, rainfall estimates from gauges become inadequate due to their poor representation of areal rainfall, especially in situations with sparse gauge network. Alternatively, radar that covers much larger areas has become an attractive instrument for providing area- averaged precipitation information. Despite of the limitation of the QPE(Quantitative Precipitation Estimation) using radar, we can get the better information of spatial variability of rainfall fields. Also, rain-gauges give us the better quantitative information of rainfall field. Therefore, in this study, we developed improved methodologies tu estimate rainfall fields using an ordinary cokriging technique which optimally merges radar reflectivity data into rain-gauges data.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.6 no.2
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• pp.176-186
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• 1996

The fit of a respirator to the face of an individual can be determined by a qualitative fit test (QLFT) or a quantitative fit test (QNFT). The pass/fail decision from a QLFT or QNFT for the same respirator on the same individual may vary from one wearing to the next, because the human facial features are complex and the respirator may not fit to the face in the same way every time it is worn. This study reports how the fit factors (FF) resulting from a QNFT on an individual vary from day to day and depend on the movements in the six fit test exercises. The reported FFs provide an objective and numerical basis (FF) which does not depend on the subject's voluntary or involuntary response. Four half-mask (H1-H4) and four full-facepiece respirators (F1-F4) were fit tested on one wearer 10 times a day for 5 days with a PortaCount (model 8010, TSI). The FFs obtained for each set of 10 fit tests on a specific day and 50 fit tests on five days involving one of the six exercise regimes have been recorded as log-normal distributions. All of the geometric standard deviations (GSD) of the overall FFs varied widely among every wearing and day except for H1 and F3, and the variability of the half-mask respirators was larger than that of the full-facepiece respirators. Among the six exercise regimes, reading or talking (RT) had markedly the lowest exercise FFs on the tested individual. Generally, there were significant differences between the first normal breathing (NB1) FFs and the remaining exercise FFs.

• Journal of Korean Neurosurgical Society
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• v.53 no.6
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• pp.331-336
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• 2013

Objective : The offset connector can allow medial and lateral variability and facilitate intralaminar screw incorporation into the construct. The aim of this study was to compare the biomechanical characteristics of C7 intralaminar screw constructs with and without offset connector using a three dimensional finite element model of a C6-7 cervical spine segment. Methods : Finite element models representing C7 intralaminar screw constructs with and without the offset connector were developed. Range of motion (ROM) and maximum von Mises stresses in the vertebra for the two techniques were compared under pure moments in flexion, extension, lateral bending and axial rotation. Results : ROM for intralaminar screw construct with offset connector was less than the construct without the offset connector in the three principal directions. The maximum von Misses stress was observed in the C7 vertebra around the pedicle in both constructs. Maximum von Mises stress in the construct without offset connector was found to be 12-30% higher than the corresponding stresses in the construct with offset connector in the three principal directions. Conclusion : This study demonstrated that the intralaminar screw fixation with offset connector is better than the construct without offset connector in terms of biomechanical stability. Construct with the offset connector reduces the ROM of C6-7 segment more significantly compared to the construct without the offset connector and causes lower stresses around the C7 pedicle-vertebral body complex.

• Journal of Korean Neurosurgical Society
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• v.60 no.4
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• pp.441-447
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• 2017

Objective : Computed tomography (CT)-based method of three dimensional (3D) analysis ($MIMICS^{(R)}$, Materialise, Leuven, Belgium) is reported as very useful software for evaluation of OPLL, but its reliability and reproducibility are obscure. This study was conducted to evaluate the accuracy of $MIMICS^{(R)}$ system, and inter- and intra-observer reliability in the measurement of OPLL. Methods : Three neurosurgeons independently analyzed the randomly selected 10 OPLL cases with medical image processing software ($MIMICS^{(R)}$) which create 3D model with Digital Imaging and Communication in Medicine (DICOM) data from CT images after brief explanation was given to examiners before the image construction steps. To assess the reliability of inter- and intra-examiner intraclass correlation coefficient (ICC), 3 examiners measured 4 parameters (volume, length, width, and length) in 10 cases 2 times with 1-week interval. Results : The inter-examiner ICCs among 3 examiners were 0.996 (95% confidence interval [CI], 0.987-0.999) for volume measurement, 0.973 (95% CI, 0.907-0.978) for thickness, 0.969 (95% CI, 0.895-0.993) for width, and 0.995 (95% CI, 0.983-0.999) for length. The intra-examiner ICCs were 0.994 (range, 0.991-0.996) for volume, 0.996 (range, 0.944-0.998) for length, 0.930 (range, 0.873-0.947) for width, and 0.987 (range, 0.985-0.995) for length. Conclusion : The medical image processing software ($MIMICS^{(R)}$) provided detailed quantification OPLL volume with minimal error of inter- and intra-observer reliability in the measurement of OPLL.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.11-26
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• 2009

In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern- based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3%.