• Sleep Medicine and Psychophysiology
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• v.10 no.1
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• pp.52-60
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• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.11-19
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• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• Tunnel and Underground Space
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• v.30 no.4
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• pp.359-381
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• 2020

Within the framework of DECOVALEX-2019 Task D, full-scale engineered barriers experiment (FEBEX) at Grimsel Test Site was numerically simulated to investigate an applicability of implemented Barcelona basic model (BBM) into TOUGH2-MP/FLAC3D simulator, which was developed for the prediction of the coupled thermo-hydro-mechanical behavior of bentonite buffer. And the calculated heater power, temperature, relative humidity, total stress, saturation, water content and dry density were compared with in situ data monitored in the various sections. In general, the calculated heater power and temperature provided a fairly good agreement with experimental observations, however, the difference between power of heater #1 and that of heater #2 could not captured in the numerical analysis. It is necessary to consider lamprophyre with low thermal conductivity around heater #1 and non-simplified installation progresses of bentonite blocks in the tunnel for better modeling results. The evolutions and distributions of relative humidity were well reproduced, but hydraulic model needs to be modified because the re-saturation process was relatively fast near the heaters. In case of stress evolutions due to the thermal and hydraulic expansions, the computed stress was in good agreement with the data. But, the stress is slightly higher than the measured in situ data at the early stage of the operation, because gap between rock mass and bentonite blocks have not been considered in the numerical simulations. The calculated distribution of saturation, water content, and dry density along the radial distance showed good agreement with the observations after the first and final dismantling. The calculated dry density near the center of the FEBEX tunnel and heaters were overestimated compared with the observations. As a result, the saturation and water content were underestimated with the measurements. Therefore, numerical model of permeability is needed to modify for the production of better numerical results. It will be possible to produce the better analysis results and more realistically predict the coupled THM behavior in the bentonite blocks by performing the additional studies and modifying the numerical model based on the results of this study.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.190-200
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• 2001

This paper is addressed to welding defect feature vector selection and implementation method of welding defect classifier using fuzzy techniques. We compare IAV, zero-crossing number as time domain analysis, power spectrum coefficient as frequency domain, histogram as both domain for welding defect feature selection. We choose histogram as feature vector by graph analysis and find out that maximum frequent occurrence number and section of corresponding signal scale in relative histogram show obvious difference between normal welding and voiding with penetration depth defect. We implement a fuzzy welding defect classifier using these feature vector, test it to verify its effectiveness for 695 welding data frame which consist of 4000 sampled data. As result of test, correct classification rate is 92.96%. Lab experimental results show a effectiveness of fuzzy welding defect classifier using relative histogram for practical Laser welding monitoring system in industry.

• Journal of the Korean Magnetics Society
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• v.5 no.1
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• pp.42-47
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• 1995

FeN thin films for inductive recording heads were sputter deposited using RF diode sputtering mehtod from a pure iron target onto 7059 glass substrates, and their magnetic properties were measured. The magnetic properties were greatly affected by film thickness, gas pressure, sputter power and flow ratio of $N_{2}$ to Ar. Single layer FeN films with their thickness varied from $1,000\;{\AA}$ to $6,000\;{\AA}$ were doposited. 800 W sputter power, 3 mT gas pressure, $N_{2}$ to Ar flow ratio of 6.6 : 100 were the sputtering conditions. Up to 7 layers of FeN films having total thickness of $6,000\;{\AA}$ were deposited using $SiO_{2}$ of $30\;{\AA}$ thickness as intermediate layers and their coercivity and saturation magnetization were measured. The sputtering conditions were the same as those in the single layer films. Easy axis coercivity of the single layer FeN films gradually decreased as their thickness was increased, but for the films with their thicknesses above $3,000\;{\AA}$, the coercivity changed very little. As the number of the FeN layers were increased, the coercivity decreased We estimated the grain size of FeN films from the FWHM (Full Width at Half Maximum) of X-ray diffraction peaks. The grain size steadily decreased from about $200\;{\AA}$ to $120\;{\AA}$ as the number of layers were increased. Minimum hard axis coercivity of 0.4 Oe was obtained when the number of layers was four. Maximum relative permeability was 2,900 when the number of layers was three. The cut off frequeocy of the multilayer films were above 100 MHz.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Korean Journal of Cognitive Science
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• v.20 no.2
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• pp.125-154
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• 2009

This paper describes the possibility of human physiological data, especially brain-wave activity, to detect cognitive overload, a phenomenon that may occur while learner uses an e-learning system. If it is found that cognitive overload to be detectable, providing appropriate feedback to learners may be possible. To illustrate the possibility, while engaging in cognitive activities, cognitive load levels were measured by EEG (electroencephalogram) to seek detection of cognitive overload. The task given to learner was a computerized listening and recall test designed to measure working memory capacity, and the test had four progressively increasing degrees of difficulty. Eight male, right-handed, university students were asked to answer 4 sets of tests and each test took from 61 seconds to 198 seconds. A correction ratio was then calculated and EEG results analyzed. The correction ratio of listening and recall tests were 84.5%, 90.6%, 62.5% and 56.3% respectively, and the degree of difficulty had statistical significance. The data highlighted learner cognitive overload on test level of 3 and 4, the higher level tests. Second, the SEF-95% value was greater on test3 and 4 than on tests 1 and 2 indicating that tests 3 and 4 imposed greater cognitive load on participants. Third, the relative power of EEG gamma wave rapidly increased on the 3rd and $4^{th}$ test, and signals from channel F3, F4, C4, F7, and F8 showed statistically significance. These five channels are surrounding the brain's Broca area, and from a brain mapping analysis it was found that F8, right-half of the brain area, was activated relative to the degree of difficulty. Lastly, cross relation analysis showed greater increasing in synchronization at test3 and $4^{th}$ at test1 and 2. From these findings, it is possible to measure brain cognitive load level and cognitive over load via brain activity, which may provide atimely feedback scheme for e-learning systems.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.3-20
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• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.161-167
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• 2017

We have fabricated and characterized $32{\times}32$ photonic quantum ring (PQR) laser arrays uniformly operable with $0.98{\mu}A$ per ring at room temperature. The typical threshold current, threshold current density, and threshold voltage are 20 mA, $0.068A/cm^2$, and 1.38 V. The top surface emitting PQR array contains GaAs multiquantum well active regions and exhibits uniform characteristics for a chip of $1.65{\times}1.65mm^2$. The peak power wavelength is $858.8{\pm}0.35nm$, the relative intensity is $0.3{\pm}0.2$, and the linewidth is $0.2{\pm}0.07nm$. We also report the wavelength division multiplexing system experiment using angle-dependent blue shift characteristics of this laser array. This photonic quantum ring laser has angle-dependent multiple-wavelength radial emission characteristics over about 10 nm tuning range generated from array devices. The array exhibits a free space detection as far as 6 m with a function of the distance.

• Economic and Environmental Geology
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• v.36 no.4
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• pp.305-312
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• 2003

This study was performed to delineate the subsurface geology, geologic structure, and distribution pattern of the Palgongsan granitic body, and to reveal the relationship between the Kyeongsang basin and Yongnam massif by gravity survey. The study area is located between the latitude of 35$^{\circ}$45'-36$^{\circ}$21'N and longitude of 128$^{\circ}$15'-129$^{\circ}$00'E. Total of 966 gravity data measured by Seoul National University, KlGAM(Korea Institute of Geology, Mining ＆ Materials), Pusan National University and Yonsei University were used. The Bouguer gravity anomaly in the study area ranges from -12.88 to 26.01 mgal with a mean value of 11.27 mgal. A very low anomaly zone is located in the Yongnam massif in west of the study area. The anomaly value increases going from west to east. A low anomaly distribution in Palgongsan granite and Yongnam massif is interpreted as the effect of their lower density than that of Kyeongsang Super Group. Power spectrum analysis is applied to evaluate the average depth of basement the Kyeongsang Basin and Conrad discontinuity from gravity anomaly. The average depths of density discontinuities are calculated 10.45 km and 4.9 km, and these are interpreted as Conrad discontinuity and depth of basement of the Kyeongsang Basin, respectively. The depth of Palgongsan granite is derived by means of 2-dimensional modeling and it decreases gradually toward the east. The gravity anomaly east of the study area decreases abruptly due to Shingryeong fault and Nogosan ring fault. Two deepest and sharp roots of Palgongsan granite are recognized by 2-dimensional modeling of each profiles. The depths of those roots are 5.3 km on a profile AA' and 7 km on a profile BB' which is the maximum depth of Palgongsan granite. Small granitic bodies are also seen to be intruded around the Palgongsan granite. The root of Palgongsan granite is shown by 3-dimensional analysis based on the interpolation of 2-dimensional modeling along each profiles to exist in the southwest vicinity of Palgongsan granite. The total volume of Palgongsan granite is approximately 31.211 $Km^3$.