• Journal of Industrial Convergence
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• v.22 no.1
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• pp.127-135
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• 2024

This study used a convenience sampling method to find out the effect of healthy lifestyle habits of college students participating in leisure activities on sleep quality. The results are as follows. First, in majors according to the personal characteristics of college students, the arts and sports field showed higher exercise lifestyle habits than the natural field, and in leisure participation, college students participating in the sports field showed an effect on exercise lifestyle, health perception habits, eating habits, and subjective sleep. In the leisure activity period, it was found that students who participated in leisure activities for more than 3 years had an effect on exercise lifestyle, health perception habits, and daytime sleep. In leisure activity time, it was found that students who participated for 1-2 hours had an effect on exercise lifestyle, health perception habits, and eating habits. Second, it was found that bad lifestyle habits had an effect on sleep difficulties, and in daytime sleep, exercise lifestyle habits, bad lifestyle habits, and eating habits were found to have an effect.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.434-445
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• 2020

In this study, the minimization of harbor oscillation using permeable breakwater was applied to the actual harbor and investigated an effect of minimization by computer simulation in order to take into account the water quality problems and measures of harbor oscillation by infragravity waves at the same time. The study site is Mukho harbor located at East coast of Korea that harbor oscillation has been occurred frequently. The infragravity waves obtained by analyzing the observed field data for five years focused on the distribution between wave periods of 40 s and 70 s and wave heights in less than 0.1 m was 94% of analyzing data. The target wave periods was 68.0 s. The most effective method of minimization of harbor oscillation by infragravity waves was to install a detached permeable breakwater with transmission coefficient of 0.3 on the outside harbor and replace some area of the vertical wall in the harbor with wave energy dissipating structure to achieve a reflectivity of 0.9 or less. The amplitude reduction rate of this method shown in 27.4%. And the effect of the difference in transmission coefficient of permeable breakwater on the reduction rate of the amplitude was not significant.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.664-668
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• 2008

본 연구에서는 비정상상태 자유수면 모의를 위해 2차원 유한체적법을 이용한 수치모형을 개발하였으며, 이론적인 해석해 및 수리실험을 통한 실측자료를 이용하여 검증하였다. 개발된 모형은 지배방정식으로 비선형 및 보존형 2차원 천수방정식(shallow water equation)을 이용하였으며, 동적메모리 할당 기능이 포함된 Fortran-90으로 코딩되었다. 또한 구조화된 격자 및 비구조화 격자 시스템에도 적용될 수 있도록 모형을 구성하였으며, 불규칙한 하상지형에 의해 수치진동을 감소시키기 위해 본 모형에 well-balanced HLLC 기법을 적용하였다. 모형의 적용성을 검증하기 위하여 1차원의 경우 젖은/마른 하상 조건하에서의 댐 붕괴파 문제와 하상이 변화하는 지형 구간을 통과할 때 발생되는 천이류에 대한 문제 그리고 시간에 따라 변화하는 수위와 지형 조건에서의 wetting & drying에 대한 문제에 적용하였으며, 2차원의 경우 전통적인 댐 붕괴파 문제 및 구조물에 미치는 댐 붕괴파의 영향에 대한 수리모형실험을 통한 실측자료를 이용하여 검증하였다. 검증결과 본 모형을 통해 계산된 수치해는 이론적인 해석해와 실측자료에 거의 정확히 일치하였으며, 향후 실제 하천 자료를 이용하여 모형의 현장 적용성을 검증할 것이다.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.83-90
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• 2018

Recently, many people have suffered from insomnia, labor loss, cognitive decline, and mental illness. The solution to this problem is almost entirely cognitive therapy or medication, but it is not recommended in the long term due to side effects and dependency problems. Therefore, in this paper, we propose a neuro feedback system based on portable EEG that helps induce sleeping. We design and evaluate the EEG classifier, which is the most important function to implement the system, and propose an optimized classifier modeling method for various factors that can affect performance. When using the proposed classifier, we could distinguish 97.9% of awakening and sleep phase in portable EEG.

• Korean Journal of Childcare and Education
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• v.11 no.6
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• pp.153-175
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• 2015

The purpose of this study was to find out the current status of smart phone usage, differences of developmental characteristics and time diaries between high- and low- usage groups. The study also tried predicting variables among children's personal and family environment factors in order to classify four year old children in the fifth year Panel Study on Korean Children into the two groups. Data was collected from 435 smart phone users and 157 of them who had started to use a phone before age 3.Frequency analysis, t-test and logistic regression were performed. The results showed that most 4 year old children started to use a phone in their fourth year and used it frequently. The low level groups of girls and all participants showed lower levels of behavior problems, and the low level group of all participants had a longer sleep times than the high level group had. Children's activity and social temperament were identified as predicting variables for classifying children into the two groups. The results suggested the necessity of measures preventing children from excessive smart phone usage in their early years and to consider children's temperament first.

• Sleep Medicine and Psychophysiology
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• v.11 no.1
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• pp.22-28
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• 2004

Objective: The purpose of this study is to investigate the prevalence rate of OSA in subjects whose main sleep complaint is insomnia and to find differential factors of OSA in these insomniac subjects. Method: We reviewed the medical records and polysomnographic findings of patients referred to the Sleep Laboratory at Seoul National University Hospital from January 1996 to December 2002. Four-hundred and seventy subjects complained of insomnia as their main sleep problem (235 males and 235 females, mean age $53.6{\pm}12.4\;years$). First, we investigated the prevalence rate of OSA in these insomniac patients. Second, we compared the clinical and demographic characteristics of the OSA-associated group with those of the non-associated group. Third, we examined whether the degree or presence of differential factors within the OSA group correlate with severity of OSA, as determined by the respiratory disturbance index (RDI). Results: Among 470 insomniac subjects, 125 subjects (26.6%) were diagnosed as OSA by nocturnal polysomnography. OSA-associated subjects were significantly older ($58.4{\pm}12.3\;years$ vs. $51.8{\pm}11.2\;years$, p<0.01), and had significantly higher body mass index (BMI) ($23.4{\pm}3.3\;kg/m^2$ vs. $22.5{\pm}3.1\;kg/m^2$, p=0.44) than non-associated subjects. The OSA-associated group had more subjects with male gender (64.0% vs. 44.9%, p<0.01), hypertension (20.0% vs. 9.3%, p<0.01) or snoring (96.0% vs. 63.5%, p<0.01). Within the OSA-associated group, age had a significant positive correlation with RDI (p=0.01). Conclusion: We found that a considerable portion of patients complaining of insomnia as their main sleep problem were diagnosed as OSA. Snoring, old age, male gender, obesity, and comorbid hypertension were found to be differential factors of OSA in insomniac patients. We suggest that diagnostic efforts including nocturnal polysomnography are needed for insomniac patients with any of the above risk factors of OSA.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2000.06a
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• pp.241-246
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• 2000

최근 들어 집중호우 및 게릴라성 호우 등 이상강우로부터 야기되는 급격한 하천유량의 증가는 교량에서의 세굴에 대한 안정성 여부에 큰 문제를 야기하고 있다. 홍수시에는 유량이 교량의 상관 위로 월류하게 되는 경우가 생기게 되며, 수심의 증가에 따라 상판의 일부분 또는 전체가 물이 잠기게 된다. 이때의 흐름은 압력흐름 상태를 가지게 되어 세굴의 발달을 더욱 크게 만들게 된다. 흥수시 압력흐름에 대한 위험은 일반 흐름에서보다 훨씬 크며 100년 빈도의 홍수사상일 지라도 그 효과는 500년 빈도의 홍수사상보다 더욱 크게 될 것이다. 기존의 세굴에 대한 연구의 대부분은 자유수면을 기초로 하고 있으며 이에 따라 많은 세굴방정식들도 자유수면에 대해서만 이루어지고 있다. (중략)

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.434-439
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• 2017

More and more people are suffering from sleep disturbance, which can have many different causes. The healthcare industry, which can help people with this disability, is one technology that is currently in the spotlight. However, current services are vulnerable to data concentration, because they are simple telemedicine services that transmit all data to a remote server and process the data on the server. They have a disadvantage in that the data cannot be streamed in real time by synchronizing the biometric data of remotely protected persons. In order to solve this problem, we propose a service structure for streaming biometric data of protected persons to a hospital or guardian in real time, using a self-organizing distributed middleware platform without a central server. We prove that it is possible to provide an effective streaming service by evaluating the service start time and average delay time.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.105-110
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• 2019

People sleep about one-third of their lives and their sleep time varies according to age. Adult usually sleep 8 hours a day. However, that dose not guarantee good sleep. The cause of this is due to sleep disorders like snoring and sleep apnea. In this paper, the smart pillow for detecting snoring among sleep disorders is investigated. This pillow consists of two microphones located on the left and right side of the pillow. For simple detecting, the snoring signal was converted into the pulse using a peak detection circuit. The decision of the snoring occurrence was by pulse duration. The accuracy of the snoring detection was about 97%. The research results show that the smart pillow can be use to detect the snoring during sleeping.