• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.561-566
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• 2021

Schools are highly feared to spread widely in the event of an infectious disease, and systematic management and prompt response are needed as it can undermine students' health and learning rights. This study was conducted to identify the current status of infectious diseases common to elementary, middle and high school students and to provide basic data to protect students and faculty from the threat of infectious diseases and maintain normal school functions. Sejong City was selected for investigation. The three major infectious diseases are influenza, chickenpox and aquarium, all of which are classified as acute viral infectious diseases and have fast propagation speed and strong propagation power, which can have fatal consequences for students living in groups. The research data were analyzed using the 2019 infectious disease report data from the Education Ministry's Education Administration Information Network (NEIS), and the current status data reported by elementary, middle and high schools nationwide were analyzed. The research method was to compare the current status of infectious diseases across the country and Sejong City, compare the status of issuance by each school level, compare the status of infectious diseases by item, and analyze the status of infectious diseases by time. The results of the survey on the status of the three major infectious diseases are expected to be used as basic data for managing infectious diseases not only in Sejong City but also in the nation, so that they can be used to establish measures to manage student infectious diseases in the future.

• 한국체육학회지인문사회과학편
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• v.51 no.5
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• pp.639-647
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• 2012

The purpose of this study was to examine the effects of varied resistance training intensities and rest intervals between training sets on integral electromyography (iEMG), repetition rate, and total work. All subjects, 14 college students, were tested one repetition maximum (1RM). Then, all subjects were weekly tested with 9 practice procedures, composed of diverse intensities (60, 75, 90% of 1RM) and rest intervals (1, 3, 5 min). As results show, to maintain the same load and target repetition maximum for an untrained person, muscular power training (90% of 1RM), muscular hypertrophy training (75% of 1RM), and muscular endurance training (60% of 1RM) should be applied with 5 min or longer rest interval periods for 3 training sets. In addition, 2 training sets with 3 min rest intervals and a set with an 1 min rest interval were capable by the subjects. Thus, at least 3 min or longer rest intervals should be applied to maintain multiple training sets. In case for muscular endurance training, which requires shorter rest intervals, the intensity of exercise should be adjusted to 60% of 1RM or less. In conclusion, depending on diverse purposes of resistance training such as improving muscular power, muscular hypertrophy, or muscular endurance, appropriate exercise intensity and rest intervals should be applied.

• Analytical Science and Technology
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• v.21 no.2
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• pp.93-101
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• 2008

The application of solid phase microextraction (SPME) is generally conducted by directly immersing the fiber into the liquid sample or by exposing the fiber in the head space (HS). The extraction temperature, the time of incubation, and application of stirring are often designated to be the most important parameters for achieving the best extraction efficiencies of HS-SPME analysis. In this study, relative importance of these three analytical parameters involved in the HS-SPME method is evaluated using a polydimethylsiloxane/carboxen (PDMS/CAR) fiber. To optimize its operation conditions the competing relationships between different parameters were investigated by comparing the extraction efficiency based on the combination of three parameters and two contracting conditions: (1) heating the sample at 30 vs. 50 C, (2) exposing samples at two durations of 10 vs. 30 min, and (3) application of stirring vs. no stirring. According to our analysis among 8 combination types of HS-SPME method, an extraction condition termed as S50-30 condition ((1) 1200 rpm stirring, (2) $50^{\circ}C$ exposure temp, and (3) 30 min exposure duration) showed maximum recovery rate of 45.5~68.5% relative to an arbitrary reference of direct GC injection. According to this study, the employment of stirring is the most crucial factor to improve extraction efficiency in the application of HS-SPME.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.40-40
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• 2017

최근 2014년 마른장마의 영향으로 중부 지방에 가뭄이 발생하였으며, 장마철 강수부족은 2015년까지 영향을 미친바 있다. 이로 인해 소양강 댐은 역대 최저수위를 기록하였으며, 일부 지역에서는 제한급수, 농업용수 부족 등의 피해가 발생하였다. 일반적으로 가뭄은 발생순서에 따라 기상학적, 농업적, 수문학적 가뭄 등으로 분류하고 있다 (Wilhite and Grantz, 1985). 기상학적 가뭄은 농업 및 수문학적 가뭄에 영향을 미치는 가뭄의 시작 단계를 의미하며, 가뭄을 판단하는데 있어 중요한 요소라 할 수 있다. 기상학적 가뭄을 정량적으로 판단하기 위해 SPI, PDSI, PN 등이 활용되고 있으며, 특히 강수량 기반의 SPI는 계산과정이 쉽고, 다양한 지속시간(3, 6, 9, 12개월 등)에 따라 가뭄을 객관적으로 판단할 수 있어 가장 활발하게 이용되고 있다(Mckee et al., 1993). 최근 기상청은 대기와 해양-해빙 모델을 접합한 GloSea5의 장기예보자료를 활용하여 월 내지 계절 가뭄전망을 위한 기상학적 가뭄지수를 현업에 활용하고 있다. 다만 국내에서는 주로 단기가뭄(1~3개월)이 빈번하게 발생함에 따라 짧은 예보선행시간을 갖는 가뭄전망에 대한 평가에 집중되어 왔다. 2014, 15년에는 이례적으로 2년 연속 가뭄이 지속된바 있으며, 장기가뭄(3개월 이상)에 대한 전망정보의 필요성이 증가하고 있다. 본 연구에서는 장기예보자료 기반의 기상학적 가뭄전망정보를 산정하고, 2015년 가뭄을 대상으로 활용성을 평가하였다. 이를 위해 ASOS 59개 지점의 관측강수량, GloSea5의 미래예측(Foreacst) 및 과거재현(Hindcast) 자료를 활용하였으며, 다양한 지속시간(3, 6, 9, 12개월)에 대한 SPI를 산정하였다. 또한 예보선행시간(1~6개월)에 따른 SPI와 관측자료 기반의 SPI 간의 통계적 분석(상관계수, 평균제곱근오차)을 수행하여 전망정보의 정확도를 평가하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.392-392
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• 2022

세계적 규모의 팬데믹 감염병의 출현은 전 세계적으로 경제적, 문화적, 사회적 파급효과가 매우 강력하며 전 인류를 위협하고 있다. 최근에 발병한 중증급성 호흡기질환 코로나바이러스 2(Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2)는 2019년 12월 중국 우한에서 첫 보고 되었고 2022년 현재까지 종식되지 않고 있으며 바이러스의 전파력과 치명률이 높고 무증상 감염상태일 때에도 전염이 가능하여 현재 역학조사의 사후적 대응에 대한 한계가 있어 선제적 대응을 위한 수단이 필수 불가결해지고 있는 실정이다. 하수기반역학(Waste Based Epidemiology, WBE)이란 하수처리장으로 유입되기 전의 하수를 분석하여 하수 집수구역 내 도시민의 생활상을 예측하는 것으로 하수로 배출된 감염자의 분비물 및 배설물 속 바이러스를 하수관로에서 신속하게 검출함으로써 특정지역의 감염성 질환 전파 정도와 유행하는 타입(변이)등을 분석하고 기존 역학조사의 문제점을 극복할 수 있으며 선제적인 대응이 가능하다. 현재 COVID-19의 대유행과 관련하여 WBE를 기반으로 한 다양한 연구가 진행되고 있으며 실제 환자의 발생과 상관관계가 있음이 확인되고 있고 백신 접종과 새롭게 발생한 변이바이러스의 관계 속에서 발생하는 변수를 고려한 모델이 없다는 점을 들어 새로운 감염병 확산 예측 모델에 대한 필요성 또한 커지고 있다. 본 연구에서는 병원에서부터 하수처리장까지의 하수관거와 하수처리장에서의 SARS-CoV-2 검출농도 및 거동을 파악하는 것을 목적으로 하고 있으며 COVID-19의 감염규모 확산에 관한 방법론에서 수학적모델 (Euler Method, RK4 Method, Gillespie Algorithm)과 딥러닝 기반의 Nowcasting model과 Fore casting model을 살펴보고자 한다.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.209-221
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• 2015

Temperature is one of the most important factors affecting crop growth. The diurnal cycle of the scale factor [Tsc] for air temperature and the surface temperature of crop leaf and soil could be estimated by the following equation : $[Tsc]=0.5{\times}sin(X+C)+0.5$. The daily air temperature (E[Ti]) according to the E&E time [X] can be estimated by following equation using average (Tavg), maximum (Tm) and minimum (Tn) temperature : $E[Ti]=Tn+(Tm-Tn){\times}[0.5{\times}sin\;\{X+(9.646Tavg+703.65)\}+0.5]$. The crop leaf temperature in 24th June 2014 was high as the order of red pepper without mulching > red pepper with mulching > soybean under drought > soybean with irrigation > Chinese cabbage. The case in estimating crop leaf surface temperature using air temperature and soil surface temperature was lower in the deviation compared to the case using air temperature for Chinese cabbage and red pepper. These results can be utilized for the crop models as input data with estimation.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.123-136
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• 2021

The Palace Museum Cultural Creative Products on Attitude and Purchasing Intention. Online surveys were conducted on consumers who had consumed Cultural and Creative Products of the Palace Museum in the past. There were 305 valid questionnaires empirical survey was analyzed. The results of the study are as follows. First, it was found that all three variables of Consumer Perceived Value had a significant positive effect on product Attitude. Second, among the three variables of Consumer Perceived Value, the Cultural Educational Value did not significantly affect the onsumer urchasing ntention, but other variables had a significant positive effect on the Consumer Purchasing Intention. Third, it was verified that Attitude has a positive effect on Purchasing Intention. Fourth, it was found that there was a partial mediating effect of the Attitude between the Perceived Value and the Consumer Purchasing Intention. According to the research results, while maintaining the cultural identity of cultural and creative products, it is necessary to adopt reasonable methods to maximize the sense of enjoyment and comfort, enriching the daily use functions and rationalizing the price standards.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.62-69
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• 2023

This study investigated the electromechanical properties of cement based smart repair materials (SRMs) according to the different amounts of fine steel slag aggregates (FSSAs). SRMs can self-diagnose the quality of repairing and self-sense the damage of repaired zone. The replacement ratios of FSSAs to sand for SRMs were 0% (FSSA00), 25% (FSSA25), and 50% (FSSA50) by sand weight. The electrical resistivity of SRMs generally decreased as the compressive stress of SRMs increased: the electrical resistivity of FSSA25 at the age of 7 hours decreased from 78.16 to 63.68 kΩ-cm as the compressive stress increased from 0 to 22.37 MPa. As the replacement ratio of FSSAs by weight of sand increased from 0% to 25%, the stress sensitivity coefficient (SSC) of SRM at the age of 7 h increased from 0.471 to 0.828 %/MPa owing to the increased number of partially conductive paths in the SRMs. However, as the replacement ratio of FSSAs further increased up to 50%, the SSC decreased from 0.828 to 0.649 %/MPa because some of the partially conductive paths changed to continued conductive ones. SRMs are expected to self-sense the quality and future damage of repaired zone only by measuring the electrical resistivity of the repaired zone in addition to fast recovery in the mechanical resistance of structures.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.