• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1477-1488
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• 2014

Prevalent construction of impermeable pavements in urban areas causes diverse water-related environmental issues, such as lowering ground water levels and shortage of water supply for the living. In order to resolve such problems, a rainwater reservoir can be an effective and useful solution. The rainwater reservoir facilitates the hydrologic cycle in urban areas by temporarily retaining precipitation-runoff within a shallow subsurface layer for later use in a dry season. However, in order to use the stored water of precipitation-runoff, non-point source pollutants mostly retained in initial rainfall should be removed before being stored in the reservoir. Therefore, the purification system to filter out the non-point source pollutants is essential for the rainwater reservoir. The conventional soil filtration technology is well known to be able to capture non-point source pollutants in a economical and efficient way. This study adopted a sand filter layer (SFL) as a non-point source pollutant removal system in the rainwater reservoir, and conducted a series of lab-scale chamber tests and field tests to evaluate the pollutant removal efficiency and applicability of SFL. During the laboratory chamber experiments, three types of SFL with the different grain size characteristics were compared in the chamber with a dimension of $20cm{\times}30cm{\times}60cm$. To evaluate performance of the reservoir systems, the concentration of the polluted water in terms of TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) were measured and compared. In addition, a reduction in hydraulic conductivity of SFL due to pollutant clogging was indirectly estimated. The optimum SFL selected through the laboratory chamber experiments was verified on the in-situ rainwater reservoir for field applicability.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• Journal of Wetlands Research
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• v.18 no.4
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• pp.378-385
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• 2016

This study aims to suggest the framework of functional assessment on lotic area based on HGM(Hydrogeomorphic) approach targeting Wetland Protected Areas which are in the type of river channel, and to set up the fundamental data as a reference wetland. A total of 10 factors in terms of hydrology, biogeochemistry, plant habitat and animal habitat was analyzed based on the original approach of HGM and each Functional Capacity Index(FCI) of those factors was calculated. As the result of the modified FCI analysis, Damyang riverine wetland which is with artificial river bank had high values in the variables of area ratio of actual vegetation in the foreland, the number of plant per area and the area ratio of Salix spp., and those values were highly reflected on the factors of Nutrient Cycling(947,668.00), Species Richness and Maintain Characteristic Plant Communites(6.39) and Maintain Spatial Structure of Habitat(11.00). The Hanbando wetland which is keeping the natural bank had higher values in the variables of structural scale and species diversity, and the those values were highly reflected on the factors of Energy Dissipation(17,805.16), Subsurface Storage of Water(0.54), Removal of Imported Elements and Compounds(103,052.73), Maintain Characteristic Detrital Biomass(2.31), Maintenance of Interspersion and Connectivity (6.50), Species Diversity of Benthic macro-invertebrates(1.60) and Species Diversity of Vertebrate & Species Number of Other Animals(2.52/ 151.50), compared to the Damyang Riverine Wetland.

• Journal of Wetlands Research
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• v.18 no.4
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• pp.481-487
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• 2016

In this research, a pilot scale hybrid rain garden system was developed in order to investigate the efficiency in the different components of the hybrid rain garden system and at the same time evaluate the initial efficiency of the system in treating urban stormwater runoff prior to its actual use in the field. Experimental runs were conducted using synthetic runoff having target concentrations similar to that of the typical runoff characteristics found in different countries and in Korea. With the employment of the hybrid rain garden system, hydrologic improvement was observed as the system demonstrates an approximately 95% reduction in the influent runoff volume with 80% retained in the system, and 15% recharged to groundwater. The reduction was contributed by the retention capabilities of ST and infiltration capabilities in PB and IT. With the combined mechanisms such as filtration-infiltration, biological uptake from plants and soil and phytoremediation that are incorporated in PB and IT, the system effectively reduces the amount of pollutant concentration wherein the initial mean removal efficiency for TSS is 87%, while an approximate mean removal efficiency of 76%, 46% and 56% was observed in terms of organics, nutrients and heavy metal, respectively. With these findings, the research helps in the further improvement, innovation and optimization of rain garden systems and other facilities as well.

• The Korean Journal of Ecology
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• v.28 no.4
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• pp.215-222
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• 2005

MODIS (Moderate Resolution Image Spectrometer) is a core satellite sensor boarded on Terra and Aqua satellite of NASA Earth Observing System since 1999 and 2001, respectively. MODIS LAI, FPAR, and GPP provide useful means to monitor plant phonology and material cycles in terrestrial ecosystems. In this study, LAI, FPAR, and GPP in Korea were evaluated and errors associated with cloud contamination on MODIS pixels were eliminated for years $2001\sim2003$. Three-year means of cloud-corrected annual GPP were 1836, 1369, and 1460g C $m^{-2}y^{-1}$ for evergreen needleleaf forest, deciduous broadleaf forest, and mixed forest, respectively. The cloud-originated errors were 8.5%, 13.1%, and 8.4% for FPAR, LAI, and GPP, respectively. Summertime errors from June to September explained by 78% of the annual accumulative errors in GPP. This study indicates that cloud-originated errors should be mitigated for practical use of MODIS vegetation products to monitor seasonal and annual changes in plant phonology and vegetation production in Korea.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• Korean Journal of Microbiology
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• v.43 no.1
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• pp.23-30
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• 2007

The most rapid Real-time PCR based detection method for Avian influenza A virus (AIV) subtype H5N1 was developed. The target DNA sequence in this study was deduced from H5N1 subtype-specific 387 bp partial gene of hemagglutinin, and was synthesized by using PCR-based gene synthesis on the ground of safety. Real-Time PCR was performed by $GenSpector^{TM}$ using microchip-based, total $1{\mu}l$ of reaction mixture with extremely short time in each steps in PCR. The detection including PCR-amplication and analysis of melting temperature was totally completed within 13 min. The H5N1-specific 189 bp PCR product was correctly amplified until 2.4 molecules of hemagglutinin gene as minimum of templates. This kind of PCR was designated as Quick Real-Time PCR in this study and it could be applied to detect not only AIV H5N1, but also other pathogens using PCR-based detection.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.4
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• pp.281-288
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• 2011

Recently, researches that a variety of contaminants in water are removed by sonolysis technology with oxidation and pyrolysis process from cavitation were conducted. However, there are few studies for sonochemical treatment by a pilot-scale ultrasound system. This research focused on developing pilot-scale ultrasound systems, which could be an continuously effective treatment for a large volumes of contaminants, and demonstrating the feasibility of utilizing these systems to remove naphthalene from groundwater. V-120 type reactor was found to be 1.4~2.2 times higher effective than the normal type. A total of three different pilot scale's systems consisted of installing effluent and irrigation water in order to be a continuos system, including supplemental additives, and applying a V-120 type reactor and a external cooling cycle system. Naphthalene levels treated by three systems were lower than a recommended guideline of naphthalene for drinking water in EPA. Especially, the naphthalene removal efficiencies of PS1 and PS2 systems were over 97%. The pilot-scale continuous ultrasound clean-up system delivered over 84~95% naphthalene removal efficiency for treatment of 10~20 liter of groundwater. In addition, the ultrasound system could be successfully applied to the conditions of artificial and genuine groundwater contaminated with naphthalene.

• Current Research on Agriculture and Life Sciences
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• v.15
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• pp.69-81
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• 1997

For the prevention of worldwide prevalent disease of nuclear polyhedrosis virus (NPV), environmental conditions and their incidence of grasserie was investigated through 57 cases of silkworm rearing from the year of 1979 to 1993 in the countries of Korea, Japan, and Philippines. Relationship between the occurrence of NPV and environmental factors were also analysed from the aspect of causal pathogenesis. Unfavorable foactors related to the prevalence of NPV disease was reconfirmed by the assay of experimental rearing. Silkworms reared on mulberry leaves or artificial diet appeared similar result on the occurrence of grasserie. Disinfection by formalin and simple sweeping or washing was not significantly different on the occurrence of NPV disease. Following insufficient ventilation on the younger larvae. from the 1st to 3rd instar, the disease by NPV at the later stage was remarkably emphasized those insidence. An experimental rearing from 1993 to 1996 demonstrated the prevention of NPV disease by simple cleaning of sweeping under the condition of air forced ventilation, the customal practice of disinfection with formalin or any other chemical agents could be omissible.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2179-2183
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• 2008

지금까지 분포형 모형 개발에 대한 많은 노력이 있음에도 불구하고 여러 제약사항들에 의해 잠재력을 보여주는 정도로 활용되어 왔으나, 최근 급속도로 발전하는 컴퓨터의 계산능력, DEM 등 디지털정보의 구축이 진행되어 오고 있고, GIS 및 인공위성 영상기법의 발달로 공간적인 비균질성을 고려하여 유출과정에서 운동역학적인 이론을 기반으로 물의 흐름을 수리학적으로 추적해 나가는 물리적기반의 분포형 유출모형의 활용도가 높아지고 있다. 본 모형개발에 있어 이론적 배경이 된 모형은 1998년부터 일본 교토대학 방재연구소 코지리 연구실에서 개발 중인 Hydro-BEAM으로 유역 물순환의 건전성을 평가하기 위하여 장기간의 유역 내 유량, 수질을 시계열 및 공간적으로 파악하여 유역의 영향평가를 위해 개발된 물리적 기반의 격자구조를 가진 분포형 장기유출 모형이다. 유출량 계산은 유역내 수평 유출량산정 모듈로서 평면 분포형의 격자형을, 연직 분포형으로는 $A{\sim}B$층의 수평유출량은 하천으로 유입하고, C층은 하천유량에 영향을 미치지 않는 지하수층으로 가정하는 다층모형을 이용해서 A층, 지표 및 하도흐름은 운동파 법(kinematic wave)으로, $B{\sim}C$층의 유출량은 선형저류법으로 계산하는 모형이다. 본 연구에서는 격자흐름방향을 4방향에서 8방향으로 개선하였고, 모형의 각종 수문매개변수들을 GIS와 연계하여 직접 입력할 수 있도록 하였으며, 물리적기반의 침투과정을 모의할 수 있도록 Green & Ampt모듈을 추가하고, 향후 레이더 강우 및 수치예보강우의 홍수유출예측을 염두에 두고 격자강우량을 활용할 수 있도록 하는 등 홍수유출해석을 위한 분포형 강우-유출모형으로 개선 하였고, 이를 남강댐유역에 적용해 봄으로써 모형의 적용성을 검토해 보고자 하였다. 홍수기동안의 지표흐름과 지표하 흐름의 시간적 변화와 공간적 분포를 모의할 수 있었으며, 전처리과정으로서 ArcGIS 혹은 ArcView등의 GIS 프로그램을 이용하여 모형에 필요한 ASCII형태의 입력 매개 변수 자료들을 가공하였다. 또한 후처리과정으로서 모형의 수행결과인 유역내의 유출량 분포 등을 GIS상에서 나타낼 수 있도록 ASCII형태로 출력하도록 구성하였다. 남강댐유역을 대상으로 유역을 500m의 정방형 격자로 분할하고 수계망을 통하여 유역 출구까지 운동파이론에 의해 추적 계산하였으며, 수문곡선 비교결과 재현성 높은 결과를 보여주었다. 모형의 정확성 및 실용성에 대한 보다 정확한 평가를 위해서는 향후 다양한 강우 사상 혹은 다양한 크기의 유역에 대한 유출량의 재현성 및 매개변수 등에 검증이 이루어져야 할 것이다.