• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.489-499
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• 2020

Effective science-based management of the basin water resources requires an understanding of the characteristics of the streams, such as the baseflow discharge. In this study, the base flow was estimated in the two watersheds with the least artificial factors among the Nakdong River watersheds, as determined using the chemical hydrograph separation technique. The 16-year (2004-2019) discontinuous observed stream flow and electrical conductivity data in the Total Maximum Daily Load (TMDL) monitoring network were extended to continuous daily data using the TANK model and the 7-parameter log-linear model combined with the minimum variance unbiased estimator. The annual base flows at the upper Namgang Dam basin and the upper Nakdong River basin were both analyzed to be about 56% of the total annual flow. The monthly base flow ratio showed a high monthly deviation, as it was found to be higher than 0.9 in the dry season and about 0.46 in the rainy season. This is in line with the prevailing common sense notion that in winter, most of the stream flow is base flow, due to the characteristics of the dry season winter in Korea. It is expected that the chemical-based hydrological separation technique involving TANK and the 7-parameter log-linear models used in this study can help quantify the base flow required for systematic watershed water environment management.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.525-532
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• 2021

In the ocean, it is difficult to separate the effects of one cause due to the multiple causes, but the self-organizing map can be analyzed by adding other factors to the cluster result. Therefore, in this study, the results of the clustering of sea level data were applied to sea surface temperature. Sea level data was clustered into a total of 6 nodes. The difference between sea surface temperature and sea level height has a one-month delay, which applied sea surface temperature data a month ago to the clustered results. As a result of comparing the mean of sea surface temperature of 140 to 150°E, where the sea surface temperature was variously distributed, in the case of nodes 1, 3, and 5, it was possible to find a meandering sea surface temperature distribution that is clearly distinguished from the sea level data. While nodes 2, 4 and 6, the sea surface temperature distribution was smooth. In this study, sea surface temperature data were applied to the clustered results of sea level data, but later it is necessary to apply wind or geostrophic velocity data to compare.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1062_1063
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• 2009

청정에너지로 각광받고 있는 태양광발전은 지구환경문제가 작금의 관심사로 떠오르면서 더욱더 보급이 확산되고 있다. 정부에서도 태양광발전 사업자의 지원정책과 일반보급사업, 지방보급사업 및 100만호 보급사업 등의 강력한 정책적 지원으로 보급의 활성화를 위한 기반을 마련하게 되었다. 미래의 지구환경과 청정에너지에 대한 국민들의 관심 고조 등은 정부정책과 맞물려 전력계통에 대규모 대용량의 발전시스템과 다수의 시스템이 도입되면서 전력계통 운영에 작은 변화를 보여 주고 있다. 본 고에서는 태양광발전의 계통도입에 대한 검토사항과 확대 도입에 의한 영향에 대하여 고찰하고자 한다.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• The Journal of Engineering Geology
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• v.34 no.3
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• pp.429-445
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• 2024

The Journal of Engineering Geology (JEG), a leading academic journal in the field of engineering geology in South Korea, was first published in 1991 and has since been publishing academic papers and research findings. While several literature reviews have been undertaken on specific research areas in recent decades, comprehensive reviews focusing on JEG have been relatively limited. To address this gap, this study applied the latent Dirichlet allocation (LDA) model to analyze the main research topics and trends, and undertook network analysis to identify relationships between topics over different periods. Results for the LDA indicate seven key research topics categorized into three trends: Classic, Emerging and Stable topics. Classic topics include 'Geophysics' and 'Structural geology', which were major subjects in the early years, with their focus shifting to other areas over time. Emerging topics such as 'Hydrogeology' and 'Geohazards' have gained prominence in recent years. Stable topics including 'Geotechnical structures', 'Geomechanics', and 'Environmental geology' have maintained consistent research interest. Network analysis revealed that Structural geology was the central topic prior to 2008, while Geotechnical structures became the focal point of research after 2008, with a shift in research focus. The results of this study contribute to our understanding of research trends and the development of JEG, providing insights for the setting of future research directions.

• Journal of Power System Engineering
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• v.10 no.2
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• pp.75-82
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• 2006

세계적인 환경 보호 정책에 따라 할로겐화탄소 냉매를 대체할 환경 친화적인 초저온 냉매의 개발과 연구가 활발히 진행되고 있다. 일반적으로 캐스캐이드 2원 냉동 시스템에서 아직까지 할로겐화탄소 냉매가 널리 사용되고 있다. 탄화수소 화합물의 한 종류인 에탄은 낮은 지구 온난화 지수와 낮은 오존층 파괴지수를 가진 친 환경적인 자연 냉매이다. 본 연구는 지구 온난화 지수가 높은 R-23 냉매와 비교하여 캐스캐이드 2원 냉동 시스템에서 에탄의 성능 시험을 목적으로 수행 하였다. 1원측에는 R-22를 사용하였으며, 증발 온도에 따른 성능은 R-23 보다 에탄(R-170)이 더 높게 나타났다.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.36-45
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• 2015

It is important subject of research in engineering and natural science field that creating continuing high-definition image from very large volume data. The necessity of software that helps analyze useful information in data has improved by effectively showing visual image information of high resolution data with visualization technique. In this paper, we designed multi-platform visualization system based on client-server to analyze and express earth environment data effectively constructed with observation and prediction. The visualization server comprised of cluster transfers data to clients through parallel/distributed computing, and the client is developed to be operated in various platform and visualize data. In addition, we aim user-friendly program through multi-touch, sensor and have made realistic simulation image with image-based lighting technique.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.