• Journal of the Korean Geophysical Society
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• v.9 no.2
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• pp.121-128
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• 2006

Recently, spillways are need to control stable water level for supporting main dams because of floods by unusual change of weather such as Typhoon Rusa. This study has been focused on the amount of leakage through the rock mass distributed fractures and joints under the opened emergency spillway. It is very important to evaluate the amount of leakage as these affect stability of spillway by interaction between effective stress and pore pressure. The commercial program MAFIC has been used for analyzing groundwater flow in fractured rock mass. The results showed that the values of range, average and deviation of leakage were 2.85∼ 3.79×10-1, 3.32×10-1 and 1.70×10-2 m3/day/m2 respectively. Secondary, we have estimated the effect of grouting after the transmissivity(Tf) of joint 1 as main pathway of leakage known from above results was changed from 1.78×10-7 to 1.59×10-9 m2/s. The results showed that the values of range, average and deviation of leakage were 7.80×10-4∼1.53×10-3, 1.18×10-3 and 1.32×10-4 m3/day/m2 respectively. As the result, the amount of leakage after grouting has been decreased by a ratio of 1 to 277.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.701-707
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• 2021

In this study, we examined the sea surface temperature (SST), air temperature (AT), and their time lag in response to an extreme cold wave in 2018 and a weak cold wave in 2019, cross-correlating these to the northern wind direction frequency. The data used in this study include SST observations of seven ocean buoys Real-time Information System for Aquaculture Environment provided by the National Institute of Fisheries Science and automatic weather station AT near them recorded every hour; null data was interpolated. A finite impulse response filter was used to identify the appropriate data period. In the extreme cold wave in 2018, the seven locations indicated low SST caused by moving cold air through the northern wind direction. A warm cold wave in 2019, the locations showed that the AT data was similar to the normal AT data, but the SST data did not change notably. During the extreme cold wave of 2018, data showed a high correlation coefficient of about 0.7 and a time lag of about 14 hours between AT and SST; during the weak cold wave of 2019, the correlation coefficient was 0.44-0.67 and time lag about 20 hours between AT and SST. This research will contribute to rapid response to such climate phenomena while minimizing aquaculture damage.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.1-11
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• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.764-771
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• 2018

Domestic and foreign agricultural environments nowadays are undergoing various changes such as aging of agricultural population, increase of earned population, rapid climate change, diversification of agricultural product distribution structure, depletion of water resources and limited cultivation area. In order to respond to various environmental changes in recent agriculture, practical use of Smart Greenhouse to easily record, store and manage crop production information such as crop growing information, growth environment and agriculture work log, Interest is growing. In this paper, we propose a system that collects the situation information necessary for growth such as temperature, humidity, solar radiation, CO2 concentration, and monitor the collected data, which can be measured in the rhizosphere of the crop. We have developed a system that collects data such as temperature, humidity, radiation, and growth environment data, which are measured by data obtained from the rhizosphere measuring section of a growing crop and measured by a sensor, and transmitted to a wireless communication gateway of 400 MHz. We developed the integrated SW that can monitor the rhythm environment data and visualize the data by using cloud based data. We can monitor by graph format and data format for visualization of data. The existing smart farm managed crops and facilities using only the data within the farm, and this study suggested the most efficient growth environment by collecting and analyzing the weather and growth environment of the farms nationwide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.172-178
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• 2019

Although the needs for providing resilient PNT information are increasing, threats due to the intentional RFI or space weather change are challenging to resolve. eLoran, which is a terrestrial navigation system that use a high-power signal is considered as a best back-up navigation system. Depending on the user's environment in the eLoran system, the user may use one of E-field or H-field antennas. H-field antenna, which has no restriction on setting stable ground and is relatively resistant to noise of general electronic equipment, is composed of two loops, and shows anisotropic gain pattern due to the different measurement at the two loops. Therefore, the H-field antenna's phase estimation value of signal varies depending on its direction even at the static environment. The error due to the direction of the signal should be eliminated if the user want to estimate the own position more precisely. In this paper, a method to compensate the error according to the geometric distribution between the H-field antenna and the transmitting station is proposed. A model was developed to compensate the directional error of H-field antenna based on the signal generated from the eLoran signal simulator. The model is then used to the survey measurement performed in the land area and verify its performance.

• Journal of Environmental Impact Assessment
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• v.28 no.1
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• pp.23-34
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• 2019

Global warming becomes a serious issue that poses subsidiary issues like a sea level rise or a capricious climate over the world. Because of severe heat-wave of the summer in Korea in 2016, a big attention has been focused on urban heat island since then. Not just about heat-wave itself, many researches have been concentrated on how to adapt in this trendy warming climate and weather in a small scope. A big part of existing studies is mitigating "Urban Heat Island effect" and that is because of huge impervious surface in urban area where highly populated areas do diverse activities. It is a serious problem that this thermal context has a high possibility causing mortality by heat vulnerability. However, there have been many articles of a green infrastructures' cooling impact in summer. This research pays attention to measure cooling effect of a street planting considering urban canyon and type of green infrastructures in neighborhood scale. This quantitative approach was proceeded by ENVI-met simulation with a spatial scope of a commercial block in Seoul, Korea. We found the dense double-row planting is more sensitive to change in temperature than that of the single-row. Among the double-row planting scenarios, shrubs which have narrow space between the plant and the land surface were found to store heat inside during the daytime and prevent emitting heat so as to have a higher temperature at night. The quantifying an amount of vegetated spaces' cooling effect research is expected to contribute to a study of the cost and benefit for the planting scenarios' assessment in the future.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.483-489
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• 2019

True color images display colors similar to natural colors. This has the advantage that it is possible to monitor rapidly the complex earth atmosphere phenomenon and the change of the surface type. Currently, various organizations are producing true color images. In Korea, it is necessary to produce true color images by replacing generations with next generation weather satellites. Therefore, in this study, visual enhancement for true color image production was performed using Top of Atmosphere (TOA) data of Advanced Himawari Imager (AHI) sensor mounted on Himawari-8 satellite. In order to improve the visualization, we performed two methods of Nonlinear enhancement and Histogram equalization. As a result, Histogram equalization showed a strong bluish image in the region over $70^{\circ}$ Solar Zenith Angle (SZA) compared to the Nonlinear enhancement and nonlinear enhancement technique showed a reddish vegetation area.