• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.35-47
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• 2022

Expansion of potato production areas can improve the food security in North Korea because the given crop has less requirements for agricultural materials and facilities. The Global Agro-Ecological Zones (GAEZ) model, which was developed to evaluate climate suitability under different cultivation conditions, was used to identify potential areas for the potato production. The spatial estimates of crop suitability under low and high input management conditions were downloaded from the GAEZ data portal. The values of suitability were obtained at the potato occurrence sites retrieved from the Global Biodiversity Information Facility (GBIF) database. The suitable areas for the potato production were identified using a threshold value derived from the suitability estimates at the occurrence sites. It was found that 90% of the occurrence sites had the suitability index value >3,333, which was set to be the threshold value. The suitable areas in North Korea were summarized by province and county. Rice cultivation areas were excluded from the analysis. The reported relative acreage of potato production was better represented by the suitable areas under the low input management options than the high input conditions. The suitable areas also had a similar distribution to the reported acreage of potato production by county. These results indicated that the GAEZ model would be useful to identify the candidate production areas, which would facilitate the increases in potato production especially under future climate conditions. Furthermore, monthly maps of crop suitability can be used to design cropping systems that would improve crop production under the limited use of agricultural materials and facilities.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.505-513
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• 2022

Recently, uncertainty in predicting available water resources is gradually increasing due to climate change and extreme weather conditions. Social interest in water management such as flood and drought prevention is also increasing, and after the unification of water management implemented in 2018, domestic water management is facing a major turning point. As part of such strengthening of water management capabilities, various studies are being conducted to utilize a hydropower dam for flood control and water supply purposes, which was mainly operated for hydroelectric power generation. However, since the dam evaluation methods developed based on a multi-purpose dam are being applied to hydropower dams, an additional evaluation approach that can consider the characteristics of hydropower dams is required. In this study, a new water supply capacity evaluation method is presented in consideration of the operational characteristics of hydropower dams in terms of water supply, and a connected reservoir simulation method is proposed to evaluate the comprehensive water supply capacity of a dam group operating in a river basin. The presented method was applied to the hydropower dams located in the Bukhan River basin, and the results of the water supply yield of individual dams and multi-reservoir systems were compared and analyzed. In the future, the role of hydropower dams for water supply during drought is expected to become more important, and this study can be used for sustainable domestic water management research using hydropower dams.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.289-295
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• 2021

Today, the most important factor affecting the quality of human life is thought to be due to the environment. The importance of environmental monitoring systems to improve human life and improve welfare as the magnitude of the damage increases year by year due to the rapid increase in the frequency of hail, typhoons, collapse of incisions, landslides, etc. Is increasing day by day. Among environmental problems, problems caused by water quality have a very high proportion, and as there is a growing concern that the scale of damage will increase when water pollution accidents occur due to urbanization and industrialization, the demand for social water safety nets is increasing. have. In the last 5 years, 259 cases of water pollution (Han River 99, Nakdong River 31, Geum River 25, Seomjin River and Yeongsan River 19, and 85 others) have occurred in the four major river basins. Caused damage. Therefore, it is required to establish a water quality environment management strategy system based on big data that can minimize the uncertainty of the water quality environment by expanding the target of water quality management from the current water quality management system centered on the four major rivers to small and medium-sized rivers, tributaries/branches, and reservoirs. In this paper, we intend to construct and analyze a water quality monitoring system based on big data that can present useful water quality environment information by analyzing the water quality information accumulated for a long time.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.35-44
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• 2022

The V_S30 map is used as a key variable for site amplification in the ShakeMap, which predicts ground motion at any site. However, no V_S30 map considering Korean geology and geomorphology has been developed yet. To develop a proxy-based V_S30 map, we used 1,101 V_S profiles obtained from a geophysical survey and collected proxy layers of geological and topographical information for the Korean Peninsula. Then, V_S30 prediction models were developed using linear regression analysis for each geological age considering the distribution of V_S30. As a result, models depending on geomorphology were suggested per each geologic group, including Quaternary, Fill, Ocean, Mesozoic group and Precambrian. Resolution of map is doubled from that of V_S30 map by U.S. Geological Survey (USGS). Standard deviation of residual in natural log of proxy-based V_S30 map is 0.233, whereas standard deviation of slope-based USGS V_S30 map is 0.387. Therefore, the proxy-based V_S30 map developed in this study is expected to have less uncertainty and to contribute to predicting more accurately the ground motion amplitude.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.785-799
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• 2022

The aim of this study was to characterize the emission pathways and the footprint of greenhouse gases (GHG) in Daecheong Reservoir using the G-res Tool, and to evaluate the GHG emission intensity (EI) compared to other energy sources. In addition, the change in GHG emissions was assessed in response to the total phosphorus (TP) concentration. The GHG flux in post-impoundment was found to be 262 gCO₂eq/m²/yr, of which CO₂ and CH₄ were 45.7% and 54.2%, respectively. Diffusion of CO₂ contributed the most, followed by diffusion, degassing, and bubbling of CH₄. The net GHG flux increased to 510 gCO₂eq/m²/yr because the forest (as CO₂ sink) was lost after dam construction. The EI of Daecheong Reservoir was 86.8 gCO₂eq/kWh, which is 3.7 times higher than the global EI of hydroelectric power, due to its low power density. However, it was remarkable to highlight the value to be 9.5 times less than that of coal, a fossil fuel. We also found that a decrease in TP concentration in the reservoir leads to a decrease in GHG emissions. The results can be used to improve understanding of the GHG emission characteristics and to reduce uncertainty of the national GHG inventory of dam reservoirs.

• Journal of IKEEE
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• v.26 no.3
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• pp.510-514
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• 2022

In this paper, we propose the development of high-sensitivity low-end radiation measuring sensor module. The proposed measurement sensor module is a scintillator + photomultiplier(SiPM) sensor optimization structure design, amplification and filter and control circuit design for sensor driver, control circuit design including short-distance communication, sensor mechanism design and manufacturing, and GUI development applied to prototypes consists of, etc. The scintillator + photomultiplier(SiPM) sensor optimization structure design is designed by checking the characteristics of the scintillator and the photomultiplier (SiPM) for the sensor structure design. Amplification, filter and control circuit design for sensor driver is designed to process fine scintillation signal generated by radiation with a scintillator using SiPM. Control circuit design including short-distance communication is designed to enable data transmission through MCU design to support short-range wireless communication function and wired communication support. The sensor mechanism design and manufacture is designed so that the glare generated by wrapping a reflective paper (mirroring) on the outside of the plastic scintillator is reflected to increase the efficiency in order to transmit the fine scintillation signal generated from the plastic scintillator to the photomultiplier(SiPM). The GUI development applied to the prototype expresses the date and time at the top according to each screen and allows the measurement unit and time, seconds, alarm level, communication status, battery capacity, etc. to be expressed. In order to evaluate the performance of the proposed system, the results of experiments conducted by an authorized testing institute showed that the radiation dose measurement range was 30 𝜇Sv/h ~ 10 mSv/h, so the results are the same as the highest level among products sold commercially at domestic and foreign. In addition, it was confirmed that the measurement uncertainty of ±7.4% was measured, and normal operation was performed under the international standard ±15%.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.