• Elastomers and Composites
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• v.47 no.2
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• pp.129-140
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• 2012

The objective of this study was to estimate the practical recycling rate of plastic products, so that the study was conducted to build material flow diagram for PVC profile. For this objective, product generation, waste generation and recycling status were investigated. Using collected and analyzed status data, analysis of material flow by product and building material flow diagram were conducted. As result of estimating the recycling rate by product, The sum of domestic demand was 525,448 ton and waste generation was 105,853ton in PVC flooring and profile. The sum of generation of recycling product and raw material was investigated to be 76,004ton(14.46%), which is higher compared to recycling obligation(8.5%) in 2009. To build the material flow diagram in the years(5~20years) ahead, prediction of future demand was based on the assumption that there will be no difference in annual generation of current and future. As the recycling rate of flooring and profile increases, it is estimated to reach 20% in 2013 according to the material flow diagram.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1051-1060
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• 2014

This study is attempted to realize an improved computation performance by combining the MPI (Message Passing Interface) Technique, a standard model of the parallel programming in the distributed memory environment, with the DHM(Diffusion Hydrodynamic Model), a inundation analysis model. With parallelizing inundation model, it compared with the existing calculation method about the results of applications to complicate and required long computing time problems. In addition, it attempted to prove the capability to estimate inundation extent, depth and speed-up computing time due to the flooding in protected lowlands and to validate the applicability of the parallel model to the actual flooding analysis by simulating based on various inundation scenarios. To verify the model developed in this study, it was applied to a hypothetical two-dimensional protected land and a real flooding case, and then actually verified the applicability of this model. As a result of this application, this model shows that the improvement effectiveness of calculation time is better up to the maximum of about 41% to 48% in using multi cores than a single core based on the same accuracy. The flood analysis model using the parallel technique in this study can be used for calculating flooding water depth, flooding areas, propagation speed of flooding waves, etc. with a shorter runtime with applying multi cores, and is expected to be actually used for promptly predicting real time flood forecasting and for drawing flood risk maps etc.

• Journal of Gifted/Talented Education
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• v.22 no.2
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• pp.221-241
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• 2012

The purpose of this study was to explore differences in styles of perfectionism, stress, and career decision-making of gifted musicians by their grade level and examine the effect of perfectionism and stress of gifted musicians on their rational career decision-making. The participants of this study were 88 gifted musicians attending in the middle school of arts. The results indicated that there were significant differences in dimensions of perfectionism by the grade level of gifted musicians. The tendency of self-oriented, other-oriented, and socially prescribed perfectionism was higher with grade level. There were significant differences by grade level in stresses that gifted musicians experienced. The higher a grade level was, the more gifted musicians got stresses in self-development, parent-child relationship, peer relationship, and school life. The effect of grade level on career decision-making was significant in the rational and the transitional career decision-making. The higher a grade level was, the more rational decision-making was but the less transitional decision-making was. Meanwhile, when the effect of perfectionism and stress of gifted musicians on the rational career decision-making was examined, the rational career decision-making was predicted by self oriented perfectionism and self-development stress.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.1
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• pp.72-82
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• 2014

This study was conducted to predict the changes of yearly productive area distribution for pinus densiflora under climate change scenario. For this, site index equations by ecoprovinces were first developed using environmental factors. Using the large data set from both a digital forest site map and a climatic map, a total of 48 environmental factors including 19 climatic variables were regressed on site index to develop site index equations. Two climate change scenarios, RCP 4.5 and RCP 8.5, were then applied to the developed site index equations and the distribution of productive areas for pinus densiflora were predicted from 2020 to 2100 years in 10-year intervals. The results from this study show that the distribution of productive areas for pinus densiflora generally decreases as time passes. It was also found that the productive area distribution of Pinus densiflora is different over time under two climate change scenarios. The RCP 8.5 which is more extreme climate change scenario showed much more decreased distribution of productive areas than the RCP 4.5. It is expected that the study results on the amount and distribution of productive areas over time for pinus densiflora under climate change scenarios could provide valuable information necessary for the policies of suitable species on a site.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.287-297
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• 2016

This study aims to identify the relationship between climatic factors and stand mean Diameter at Breast Height (DBH) for two major tree species; Pinus densiflora and Quercus mongolica in Andong-dam basin. Forest variables such as age, diameter distribution and number of trees per hectare from the $5^{th}$ and $6^{th}$ National Forest Inventory data were used to develop a DBH estimation model. Climate data were collected from six meteorological observatory station and twelve Automatic Weather System provided by Korea Meteorological Administration to produce interpolated daily average temperature map with Inverse Distance Weighting (IDW) method. Andong-dam basin reflects rugged mountainous terrain, so temperature were adjusted by lapse rate based correction. As a result, predictions of model were consistent with the previous studies; that the rising temperature is negatively related to the growth of Pinus densiflora whereas opposing trend is observed for Quercus mongolica.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.118-118
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• 2012

수자원의 효율적 이용 및 관리는 심화되고 있는 기후변화에 선제적으로 대응하고 발생가능한 물위기에 대비하기 위한 필수조건이다. 그 중 가장 핵심이 되는 요소는 댐에 저수된 물을 효과적으로 이용하는 것, 즉 댐 건설목적에 따라 시간 및 공간별로 적절하게 할당시키는 것이라고 할 수 있다(Kim, 1998). 그러나 단일 댐의 운영과는 달리 수계내 댐군의 연계 운영은 매우 복잡하고 어려운 문제이다. 연계된 댐들간 저수 상황을 고려하여 유역내 시 공간적인 용수 수요의 지속적인 충족을 위하여 유입량 예측의 정확성을 높이도록 하고, 상류 댐에 최대한 저류하도록 하며, 여수로 방류 같은 불필요한 방류를 최소화 하고, 서로 상충되는 목표를 갖고 있지만, 홍수용량 및 발전수위를 최대로 확보하도록 하여야 한다. 이처럼 댐 운영을 위한 실제 상황은 단일 목적에 의한 최적화와는 달리 여러 상충되는 목적 및 구성 요소들간의 타협, 조정을 필요로 한다. 댐군의 연계운영 문제는 1960년대 초부터 현재까지 활발히 연구가 진행되어 온 분야 중 하나이나 문제의 복잡성과 어려움으로 인해 아직까지도 최선의 방안을 제시하기 어려운 문제이다(ReVelle, 2000). 이를 위한 방법은 시뮬레이션 모형 활용기법과 최적화 모형 활용기법으로 대별할 수 있으며 각 방법의 서로 다른 구조적 특성과 장단점으로 인하여 이원화된 체계로 사용되는 것이 현재의 국내 실정이다. 대부분의 실무에서는 이해도도 쉽고, 비교적 결과를 빨리 도출할 수 있는 시뮬레이션 기반의 모형을 활용하며 대표적으로 HEC-5, K-ModSim, HEC-ResSim 등이 활용되어왔다. 반면, 학계에서는 DP, MIP, SLP, SDP 등 최적화기법을 댐운영에 활용 할 것을 제안하고 있지만, 활용에 대한 거부감이 남아있는 것이 현실이다. 본 연구에서는 시뮬레이션과 최적화기법의 원론적 비교를 통해 각 방법의 장단점과 한계점을 분석하고, 왜 이원화된 사용체계로 되었는지에 대한 고찰과 이에 대한 해결책으로 시뮬레이션모형의 장점과 최적화기법의 장점을 결합한 모형을 제안한다. 국내에는 Kim and Park(1998)이 시뮬레이션 기반의 최적화 모형 CoMOM(Coordinated Multi-Reservoir Operating Model)을 개발하였으며, 이후 21C프론티어 연구사업(2001-2011)에서 모형의 보완수정 검증을 통해 실무 활용도를 높여 왔다. 본 연구를 통하여 거부감의 원천을 추적해 보고, 타당한 이유가 있는지 대한 것을 심층 분석해보고, CoMOM모형과 시뮬레이션 모형, 다른 최적화 기법들과의 원론적 비교를 통해 각 방법들의 효율적인 활용방안과 최적화모형의 구체적인 활용방안을 제시하고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.27-39
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• 2008

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In summer, landslides frequently occur due to local torrential rains and storms. It is critical to predict the potential areas of landslides in advance and to take preventive measures to minimize consequences and to protect property and human life. The previous study on landslides mostly focused on identifying the causes of landslides in the areas where they occurred, and on analyzing landslide vulnerability around the areas without considering rainfall conditions. Thus there were not enough evaluations of the direct risk of landslides to human life. In this study, potentially risky areas for landslides were identified using the GIS data in order to evaluate direct risk on farmlands, roads, and artificial structures that were closely connected to human life. A map of landslide risk was made taking into account rainfall conditions, and a land use map was also drawn with satellite images and digital maps. Both maps were used to identify potentially risky areas for landslides.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.