• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.440-447
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• 2013

In this paper, a higher harmonic control (HHC) methodology is applied to find the optimum input scenario for the vibratory hub loads reduction. A comprehensive aeroelastic analysis code, CAMRAD II, is used to model the HART (Higher-harmonic-control Aeroacoustic Rotor Test) II rotor, and parametric study is conducted for the best HHC inputs leading to a minimum vibration (MV) condition. The resulting outcomes are compared with the earlier HART II test results. It is indicated that the control input adopted in the MV condition showed less satisfactory results. The new MV condition obtained in the present investigation can achieve 45% lower vibration level than the baseline uncontrolled condition. The optimum HHC input results lead to 3/rev harmonic input having $0.8^{\circ}$ amplitude and $350^{\circ}$ phase angle. About 5% reduction in the required power is possible but accompanies with the increase of vibration level.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1097-1101
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• 2008

본 연구에서는 SLURP 수문모형을 이용하여 미래기후와 예측된 토지이용자료 및 식생의 활력도를 고려한 상태에서 하천유역의 유출 및 증발산량에 미치는 영향을 분석하였다. 경안천 상류유역($260.04\;km^2$)을 대상유역으로 선정하여 4개년(1999-2002) 동안의 일별 유출량 자료를 바탕으로 모형의 보정(1999-2000)과 검증(2001-2002)을 실시하였다. 모형의 보정 및 검정 결과 Nash-Sutcliffe 모형효율은 0.79에서 060의 범위로 나타났다. 미래 기후자료는 IPCC(Intergovernmental Panel on Climate Change)에서 제공하는 A1B 기후변화시나리오의 MIROC3.2 hires, ECHAM5-OM, HadCM3 모델의 결과값을 이용하였다. 먼저 과거 30년 기후자료(1977-2006, baseline)를 바탕으로 각 모델별 20C3M(20th Century Climate Coupled Model)의 모의 결과값을 이용하여 강수와 온도를 보정한 뒤 Change Factor Method로 Downscaling하였다. 미래 기후자료는 2020s(2010-2039), 2050s(2040-2069), 2080s(2070-2099)의 세 기간으로 나누어 분석하였다. 미래 토지이용은 과거 시계열 Landsat 토지이용도를 이용하여 CA-Markov기법으로 예측된 토지이용을 사용하였으며, 미래의 식생정보 예측을 위하여 NOAA/AVHRR 위성영상으로부터 추출된 월별 NDVI(1998-2002)와 월평균기온간의 선형 회귀식을 도출하여 미래의 식생지수 정보를 추정하였다. 모형의 적용결과, 미래기후변화에 따른 연평균 하천유출은 현재보다 최대 2020s는 23.9%, 2050s는 40.7%, 2080s는 39.5% 증가하였다. 봄 강수량 패턴의 변화로 유출량 증가하는 것으로 나타났으며 여름에는 유출량은 감소하고 증발산량은 증가하는 결과를 보였다.

• Ocean and Polar Research
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• v.34 no.2
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• pp.239-251
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• 2012

The food web dynamics in a coastal ecosystem of Korea were predicted with Ecosim, a trophic flow model, under various scenarios of primary productivity due to ocean warming and ocean acidification. Changes in primary productivity were obtained from an earth system model 2.1 under A1B scenario of IPCC $CO_2$ emission and replaced for forcing functions on the phytoplankton group during the period between 2020 and 2100. Impacts of ocean acidification on species were represented in the model for gastropoda, bivalvia, echinodermata, crustacean and cephalopoda groups with effect sizes of conservative, medium and large. The model results show that the total biomass of invertebrate and fish groups decreases 5%, 11~28% and 14~27%, respectively, depending on primary productivity, ocean acidification and combined effects. In particular, the blenny group shows zero biomass at 2080. The zooplankton group shows a sudden increase at the same time, and finally reaches twice the baseline at 2100. On the other hand, the ecosystem attributes of the mean trophic level of the ecosystem, Shannon's H and Kempton's Q indexes show a similar reduction pattern to biomass change, indicating that total biomass, biodiversity and evenness shrink dynamically by impacts of climate change. It is expected from the model results that, after obtaining more information on climate change impacts on the species level, this study will be helpful for further investigation of the food web dynamics in the open seas around Korea.

• KIEAE Journal
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• v.14 no.3
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• pp.5-13
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• 2014

This paper introduces emergy(spelled with "m") that is a new environmental indicator in architecture, aiming to clarify conflicting claims of building design components in the process of energy-retrofit. Much of design practitioners' attention on low energy use in operational phases, may simply shift the lowered environmental impact within the building boundary to large consumption of energy in another area. Specifically, building energy reduction strategies without a holistic view starting from natural formation, may lead to the depletion of non-renewable geobiological sources (e.g. minerals, fossil fuels, etc.), which leaves a building with an isolated energy-efficient object. Therefore, to overcome the narrow outlook, this research discusses the total ecological impact of a building which embraces all process energy as well as environmental cost represented by emergy. A case study has been conducted to explore emergy-driven design work. In comparison with operational energy-driven scenarios, the results elucidate how energy and emergy-oriented decision-making bring about different design results, and quantify building components' emergy contribution in the end. An average-size ($101.9m^2$) single family house located in South Korea was sampled as a benchmark case, and the analysis of energy and material use was conducted for establishment of the baseline. Adoption of the small building is effective for the goal of study since this research intends to measure environmental impact according to variation of passive design elements (windows size, building orientation, wall materials) with new metric (emergy) regardless of mechanical systems. Performance simulations of operational energy were developed and analyzed separately from the calculation of emergy magnitudes in building construction, and then the total emergy demand of each proposed design was evaluated. Emergy synthesis results verify that the least operational energy scenario requires greater investment in indirect energy in construction, which clearly reveals that efficiency gains are likely to be overwhelmed by increment of material flows. This result places importance on consideration of indirect energy use underscoring necessity of emergy evaluation towards the environment-friendly building in broader sense.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.5
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• pp.77-87
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• 2014

The impacts of climate change on upland crops is great significance for water resource planning, estimating crop water demand and irrigation scheduling. The objective of this study is to predict upland crop evapotranspiration, effective rainfall and net irrigation requirement for upland under climate change, and changes in the temporal trends in South Korea. The changes in consumptive use and net irrigation requirement in the six upland crops, such as Soybeans, Maize, Potatoes, Red Peppers, Chinese Cabbage (spring and fall) were determined based on the soil moisture model using historical meteorological data and climate change data from the representative concentration pathway (RCP) scenarios. The results of this study showed that the average annual upland crop evapotranspiration and net irrigation requirement during the growing period for upland crops would increase persistently in the future, and were projected to increase more in RCP 8.5 than those in RCP 4.5 scenario, while effective rainfall decreased. This study is significant, as it provides baseline information on future plan of water resources management for upland crops related to climate variability and change.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.1-10
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• 2021

Agricultural reservoirs have a great influence on the water circulation in the watershed. It is necessary to evaluate the impact on water circulation by the agricultural reservoir. Therefore, in this study, we simulated the agricultural watershed through linkage of Hydrological Simulation Program Fortran (HSPF) and Module-based hydrologic Analysis for Agricultural watershed (MASA) and evaluated the contribution of the agricultural reservoir to water circulation by watershed water circulation index. As a result of simulating the Idong reservoir watershed through the HSPF-MASA linkage model, the model performance during the validation period was R2 0.74 upstream, 0.78 downstream, and 0.76 reservoir water level, respectively. To evaluate the contribution of agricultural reservoirs, three scenarios (baseline, present state, and present state without reservoir) were simulated, and the water balance differences for each scenario were analyzed. In the evaluation through the agricultural water circulation rate in the watershed, it was found that the water circulation rate increased by 1.1%, and the direct flow rate decreased by 13.6 mm due to the agricultural reservoir. In the evaluation through the Budyko curve, the evaporation index increased by 0.01. Agricultural reservoirs reduce direct runoff and increase evapotranspiration, which has a positive effect on the water circulation.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of Radiation Protection and Research
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• v.48 no.2
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• pp.90-99
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• 2023

Background: Quantitative risk assessments should be accompanied by uncertainty analyses of the risk models employed in the calculations. In this study, we aim to develop a computational code named SUMRAY for use in cancer risk projections from radiation exposure taking into account uncertainties. We also aim to make SUMRAY publicly available as a resource for further improvement of risk projection. Materials and Methods: SUMRAY has two versions of code written in R and Python. The risk models used in SUMRAY for all-solid-cancer mortality and incidence were those published in the Life Span Study of a cohort of the atomic bomb survivors in Hiroshima and Nagasaki. The confidence intervals associated with the evaluated risks were derived by propagating the statistical uncertainties in the risk model parameter estimates by the Monte Carlo method. Results and Discussion: SUMRAY was used to calculate the lifetime or time-integrated attributable risks of cancer under an exposure scenario (baseline rates, dose[s], age[s] at exposure, age at the end of follow-up, sex) specified by the user. The results were compared with those calculated using another well-known web-based tool, Radiation Risk Assessment Tool (RadRAT; National Institutes of Health), and showed a reasonable agreement within the estimated confidential interval. Compared with RadRAT, SUMRAY can be used for a wide range of applications, as it allows the risk projection with arbitrarily specified risk models and/or population reference data. Conclusion: The reliabilities of SUMRAY with the present risk-model parameters and their variance-covariance matrices were verified by comparing them with those of the other codes. The SUMRAY code is distributed to the public as an open-source code under the Massachusetts Institute of Technology license.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.1-24
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• 2014

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis. Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world. We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively. The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change. Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.277-287
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• 2015

This paper addresses limitations of land-change modeling application in the context of REDD (Reducing Emissions from Deforestation and forest Degradation). REDD is an international conservation policy that aims to protect forests via carbon credit generation and trading. In REDD, carbon credits are generated only if there is measurable quantied carbon sequestration activities that are additional to business-as-usual (BAU). A "reference level" is defined as simulated baseline carbon emissions for the future under a BAU scenario, and predictive land-change modeling plays an important role in constructing reference levels. It is tested in this research how predictive accuracies of two land-change models, namely Geographic Emission Benchmark (GEB) and GEOMOD, vary with respect to different spatial scales: Xishuangbanna prefecture and Yunnan province. The accuracies are measured by Figure of Merit. In this Chinese case study, it turns out that GEB's better performance is mainly due to quantity (e.g., how many hectares of forest will be converted to agricultural land?) rather than spatial allocation (e.g., where will the conversion happen?). As both quantity and allocation are crucial in REDD reference level setting it appears to be fundamental to systematically analyze accuracies of quantity and allocation independently in pursuit of accurate reference levels.