• International Area Studies Review
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• v.13 no.2
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• pp.66-88
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• 2009

This paper studies optimal discretionary monetary policy in the presence of uncertainty in the housing sector. In particular, we allow two possible regimes regarding the evolution of housing price inflation and the effects of housing price inflation on the aggregate demand. Estimation results with the US data confirm the presence of two distinctive regimes, one 'normal' and the other more akin to the housing price 'bubble' state. The optimal policy is 'asymmetric' in that the optimal responses in the 'normal' regime require the central bank to lean against the wind to inflationary pressure from CPI and housing inflation, while the central bank is recommended to accommodate it in the other regime.

• Journal of Radiation Protection and Research
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• v.43 no.4
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• pp.143-153
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• 2018

Background: The determination of the amount of radionuclides and internal dose for the worker who may have intake of radionuclides results in a variation due to uncertainty of measurement data and ingestion information. As a result of this, it is possible that for the same internal exposure scenario assessors could make considerably different estimation of internal dose. In order to reduce this difference, internal exposure scenarios for nuclear facilities were developed, and intercomparison were made to determine the harmonization of dose assessment results among the assessors. Materials and Methods: Seven cases on internal exposures incidents that have occurred or may occur were prepared by referring to the intercomparison excercise scenario that NRC and IAEA have carried out. Based on this, 16 nuclear facilities concerned with internal exposure in Korea were asked to evaluate the scenarios. Each result was statistically determined according to the harmonization discrimination criteria developed by IDEAS/IAEA. Results and Discussion: The results were evaluated as having no outliers in all 7 cases. However, the distribution of the results was spread by various causes. They can be divided into two wide categories. The first one is the distribution of the results according to the assumption of the intake factors and the evaluation factors. The second one is distribution due to misapplication of calculation method and factors related to internal exposure. Conclusion: In order to satisfy the harmonization criteria and accuracy of the internal exposure dose evaluation, it is necessary that exact guidelines should be set on low dose, and various intercomparison cases also be needed including high dose exposure as well as the specialized education. The aim of the blind test is to make harmonization evaluation, but it will also contribute to securing the expertise and high quality of dose evaluation data through the discussion among the participants.

• Journal of the Korean Geotechnical Society
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• v.35 no.3
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• pp.37-46
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• 2019

Recently, the use of CPT-based liquefaction assessment method has increased by providing more accurate results than other field tests. In CPT-based liquefaction evaluation, various soil properties are predicted and they are used for liquefaction potential assessment. In particular, fines content is one of the important input parameters in CPT-based liquefaction assessment, so it is very important to use correct prediction model and to make quantitative evaluation of estimating variability of fines content. In this study, the error evaluation of existing models for prediction of fines content through CPT was performed, and the most suitable model was selected for Pohang area, where the liquefaction phenomenon was observed in the 2017. In addition, the inherent variability of soil was analyzed, and the estimating variability of fines content was evaluated quantitatively considering the inherent variability of soil, measurement error of CPT and transformation uncertainty of selected model.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.1
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• pp.1-6
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• 2019

The annual production of silver croaker (Argyrosomus argentatus) in Korean towed fishing gears has been increased in recent five years. In 2017, the annual production of silver croaker in metric ton was increased 99.2% compared to 2013. However, the research for silver croaker has been focused on ecology in Korea. There has not been enough research in terms of fishing gears. Therefore, the research for retention probability for towed gears was conducted on covered codend method from June, 2016 to July, 2018. During the experiments, the total catch of silver croaker was 1,563. The geometry of the experimental trawl gear was controlled by trawl monitoring system; net height was 3.3 m, distance of trawldoors was 59.8 m and distance of wing net was 17.3 m. The selection curve for silver croaker was estimated by a logit model. The analysis was applied with the confidence interval to reduce uncertainty of the estimation. The $l_{50}$ was 13.87 cm and its selection range was 2.71 cm. P-value was estimated at 0.99. The mesh size for silver croaker in towed gears needs to be adjusted by considering its minimum maturity length, stakeholder's interests and fisheries regulations.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.110-110
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• 2016

도시 유역의 강우-유출 모의에는 지표 투수율 및 하수관거 영향 등 인위적 배수계통의 영향을 고려할 수 있는 도시유출모형이 널리 이용되고 있으며, 모형 검증을 통해 모의 성능을 평가한다. 도시유출모형의 검증은 일반적인 강우-유출 모형과 같이 강우사상별 유량의 관측시계열과 모의시계열의 목적함수가 최소가 되는 최적 매개변수를 탐색하는 과정이다. 도시유출모형의 검증에서 발생하는 문제점은 크게 다음과 같다. 첫째, 대규모 도시 유역의 복잡하고 다양한 하수관거에 대한 최적매개변수를 관거별로 구하는 것은 물리적으로 불가능하다. 따라서 동일 배수분구내 하수관거의 매개변수 값은 동일하다고 가정하거나, 모형 단순화 과정을 통해 매개변수의 물리적 범위 내에서 최적해를 탐색해야 하는 단순화에서 기인한 불확실성이 있다. 둘째, 다양한 매개변수들의 물리적 범위를 고려하기 위해서는 전역최적화기법이 유효하다. 그러나 전역최적화 종류, 목적함수, 모의횟수, 목표성능별 최적 매개변수 결과가 각각 다르므로 추정된 최적 매개변수의 범위에 대한 불확실성이 있다. 이에 본 연구에서는 Bayesian 모형과 EPA SWMM(Storm Water Management Model)을 연계하여 도시유출모형의 매개변수 불확실성을 정량적으로 분석할 수 있는 모형을 제안하고자 한다. 이를 위해 서울 우이천 유역을 대상으로 SWMM 모형을 구축하고, 절단 정규분포(truncated Gaussian distribution)를 사전분포(prior)로 가정하여 매개변수의 물리적 범위를 고려하였다. 최종적으로 결합확률분포로 계산된 각 매개변수간 사후분포를 통해 모의된 유출량의 불확실성을 정량적으로 분석하였다. 본 연구에서 제안된 모형은 대규모 도시 유역의 도시유출모형 구축 시 다양한 매개변수의 물리적 범위를 고려한 최적화와 동시에 내재된 불확실성을 정량적으로 분석할 수 있으므로, 침수예측 및 홍수예경보 등의 문제에서 상당한 신뢰성을 확보할 수 있을 것으로 판단된다.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.131-139
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• 2022

The aim of this study was to assess the feasibility of a landfill project to reduce greenhouse gas (GHG) from La Chureca Landfill in Managua, Nicaragua ("Project"). The feasibility study involved surveying the status and composition of waste on its way in to the landfill and projecting GHG emissions from the landfill. A projection of the GHG emissions with the IPCC model based on the survey results indicated the period 2006 to 2043 would see mean yearly GHG emissions of 290,147 ton-CO₂/year with model certainty not considered, and 217,610 ton-CO₂/year with model certainty considered. Thus, the result exceeded the corresponding median and mean values of other CDM projects implemented in Central America, even after model uncertainty was considered together with the conservative estimation of carbon capture efficiency. The similar result was produced even with an analysis of sensitivity to error factors. All the findings of the study are expected to be applicable as basic data for deciding about whether & how to proceed with the Project.

• Journal of Astronomy and Space Sciences
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• v.37 no.4
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• pp.237-247
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• 2020

The ground tracking support is a critical factor for the navigation performance of spacecraft orbiting around the Moon. Because of the tracking limit of antennas, only a small number of facilities can support lunar missions. Therefore, case studies for various ground tracking support conditions are needed for lunar missions on the stage of preliminary mission analysis. This study analyzes the ground supporting condition effect on orbit determination (OD) of Korea Pathfinder Lunar Orbiter (KPLO) in the lunar orbit. For the assumption of ground support conditions, daily tracking frequency, cut-off angle for low elevation, tracking measurement accuracy, and tracking failure situations were considered. Two antennas of deep space network (DSN) and Korea Deep Space Antenna (KDSA) are utilized for various tracking conditions configuration. For the investigation of the daily tracking frequency effect, three cases (full support, DSN 4 pass/day and KDSA 4 pass/day, and DSN 2 pass/day and KDSA 2 pass/day) are prepared. For the elevation cut-off angle effect, two situations, which are 5 deg and 10 deg, are assumed. Three cases (0%, 30%, and 50% of degradation) were considered for the tracking measurement accuracy effect. Three cases such as no missing, 1-day KDSA missing, and 2-day KDSA missing are assumed for tracking failure effect. For OD, a sequential estimation algorithm was used, and for the OD performance evaluation, position uncertainty, position differences between true and estimated orbits, and orbit overlap precision according to various ground supporting conditions were investigated. Orbit prediction accuracy variations due to ground tracking conditions were also demonstrated. This study provides a guideline for selecting ground tracking support levels and preparing a backup plan for the KPLO lunar mission phase.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.149-159
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• 2021

This paper represents a reentry time prediction analysis of CZ-5B rocket-body in China, subject to analysis of the Inter-Agency Space Debris Coordination Committee Reentry (IADC) reentry test campaign conducted in May 2021. Predicting the reentry of space objects is difficult to accurately predict due to the lack of accurate physical information about target, and uncertainty in atmospheric density. Therefore, IADC conducts annual re-entry campaigns to verify analysis techniques by each agency, and the Korea Aerospace Research Institute has also participated in them since 2015. Ballistic coefficient estimation method proposed to predict target reentry time and the result confirmed the difference of 73 seconds, which confirms the accuracy of the proposed method.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.