• 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.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.351-368
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• 2021

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.6
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• pp.946-953
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• 2022

Using the management strategy evaluation methods and data on the Korea chub mackerel Scomber japonicus where the state-space logistic production model is used as the operation and the estimation model respectivley, we examined the effects of both Dorn's rule, α and the buffer value for ABC (allowable biological catch), which are used by the Korea fishery managers for decision rules. We set scenarios that have different pairs of buffer and α values, which include those currently used in the management in Korea. Under each sceanario, we projected the fish population biomass until year 2050, during which ABC is determined in each year with the decision rule. We used three kinds of performance measures: (i) whether the biomass in 2050 is overfished; (ii) the average of annual yields over the simulation period; and (iii) the variability of annual yields over the period. We found that the current practice (buffer=0.9, and α = 0.05) resulted in the best performance in terms of avoiding the "overfished" status. However, the current practice failed to reach the maximum average of the annual yields and led to larger uncertainty in the annual yields.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.401-401
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• 2023

터널이나 폐기물저장소와 같은 지하 구조물 또는 지표에서부터 상당한 깊이로 설계되거나 건설되는 구조물을 계획하거나 건축할 때 구조물의 안전성에 영향을 주는 수많은 요인 중에 간과할 수 없는 것이 바로 지하수이다. 뿐만 아니라 지상 혹은 지하에서 오염이 발생하여 오염물질이 지중환경으로 유입되는 경우, 지하수 거동은 오염물의 이송·확산에 지대한 요인으로 작용한다. 최근에는 지구온난화와 같은 유례없는 기후변화를 경험하고 있고, 따라서 수자원으로써 지하수의 역할이 더욱 중요해지고 있다. 지하수의 저류와 거동은 지하매질의 특성에 지배되고 있지만, 지표 아래 자리잡고 있는 매질의 특성을 정확히 파악하기란 매우 힘들고, 따라서 지하수 거동을 해석함에 항상 불확실성이 존재한다. 전통적으로 지하매질의 특성을 이해하기 위해 다양한 지구물리탐사를 수행하여 왔고, 더욱 직접적인 관찰을 위해 시추를 수행하여, 시료를 수집·관찰하고, 시추공에서의 다양한 현장수리실험을 통해 수리특성을 알고자 하였다. 하지만 그동안의 다양한 노력에도 불구하고, 지하매질 및 지하수 거동에 대한 불확실성은 여전히 줄어들지 않고, 오히려 증가하고 있다. 따라서, 본 연구에서는 지하수 거동을 결정짓는 지하매질의 수리특성에 대한 불확실성을 정량화하기 위한 도구로써, 매개변수 보정법의 하나인 Pilot Point Method(PPM)을 소개하고자 한다. 우물 또는 관측정을 통해 관측되는 지하수의 수위는 지하매질의 특성을 반영하고 있으며, 인간이 가장 쉽게 취득할 수 있는 지하 정보에 해당한다. 지하수 수위를 이용하여 수치모형의 매개변수를 보정하게 되며, 이 때 PPM이 적용된다. Pilot points의 공간적인 분포에 따라 다양한 보정 결과가 산출될 수 있으며, 다양한 결과들을 통해 변동계수를 산정한 후 수리특성의 불확실성이 높은 지역을 나타낼 수 있다. 본 연구를 통해 얻은 결과는 물리탐사 또는 시추 작업을 위한 위치 선정의 기초자료로 활용될 수 있다.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.248-255
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• 2023

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship's route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5C
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• pp.343-350
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• 2006

The resistance bias factors for driven steel pipe piles are evaluated as a part of study to develop the LRFD(Load and Resistance Factor Design) for foundation structures in Korea. The 43 data sets of static load tests and soil property tests performed in the whole domestic area were collected and analyzed to determine the representative bearing capacities of the piles using various methods. Based on the statistical analysis of the data, the Davisson's criterion is proved to be the most reasonable method for estimation of pile bearing capacity among the methods used. The static bearing capacity formulas and the Meyerhof method using N values are applied to calculate the design bearing capacity of the piles. The resistance bias factors of the driven steel pipe piles are evaluated respectively as 0.98 and 1.46 by comparison of the bearing capacities for both of the static bearing capacity formulas and the Meyerhof method. It is also shown that uncertainty of the static bearing capacity formulas is relatively less than that of the Meyerhof method.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.58-58
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• 2022

본 연구는 수분최대화방법(Moisture-maximizing method)를 기반으로 PMP(Probable Maximum Precipitation)을 산정하는 방법론을 평가하는 것을 목적으로 수행되었다. 수분최대화 방법은 특정 호우사상의 대기 수분 조건을 극대화하여 PMP 를 산정한다. 여기서, 대기 수분 조건은 대기 표면부터 상층부의 총 수분량으로부터 얻어지는 가강수량(Precipitable water, PW)으로 표현된다. PW 는 라디오존데로부터 직접 관측 및 수집되지만, 장기간 수집이 어렵고, 수집된 자료는 다수의 이상치 및 결측치를 포함한다. 이에 따라, WMO(World Meteorological Organization)에서는 표면 이슬점을 이용하여 위단열 가정(Pseudo-adiabatic assumption)하에PW 를 간접적으로 산정하는 방법론을 기반한 PMP 산정을 권고한다. 본 연구는 일본의 다수의 지역을 대상으로 실제 PW 를 이용하는 방법과 표면 이슬점을 이용하는 방법을 기반으로 산정된 수분최대화방법의 변수들의 편차를 분석하였다. 그 결과, 따듯한 기후 특성을 나타내는 일본의 남부지역은 두 방법의 편차가 매우 작았지만, 추운 기후 특성을 나타내는 일본의 북부지역은 표면 이슬점으로 산정된 PW 가 실제 PW 에 비해 과소 산정되어 PMP 를 과대 산정시켰다. 특히, 이불확실성은 호우 발생 시 표면 이슬점이 18℃ 이하일 때, 두드러지게 나타났다. 본 연구는 이불확실성을 밝히기 위해 실제 라디오존데로부터 관측된 대기 상층부의 대기 프로파일 검토하였다. 그 결과, 표면에서 가까운 대기 상층부의 위치에서 불규칙적으로 이슬점이 증가하는 패턴을 나타냈지만, 위단열 가정은 이를 묘사하기 어려웠다. 이는 결국 실제 PW 에 비해 이슬점을 이용하여 산정된 PW 가 과소 산정되는 결과로 이어졌다. 결과적으로, 호우 발생 시 표면 이슬점이 18℃ 이하로 낮은 지역에서 산정된 PW 를 적용하는 수분최대화방법으로 산정된 PMP 는 낮은 신뢰도를 나타낸다.