• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.375-389
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• 2019

Sample attrition according to a long-term tracking reduces the representativeness of the sample data in a panel study. Most panel surveys in South Korea and other countries have prepared response adjustment weights in order to solve problems regarding representativeness due to sample attrition. In this paper, we divided the panel data into continuous response group and non-continuous response group according to response patterns and considered a weighting adjustment method to reduce the bias of the non-continuous response group. A simulation indicated that the proposed composite estimation type weighting method, which reflected the characteristics of non-continuous response groups, could be more efficient than other weighting methods in terms of reducing non-response bias. As a case study, the proposed methods are applied to the Korean Longitudinal Study of Ageing (KLoSA) data of the Korea Employment Information Service.

• Journal of the Korean GEO-environmental Society
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• v.20 no.1
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• pp.35-42
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• 2019

Various development works inevitably increase cutting slopes due to land use, and many of trails managed by different authorities are being deteriorated by long-term weathering. Collapse of slopes causes unavoidable damage of property and loss of lives because of its uncertainty and difficulty in predicting its occurrence. In order to overcome the unavoidability, America, Japan, and several European nations analyze the kinetic energy and moving distance when rocks of upper slope move along the inclined plane, via field tests and computerized interpretation of the test results. Also, they are making efforts to develop measures with which the kinetic energy of the rocks moving along the slope is absorbed and fails to reach to specific structures. However, domestic researches just focus on fragmentary prediction of rockfall using existing programs, and there have been few approaches to identify interpretation methods appropriate for domestic cases or determination of parameters. In this context, we in this study defined rockfall types and affecting factors and analyzed effects of parameters using a general-purpose rockfall simulation program to understand principles of rockfall and to estimate effects of various parameters.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.411-434
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• 2022

In deep geological disposal concept for spent nuclear fuel, it is well-known that rock mass at near-field experiences the thermal-hydraulic-mechanical (THM) coupled behavior. The mechanical properties of rock changes during the coupled process, and it is important to consider the changes into the analysis of numerical simulation and in-situ tests for long-term stability evaluation of nuclear waste disposal repository. This report collected the previous studies on indirect coupled behaviors of rock. The effects of water saturation and temperature on some mechanical properties of rock was considered, while the change in hydraulic conductivity of rock due to stress was included in the indirect coupled behavior.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.108-116
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• 2022

Accurately predicting wartime resources requirements and preparing war supplies in peacetime is an important task that can determine the outcome of the war by guaranteeing the duration of the operation. The wartime warship damage rate is a measure of estimating the battle damage of our warships in the process of performing battles to achieve the war goal. In the previously studied wartime warship damage rate estimation method, when damage occurs, long-term repair is required due to the complexity and specificity of the ship structure. Only the case of a complete defeat at the level of sinking was defined as a damage, and even if it was impossible to perform a maritime operation mission, it was not estimated as a damage if the level of sinking was not reached. Therefore, in order to improve the reliability of the wartime warship damage rate, the equipment damage assessment level can be estimated based on the warhead weight of the threat weapon system, the vulnerability rate of the warship's equipment, and the warship's hull. In the future, it is expected that the estimation methodology proposed in this study will be used as a simulation logic when developing a model for analyzing the wartime resources requirements for the warship's equipment and hull.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.45-52
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• 2022

In the field of UAM, which is attracting attention as a next-generation transportation system, technology developments for using UAVs have been actively conducted in recent years. Since UAVs adopted with these technologies are mainly operated in urban areas, it is imperative that accidents are prevented. However, it is not easy to predict the abnormal flight state of an UAV causing a crash, because of its strong non-linearity. In this paper, we propose a method for predicting a flight state of an UAV, based on a CNN-LSTM hybrid model. To predict flight state variables at a specific point in the future, the proposed model combines the CNN model extracting temporal and spatial features between flight data, with the LSTM model extracting a short and long-term temporal dependence of the extracted features. Simulation results show that the proposed method has better performance than the prediction methods, which are based on the existing artificial neural network model.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.2
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• pp.143-149
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• 2022

PCS needs to freely switch AC and DC to connect the battery, external AC loads and renewable energy in both directions for energy efficiency. Whenever converting happens, power loss inevitably occurs. Minimization of the power loss to save electricity and convert it for usage is a very critical function in PCS. PCS plays an important role in the ESS(Energy Storage System) but the importance of stabilizing semiconductors on PCB(Printed Circuit Board) should be empathized with a risk of failure such as a fire explosion. In this study, the temperature variation inside PCS was reviewed by cooling fan on top of PCS, and the vibration characteristics of PCS were analyzed during truck transportation for reliability of the product. In most cases, a cooling fan is mounted to control the inner temperature at the upper part of the PCS and components generating the heat placed on the internal aluminum cooling plate to apply the primary cooling and the secondary cooling system with inlet fans for the external air. Results of CFD showed slightly lack of circulating capacity but simulated temperatures were durable for components. The resonance points of PCS were various due to the complexity of components. Although they were less than 40 Hz which mostly occurs breakage, it was analyzed that the vibration displacement in the resonance frequency band was very insufficient. As a result of random-vibration simulation, the lower part was analyzed as the stress-concentrated point but no breakage was shown. The steel sheet could be stable for now, but for long-term domestic transportation, structural coupling may occur due to accumulation of fatigue strength. After the test completed, output voltage of the product had lost so that extra packaging such as bubble wrap should be considered.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.212-212
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• 2023

비점오염원관리와 같이 장기적인 유역 관리 계획에서 유역 내 오염원 평가는 정말 중요하다. 유역 내 오염원 평가는 강우 유출에 의한 비점오염 발생원이 어디서 얼마나 발생시키는지에 대한 정량적인 조사가 필요하다. 유역 내의 오염원에 대한 정량적인 조사는 많은 비용과 시간이 필요하다. 이러한 비용과 시간을 줄이기 위해 유역단위 수리 수문 모델을 사용하고 있다. 유역단위 수리수문 모델은 HSPF (Hydrological Simulation Program in Fortran), SWAT (Soil and Water Assessment Tool), L-THIA ACN-WQ(The Long-term Hydrologic Impact Assessment Model with Asymptotic Curve Number Regression Equation and Water Quality model)등 다양한 모델이 사용되고 있다. 하지만 유역 모델을 통한 모의는 다양한 연산 과정을 진행하여 모의까지 많은 시간이 필요하다는 단점이 있다. 이에 따라 데이터 기반 모델링 기법(머신러닝/딥러닝)을 이용한 유출 및 수질 예측 연구가 많이 이루어지고 있다. 단순 머신러닝/딥러닝 기반 모델링 기법은 점(최종유출구)에서의 예측만 가능하여 최적관리 기법 적용 등과 같은 유역관리 방안을 적용하기 힘들다는 문제점이 있다. 따라서 본 연구에서 머신러닝/딥러닝을 통해 일부 수문 프로세스를 대체하고 소유역별 하도추적 기법을 연계하여 유량 및 수질 항목들의 모의가 가능한 하이브리드 모델을 개발하였다. 이는 머신러닝/딥러닝이 유역 모델의 일부 연산 과정을 대체하여 모의시간이 빠르며, 기존 머신러닝/딥러닝 예측 모델에서 평가가 어려웠던 유역 관리 방안 및 최적관리기법 적용 평가에도 활용이 가능할 것으로 판단이 된다.

• Computers and Concrete
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• v.31 no.5
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• pp.405-417
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• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.