• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.117-123
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• 2006

최근 들어 전통적인 (가속)수명시험으로도 고 신뢰도 제품의 신뢰도 평가가 힘들므로 제품의 성능열화를 관측하여 수명 정보를 추정하는 열화 시험에 대한 관심이 증대되고 있다. 본 논문은 대수정규분포를 따르는 확률계수 열화율 모형 하에서 분포 모수 및 수명분포의 분위수를 추정하는 세 가지 통계적 분석법(근사적, 해석적, 수치적 방법)의 통계적 성능을 비교하였다. 즉, 다양한 수치실험상황 하에서 모형에 포함되는 (측정)오차의 영향을 고려하여 세 방법의 우월성을 조사하였다.

• International journal of advanced smart convergence
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• v.1 no.1
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• pp.6-13
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• 2012

An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.344-347
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• 2007

For modeling the non-periodic and randomly scattered powder particles, the quasi-random multi-particle array is introduced. The multi-scale process simulation, which enables to formulate a regression model with a response surface method, is performed by employing a homogenization method. The size of ${Al_2}{O_3}$ particle, amplitude of cyclic compaction pressure, and friction coefficient are considered as optimal process parameters. The optimal conditions of process parameters providing the highest relative density are finally found by using the grid search method.

• 연구논문집
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• s.22
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• pp.39-46
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• 1992

A dynamic design process was proposed for the design of the secondary suspension characteristics of a magnetically levitated vehicle(MAGLEV). It is based on a ride quality-secondary stroke trade-off. For the vertical vibration analysis, a magnetically levitated vehicle was simplified as 2 d.o.f. linear model, and FRA's class-6-track irregularities were considered as exciting disturbances. The optimum value of airspring stiffness and damping coefficient for the secondary suspension of a prototype MAGLEV was determined using this proposed design process.

• Journal of Agricultural Extension & Community Development
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• v.25 no.4
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• pp.175-184
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• 2018

There are two main types of agricultural extension projects in Korea, the impacts and routes of each type on farm income are different. This paper empirically studies the impact of agricultural extension to farms' income, using Multi-level production function considering time lag. It is found that direct type of extension has positive effect to farms' income. Also indirect type on income is significant only when the level of education is high. Due to the characteristics of Korean agricultural structure, the technical level of farm is greatly influenced by the government's R&D investment and technology guidance. The result implies that indirect type of extension that take into account the educational level of farms should be emphasized for long-term technological advances.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.1-6
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• 2022

This computational investigation of micro-sized particle dispersion concerns the fume particle contamination over target surface in high-precision laser line machining process of semiconductor and display device materials. Employing the random sampling based on probabilistic fume particle generation distributions, the effects of sphericity for nonspherical fume particles are analyzed for the fume particle dispersion and contamination near the laser machining line. The drag coefficient correlation for nonspherical particles in a low Reynolds number regime is selected and utilized for particle trajectory simulations after drag model validation. When compared to the corresponding results by the assumption of spherical fume particles, the sphericity of nonspherical fume particles show much less dispersion and contamination characteristics and it also significantly affects the particle removal rate in a suction air flow patterns.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.87-94
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• 2019

This study deals with the reliability analysis approach of the temporary structure that can consider the uncertainty in rotational stiffness at the joints of the members, for which the semi-rigid connections are modelled as rotational spring and its coefficient is treated as a random variable following uniform distribution. In addition, this study introduces a computational procedure of the effective length coefficient for more accurate buckling load according to connection conditions of the supporting members attached to the joint. From the results of this study, it can be seen that the failure probability of the joint-hinge model (Case 1) presented in the design standard is higher than that of the practical model (Case 5) considering the rotational stiffness at the joints. This implies that the design standard leads to a conservative design of the temporary structure. The results also confirmed that the failure probability of the vertical member, i.e., the most critical member, can be further reduced when the base connection is provided with a fixed end. The comparative results between FORM, SORM and MCS further demonstrated that FORM can have a high level of numerical efficiency while ensuring the accuracy of the solution, compared with SORM and MCS. Based on these results, the proposed approach can be used as an accurate and efficient reliability analysis method of the three dimensional temporary structure.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.6
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• pp.488-496
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• 2019

The estimation of block erection work time at a dock is one of the important factors when establishing or managing the total shipbuilding schedule. In order to predict the work time, it is a natural approach that the existing block erection data would be used to solve the problem. Generally the work time per unit is the product of coefficient value, quantity, and product value. Previously, the work time per unit is determined statistically by unit load data. However, we estimate the work time per unit through work time coefficient value from series ships using machine learning. In machine learning, the outcome depends mainly on how the training data is organized. Therefore, in this study, we use 'Feature Engineering' to determine which one should be used as features, and to check their influence on the result. In order to get the coefficient value of each block, we try to solve this problem through the Ensemble learning methods which is actively used nowadays. Among the many techniques of Ensemble learning, the final model is constructed by Stacking Ensemble techniques, consisting of the existing Ensemble models (Decision Tree, Random Forest, Gradient Boost, Square Loss Gradient Boost, XG Boost), and the accuracy is maximized by selecting three candidates among all models. Finally, the results of this study are verified by the predicted total work time for one ship among the same series.