• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.11-11
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• 2001

기존의 웨이브릿 기반 프랙탈 압축 방법은 전 영역에 대하여 최적의 정의역을 탐색하므로, 부호화 과정에서 많은 탐색시간이 소요되는 단점이 있다. 그래서 본 논문에서는 웨이브릿 변환영역에서 SAS(Self Affine System) 기법과 다중 스케일 인자를 이용한 웨이브릿 변환 기반 프랙탈 영상 압축 방법을 제안한다. 웨이브릿 기반 영역에서 정의역과 치역을 구성하고, 각각의 치역 블럭에 대해 모든 정의역 블럭을 탐색하는 것이 아니라, 정의역 탐색과정이 필요 없는 SAS 기법을 도입하여 공간적으로 같은 위치에 있는 상위 레벨 블록을 정의역으로 선택한다 그래서 부호화 과정에서 곱셈 계산량을 감소시켜 고속 부호화를 가능하게 한다. 그리고 SAS 기법의 단점인 화질이 떨어지는 단점을 개선하기 위해, 각 레벨별로 서로 다른 스케일 인자를 사 용하여 화질을 개선한다. 그 결과 화질에는 영향을 미치지 않고 부호화 시간과 압축률이 개선되고, 점진적 전송이 가능한 알고리듬을 제안한다.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.450-457
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• 1997

저출력/정지시의 사고가 노심손상에 미치는 영향이 작지 않은데 비하여, 그 영향에 대한 평가 체계는 확립되어 있지 않다. 특히 인적오류가 차지하는 비중이 크며 이에 따라 인적오류를 평가할 수 있는 절차를 마련하여야 한다. 본 논문에서는 전출력시의 체계를 기반으로 저출력/정지시의 특성을 고려하여 인적오류를 평가할 수 있는 체계인 SEPLOT (Systematic Evaluation Procedure for LP&S Operation Tasks)을 개발하였다. SEPLOT에서는 영향도(Influence Diagrams)를 이용하여 인적오류에 영향을 주는 수행특성인자들을 동시에 고려함으로써 수행특성인자들 사이의 의존성 평가를 가능하게 하였고, 저출력/정지시의 특성을 반영할 수 있도록 절차서의 질, 인간-기계 연계, 다중 인간행위 사이의 의존성 등의 수행특성인자들이 중요하게 고려되도록 하였다. 이를 이용하여 참조 원전의 저출력/정지시 대표적인 작업인 소외전원상실사고 대응 작업 중 하나인 일차 충전 및 유출 운전(Primary Feed and Bleed)에 대하여 평가함으로써, 저출력/정지시 다른 여러 가지 수행작업들에 대한 인적오류에도 개발된 체계가 적용 가능함을 보여 주었다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.231-237
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• 2010

이상기후에 의한 집중호우나 태풍의 영향으로 예전에는 기록이 없었던 백두대간과 전국 국립공원의 자연사연에서 산사태가 많이 발생하고 있으며 특히 지형이 험준하고 고도가 높은 지리산의 경우, 다른 국립공원에 비해서 그 발생빈도가 높게 나타난다. 본 연구에서는 지리산 북쪽지역으로 경상남도 함양군 마천면과 전라북도 남원시 산내면에 걸쳐서 발생한 산사태를 중심으로 산사태를 발생시키는 영향인자를 GIS와 원격탐사를 이용하여 분석하였다. 먼저 산사태 발생 지역의 지형특성을 분석하였고 산사태 발생과 산사태 발생에 영향을 끼친 인자들의 상관관계를 알아보기 위해서 빈도비를 사용하였으며 가중치를 도출하기 위해서 다중 회귀분석을 실시하였다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.1
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• pp.89-94
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• 2014

The high Young's modulus and tensile strength of carbon nanotubes has attracted great attention from the research community given the potential for developing super-strong, super-stiff composites with carbon nanotube reinforcements. Over the decades, the strength and stiffness of carbon nanotube-reinforced polymer nanocomposites have been researched extensively. However, unfortunately, such strong composite materials have not been developed yet. It has been reported that the efficiency of load transfer in such systems is critically dependent on the quality of adhesion between the nanotubes and the polymer chains. In addition, the waviness and orientation of the nanotubes embedded in a matrix reduce the reinforcement effectiveness. In this study, we carried out performed micromechanics-based numerical modeling and analysis by varying the geometry of carbon nanotubes including their aspect ratio, orientation, and waviness. The results of this analysis allow for a better understanding of the load transfer capabilities of carbon nanotube-reinforced polymer composites.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.181-192
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• 2015

The aim of this study is to obtain optimum process condition for using two tower distribution to recycle the waste Propylene Glycol Monomethyl Ether Acetate (PGMEA) that is formed after washing LCD. The optimum process condition for the content of PGMEA, which is dependent variable, at 1st distillation was calculated according to Bottom temperature (BTM temperature), Reflux amount, Feed amount, Feed temperatures, and the optimum process conditions and optimum factors for the content of PGMEA at 2nd distillation according to Bottom temperature (BTM temperature), Reflux amount, Feed amount, Feed temperatures. At 1st distillation, Reflux amount, Feed amount, and Feed temperature are significant variables. However, it is found that the BTM temperature range is not significant in the range of process condition used in this study. The optimum process conditions are based on $5700{\ell}$ of Feed amount, $2500{\ell}$ of Reflux amount, $165^{\circ}C$ of BTM temperature, and $130^{\circ}C$ of Feed temperature. For the this condition, the predicted content of PGMEA was calculated as 92.12~94.62%. Significant factors at 2nd distillation are Reflux amount, Feed amount, and BTM temperature. Multicollinearity is between Reflux amount and BTM temperature. BTM was omitted in the multiple regression equation because there is a strong positive correlation between Reflux amount and BTM temperature. Base on $199^{\circ}C$ of BTM temperature, The optimum process conditions are based on $4275{\ell}$ of Feed amount, $6200{\ell}$ of Reflux amount and $130^{\circ}C$ of Feed temperature. In this condition, the predicted content of PGMEA was calculated as 99.0~99.5%.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.39-39
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• 2020

Low-flow 하천에서의 최저수위를 나타내는 지표이다. 일반적으로 유황곡선의 갈수량(Q355)를 대표적으로 사용한다. Low-flow는 물 공급 관리 및 계획, 관개용수, 생태계등 다양한 분야에 영향을 미친다. 이러한 Low-flow를 산정하기 위해서는 충분한 기간의 유량자료가 필요하다. 하지만 국토의 70%가 산지지형으로 구성되어 있는 우리나라의 경우 국가하천과 1급하천을 제외한 산지유역은 수위관측소가 부재하거나 결측으로 인해 자료가 충분하지 않아 Low-flow분석에 한계가 있다. 이에 과거에는 미계측지역의 갈수량을 예측하기 위해서 다중회귀분석, ARIMA 모형 등 다양한 기법을 사용하였지만, 최근들어 머신러닝 모형의 수요가 증가하고 있다. 이에 본 연구에서는 새로운 패러다임에 맞는 머신러닝 기법인 DNN기법을 사용하고자 한다. DNN기법은 ANN기법의 단점인 학습과정에서 최적 매개변수값을 찾기 어렵고, 학습시간이 느린 단점을 보완한 방법이다. 따라서 본연구에서는 머신러닝 기법인 DNN기법을 통해 미계측지역에 적용 가능한 지역화 Low-flow indices를 산정하고자 한다. 먼저, Low-flow에 영향을 미치는 인자들을 수집하고 인자들간의 상관분석, 다중공선성 분석을 통해 통계적으로 유의한 변수를 선정하여, 머신러닝 모형에 입력자료를 구축하였다. 또한 기존의 갈수량 예측기법인 다중회귀분석 결과와 비교하여 머신러닝 기법의 효용성을 검토하였다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.467-474
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• 2012

Human diseases and livestock economic traits are not typically the result of variation of a single genetic locus, but are rather the result of interplay between interactions among multiple genes and a variety of environmental exposures. We have used linear regression model for adjusted environmental effects and multifactor dimensionality reduction (MDR) method to identify gene-gene interaction effect of statistical model in general. Of course, we use 5 SNPs (single uncleotide polymorphism) which were studied recently by Oh et al. (2011). We apply the MDR (multifactor demensionality reduction) method on the identify major interaction effects of single nucleotide polymorphisms responsible for economic traits in a Korean cattle population.

• Journal of Korean Society of Forest Science
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• v.90 no.6
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• pp.683-691
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• 2001

This study aimed to clarify the influencing factors of the mesopore ratio on a pore geometry of surface soil in mixed stands as an index of the water retention capacity. Twenty four factors including site conditions and soil properties were analyzed by spss/pc+ for the data collected from during March to October of 1995. The factors influencing the mesopore ratio(pF2.7) on the surface soil were as follows; mesopore ratio(pF2.7) on the B horizon soil, under vegetation coverage, organic matter contents of surface soil and F layer depth. And influencing factor on the ratio of mesopore in the soil surface was correlated with surface soil hardness and depth of 10cm soil hardness shows high negative significance. Also, multiple regression equations for mesopore ratio of the mesopore ratio of B horizon soil and organic matter contents shows high significance($R^2$; 0.84).

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.585-596
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• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.