• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.41-46
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• 2014

Bus service is easily adjusted to accommodate the changed demand. Despite the flexibility of that, its relocation should overcome the following problems: first, Bus line rearrangement should consider the balance between the demand and the supply to enhance the transit equity among the users scattered around the area that supply against demand imbalances. Second, the existing demand analysed is to crude since the demand was analysed based on TAZ. mainly based on the Dong unit. Utilization of the GWR and GIS-T data can resolve the problem. In this paper, the limitation of the conventional transit demand analysis model is overcome by deploying the GWR model which identifies the transit demand based on the geographic relation between the service location and those of the users. GWR model considers the spatial effect of the bus demand in accordance with the distance to the each bus stops using SCD(Smart Card Data) and BIS(Bus Information System). This demand map was then superimposes with the existing bus route which identified the areas where the balance between demand and supply is severly skewed. since the analysis was computed with SCD and BIS at every bus stops. the shortage and surplus of bus service of entire study area could computed. Further. based on this computational result and considering the entire bus service capacity data. Bus routes optimization from the oversupplied areas to the undersupplied area was illustrated thus this study clearly compared the benefits the GIS.

• Korean Chemical Engineering Research
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• v.53 no.4
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• pp.450-456
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• 2015

In the present study, modelling and optimization of ethanol-benzene separation process were performed using pressure-swing distillation. Order to obtain a reliable results, vapour-liquid equilibrium (VLE) experiments of ethanol-benzene binary system were performed. The parameters of thermodynamic equation were determined using experimental data and the regression. The pressure-swing distillation process optimization was performed to obtain high purity ethanol and high purity benzene into a low-high pressure columns configuration and a high-low pressure columns configuration. The heat duty values of the reboiler from simulation were compared, and the process was optimized to minimize the heat duty.

• Applied Chemistry for Engineering
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• v.32 no.5
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• pp.574-580
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• 2021

The main goal of this study was to find an optimal surfactant mixture composition for the development of the best performing cleansing products. Three different surfactants including sodium cocoyl alaninate (SCoA), cocamidopropyl betaine (CPB), and decyl glucoside (DG) were selected, which showed excellent properties in detergency, foaming height, and contamination rate through preliminary experiments. The experiments by simplex centroid design matrix for surfactant mixtures were performed, and the regression analysis was conducted with the experimental data. Surface response model equations, which is statistically significant (p < 0.05), were obtained. The optimal composition of the surfactant mixture was also determined as SCoA (0.22), CPB (0.78), and DG(0.00) from simultaneous optimization of three response variables.

• Annual Conference of KIPS
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• 2024.10a
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• pp.25-28
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• 2024

본 논문에서는 LiDAR 데이터와 스테레오 이미지를 융합하여 고품질 3D 표현을 생성하는 새로운 접근 방식을 제안한다. 제안하는 방법은 LiDAR 포인트 클라우드와 스테레오 비전을 통해 3D 포인트를 추출하는 것에서 시작하며, 이후 스테레오 비전 포인트 클라우드의 스케일을 LiDAR 스케일에 맞추는 조정 과정을 거친다. 스케일이 조정된 스테레오 포인트와 LiDAR 데이터를 초기 융합하여 두 가지 모달리티의 장점을 모두 활용한 포괄적인 포인트 클라우드를 생성한다. 융합된 포인트 클라우드를 정제하기 위해, DBSCAN과 같은 클러스터링을 통한 노이즈 제거와 포인트 그룹화, 그리고 LiDAR 데이터를 기준으로 스테레오에서 추출한 포인트들을 정밀하게 맞추기 위한 회귀 모델을 결합한 하이브리드 기법을 도입한다. 정제된 포인트 클라우드는 3D Gaussian Splatting 초기화를 위한 기초로 사용되며, 각 포인트를 초기 가우시안 값으로 설정하고 다양한 뷰포인트에서의 렌더링 결과를 바탕으로 가우시안 파라미터를 최적화한다. 최적화된 3D 가우시안을 활용하여 다양한 시점에서 장면을 렌더링하고, 이를 통해 연속적이고 풍부한 3D 장면 표현을 생성한다. 본 연구는 일반적인 새로운 뷰 합성(general novel view synthesis) 문제에 대한 중요한 개선을 달성하여, 컴퓨터 비전, 자율주행, 가상현실과 같은 분야에서의 응용 가능성을 보여준다.

• Food Engineering Progress
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• v.15 no.2
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• pp.97-115
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• 2011

A review about the application of response surface methodology in the optimization of food technology is presented. The theoretical principles of response surface methodology and steps for its application are described. The response surface methodologies : three-level full factorial, central composite, Box-Behnken, and Doehlert designs are compared in terms of characteristics and efficiency. Furthermore, recent references of their uses in food technology are presented. A comparison between the response surface designs (three-level full factorial, central composite, Box-Behnken and Doehlert design) has demonstrated that the Box-Behnken and Doehlert designs are slightly more efficient than the central composite design but much more efficient than the three-level full factorial designs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.203-206
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• 2002

동적 시스템의 동정은 시스템의 관측된 데이터를 가지고 동적 모델의 수학적 모델을 찾는 문제를 다루는 것이다. 기존의 고전적인 방법으로는 차분 방정식(ARX 또는 ARMAX) 또는 상태 공간 표현에 관한 계수들을 추정하기 위해서 회귀 기법 등을 사용하였다. 그러나 이러한 고전적인 방법들은 파라미터가 비선형이고, 실세계 문제에서 모델링 오차나 측정 잡음을 수반하게 되면 탐색의 어려움을 가지게 된다. 따라서 이러한 문제점을 극복하고자 퍼지 이론이나 신경망 이론 둥이 이용되었으나 본 논문에서는 비선형 동적 시스템의 파라미터 동정에 최근 복잡한 최적화 문제를 해결하는 도구로 점점 관심을 받고 있는 유전 알고리즘을 동정 알고리즘으로 제안하고, 비선형 동적 시스템의 파라미터 동정에 유전 알고리즘을 응용한 몇 가지 예를 제시하고자 한다.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Applied Chemistry for Engineering
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• v.24 no.3
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• pp.299-304
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• 2013

A 17-run Box-Behnken design was used to optimize the synthesis conditions of a monolithic sorbent. The effects of the amount of monomer (mL), crosslink (mL) and porogen (mL) were investigated. The experimental data were fitted to a second-order polynomial equation by the multiple regression analysis and examined using statistical methods. The adjusted coefficient of determination ($R^2$) of the model was 0.9915. The probability value (p < 0.0001) demonstrated a high significance for the regression model. A mean amount of polymer as 2120.15 mg was produced under the following optimum synthesis conditions: the optimized volumes of monomer, crosslink and porogen are 0.30, 1.40, and 1.47 mL, respectively. This was in good agreement with the predicted model value.

• Korean Journal of Food Science and Technology
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• v.29 no.4
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• pp.752-757
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• 1997

Crude papain extracted at optimum condition was purified with an ethanol precipitation method. Four factors of protein recovery method were optimized by response surface methodology (RSM) and the function was expressed in terms of a quadratic polynomial equation. Adequacy of the model equation for optimum response values was tested and optimum conditions of protein recovery were 38.2 mg/mL of protein, ethanol concentration of 40% and precipitation temperature of $-8^{\circ}C$. The experimental value (68.97%) for recovery yield was closed to the predicted value (77.28%) under these conditions.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.41-41
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• 2018

하천과 저수지의 수질을 예측하고 관리하는데 수리 수질예측모형이 널리 활용되고 있다. 수질예측모형은 유역이나 수체 내의 오염물질 이동경로나 농도를 수치해석 방법으로 계산하여 사용자가 필요로 하는 지점과 시점에서의 수질자료 생산하는데 활용되고 있다. 수질예측모형은 검 보정을 통해 정확도를 확보하며, 정확도의 확보를 위해서는 높은 수준의 전문성을 필요로 한다. 특히 시행착오법으로 모형을 보정하는 경우 많은 시간과 노력을 필요로 하게 되며, 보정계수를 과대 혹은 과소로 모형에 적용하는 오류를 범하기 쉽고 모델러의 주관이 관여되기 쉽다. 그래서 본 연구에서는 CE-QUAL-W2모형의 조류항목에 대한 모형 보정을 위하여 Chl-a와 남조류세포수에서 주로 활용되고 있는 보정계수에 대한 민감도 분석 결과를 토대로 매개변수별 모의결과 변화율을 산정하였으며, 시기적 경향성을 재현하기 위해 Ensemble-Bagging 기법과 머신 러닝 기법을 적용하여 모형 구동횟수를 최소화 할 수 있는 방법으로 구성하였다. Chl-a를 보정하기 위한 매개변수는 9개를 선정하였으며, 규조류, 남조류, 녹조류에 총 27개 매개 변수를 민감도 분석으로 도출 한 후 예상 변화율 대비 이벤트별 모의치와 실측치 간 %difference가 유사하도록 매개변수를 조정하였다. 또한 각 이벤트 조합의 매개변수 빈도수와 매개변수별 예상변화율, 시기적 조류특성을 고려하여 가중치를 도출하였으며, 1회 보정에 맞춰 Chl-a 모델 실행결과를 %difference로 평가한 후 "good"등급을 만족할 때까지 반복 적용하였다. 남조류세포수의 경우 Chl-a에 맞춰 매개변수 최적화 이후 남조류세포수 농도를 세포수로 환산하기 위한 CACEL에 대해 머신러닝 기법을 적용하였으며, CACEL 추정변화율 회귀식에 따라 평가 한 후 %difference "good"등급 이상을 만족할 때까지 반복 수행하는 방법을 적용하였다. 본 연구에서는 수질예측모형의 정확도를 확보하기 위하여 최적화 기법을 적용하였으며, 이를 통해 모형을 보정하는 과정에서 요구되는 시간과 노력을 줄일 수 있도록 하였으며, Ensemble기법과 머신러닝 기법을 적용하여 모형보정계수 적용에 객관성을 확보할 수 있도록 하였다.