• 한국건설관리학회논문집
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• 제16권1호
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• pp.82-91
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• 2015

최근 전 세계적으로 친환경 및 저탄소에 대한 관심이 증가하고 있으며, 이에 따라 대표적인 친환경연료인 천연가스의 사용량이 급속도로 증가하고 있다. 특히 국내에서도 산업용, 발전용, 가정용으로 사용되는 천연가스 수요증가에 대비하여 도입 물량을 확대하기 위한 전략을 수행하고 있으며, 이에 따른 LNG 저장용량의 확보를 위해 저장기지 증설을 계획하고 있다. 그러나 기존의 LNG 저장기지의 입지선정은 기업의 내부적인 절차나 용역을 통해 수행되어 LNG 및 LNG 저장기지의 특성을 반영하는 데는 미흡하였다. 또한 해외에서도 LNG 저장기지의 입지선정과 관련하여 주요 요인들에 대한 연구가 수행되고 있으나 프로세스 및 모델 등 체계적인 분석은 미흡한 상황이다. 따라서 본 연구에서는 LNG 저장기지의 특성을 고려한 입지선정 모델을 구축하고자 한다. 이를 위해 관련기업의 과거 사례를 분석하여 저장기지 입지선정 과정에서 요구되는 요인들과 기존에 연구되어온 플랜트시설, 공장, 산업단지, 관청청사 등의 입지선정에 대한 요인들을 취합하여 전문가 인터뷰를 실시하였고 최종적으로 47개의 입지선정요인을 도출하였다. 이후 기 수행된 5개 지역(PT지역, IC지역, TY지역, SC지역, BR지역)의 사례에 대한 설문을 기반으로 요인분석, 다중회귀분석을 통하여 지역별 입지선정에 대한 우선순위를 도출하였고 이를 토대로 LNG 저장기지 후보지에 대한 입지선정 모델의 활용 가능성을 검토하였다. 향후 LNG 저장기지의 추가 건설과정에서 본 연구를 기초자료로서 활용한다면, 보다 효과적이고 체계적인 입지선정이 가능할 것으로 기대된다.

• Near Infrared Analysis
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• 제1권1호
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• 산업경영시스템학회지
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• 제40권4호
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• pp.112-119
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• 2017

Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an "if-then" rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers' general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier's performance and eventually influence buyer's production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

• 한국건설관리학회논문집
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• 제20권4호
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• pp.56-68
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• 2019

거푸집 공사는 전체 건축공사의 공사기간 및 공사비 측면에서 상당 부분을 차지하고 있으며, 구조물공사의 품질, 마감 및 설비 등 후속공사에도 직접적인 영향을 미치는 매우 중요한 공정이다. 그러나 국내 건설현장에서의 거푸집 공법선정은 객관적인 데이터를 바탕으로 하지 않고, 현장 실무자의 경험과 직관에 의존하여 이루어지는 경우가 대부분이다. 그 결과, 다수의 현장에서 공기단축, 비용절감 등의 효과가 큰 신공법을 적용하고자 할 경우, 객관적이고 합리적인 의사결정 절차의 부재로 인해 최적 공법선정 시 어려움을 겪을 뿐만 아니라, 현장의 특성을 고려하지 못한 의사결정으로 인해 공기지연, 비용증대 등의 문제가 발생하고 있는 실정이다. 이러한 문제를 해결하기 위해 거푸집 공법선정과 관련한 다양한 선행연구가 이루어졌으나 현장조건이라는 중요한 요인을 고려하지 못해 실제 현장에서 적용하여 활용하기에 다소 한계성이 있었다. 따라서 본 연구는 현장 유형별 조건을 고려한 정량적 거푸집 선정 의사결정 지원 모델 개발을 그 최종 목적으로 한다. 개발된 의사결정 지원 모델은 공사현장에서 거푸집 선정 의사결정시 정량적인 평가 데이터를 제공함으로써, 기존의 경험에 의존하여 결정하던 의사결정 방법보다 신속하고 객관적으로 최적의 거푸집 공사를 선정 할 수 있도록 도울 것으로 사료된다. 또한, 경험이 풍부하지 않은 엔지니어도 본 연구의 의사결정 지원모델을 활용하여 현장의 조건을 고려한 보다 객관적이고 합리적인 의사결정을 내릴 수 있을 것으로 기대된다.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.617-631
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• 2000

This paper is concerned with suggesting a Bayesian method for variable selection in generalized logit model. It is based on Laplace-Metropolis algorithm intended to propose a simple method for estimating the marginal likelihood of the model. The algorithm then leads to a criterion for the selection of variables. The criterion is to find a subset of variables that maximizes the marginal likelihood of the model and it is seen to be a Bayes rule in a sense that it minimizes the risk of the variable selection under 0-1 loss function. Based upon two examples, the suggested method is illustrated and compared with existing frequentist methods.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.299-302
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• 2000

Various types of measuring devices are used for reverse engineering and inspection in different fields of industry such as automotive, aerospace, computer graphics, and home appliance. In order to measure a part easily and efficiently, it is important to select appropriate measuring device considering the characteristics of each measuring machine and part information. In this research, an optimal measuring device selection system using neural networks is proposed. There are two major steps: Firstly, the measuring information such as curvature, normal, type of surface, edge, and facet approximation is extracted from the CAD model. Second, the best suitable measuring device is proposed using the neural network system based on the knowledge of the measuring parameters and the measuring resources. An example of machine selection is implemented to evaluate the performance of the system.

• 산업경영시스템학회지
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• 제33권2호
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• pp.1-8
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• 2010

A vendor selection problem consists of two different kinds of decision making. First one is to choose the best suppliers among all possible suppliers and the next is to allocate the optimal quantities of orders among the selected vendors. In this study, an integration of the technique for order preference by similarity to ideal solution (TOPSIS) and a multi-objective mixed integer programming (MOMIP) is developed to account for all qualitative and quantitative factors which are used to evaluate and choose the best group of vendors and to decide the optimal order quantity for each vendor. A solution methodology for the vendor selection model of multiple-vendor, multiple-item with multiple decision criteria and in respect to finite vendor capacity is presented.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.176-179
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• 2022

In this paper, we consider a sectorized wireless powered sensor network (WPSN), wherein sensor nodes are clustered based on sectors and transmit data to the cluster head (CH) using energy harvested from a hybrid access point. We construct a system model for this sectorized WPSN and find optimal coordinates of CH that maximize the achievable transmission rate of sensing data. To obtain the optimal CH with low overhead, we perform an asymptotic geometric analysis (GA). Simulation results show that the proposed GA-based CH selection method is close to the optimal performance exhibited by exhaustive search with a low feedback overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.