• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.306-306
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• 2019

전지구적 기온상승으로 인해 미래기후의 관한 연구가 중요시 되고 있다. 위와 같은 현상으로 인하여 다양한 기후변화 연구가 진행되고 있다. 미래기후 연구에는 GCM (General Circulation Model) 모의 결과가 이용된다. 격자 자료로 구성된 GCM은 연구 지점으로 지역적 상세화와 연구지역의 관측자료 사이의 편이 보정(bias correction)이 필수적이다. 위와 같은 근거로 편이 보정 방법의 선택은 매우 중요하며 편의 보정의 방법에 따라서 결과가 다르게 도출될 수 있다. 또한 국내외 연구에서는 다양한 상세화 기법과 편이 보정 기법을 분석 및 평가하는 연구가 진행되고 있으며, 편의 기법 중 대표적인 기법인 Quantile mapping과 Random Forest 기법이 있다. Quantile mapping 기법은 GCM의 과거 모의 데이터와의 편이 보정에 있어서 우수하게 나타났으나, GCM 데이터의 미래 예측 기간(2010년~2018년)까지의 데이터에서는 극한 강수를 정량적으로 분석 가능한 Random Forest 기법이 편이 보정 과정에서 성능이 우수할 것으로 판단된다. 본 연구에서는 우리나라 21개 관측소를 기준으로 총 4개의 GCM(GISS, CSIRO, CCSM4,MIROC5)의 과거 기간 자료(1970년~2005년)를 실제 관측소에서 관측된 강수량을 편의 보정하는 방법에 있어서 편의 보정 기법의 성능을 비교한 결과와 GCM 미래 예측 기간 자료(2010년~2018년)에서의 편의 보정 기법의 성능 결과를 비교하였다. 이를 토대로 편이 보정 기법의 결과를 6개의 평가지수를 이용하여 정량적으로 분석하였으며, 다기준의사결정기법인 TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution)를 이용하여 편이 보정기법들의 성능에 있어서 우선순위를 선정하였다. 본 연구에서 편이 보정 방법으로 Quantile mapping 방법을 사용했으며, Quantile mapping의 기법으로는 비모수 변환법(non-parametric transformation)과 분포기반 변환법(distribution derived transformation)이 사용되었다. 또한 머신러닝 방법 중 하나인 Random Forest 방법을 동시에 사용하여 결과를 비교하였다. 또한 GCM 자료가 격자식으로 제공하고 있기 때문에 관측소 강수량도 공간적으로 환산하여야 하는데, 본 연구에서는 역거리 가중치법(inverse distance weighting, IDW) 방법을 이용하였다.

• 토지주택연구
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• 제10권1호
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• pp.19-23
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• 2019

In recent years, many studies have been conducted on smart low carbon cities through the fusion of ICT information technology for the purpose of reducing carbon. In this study, we investigated 13 cities in three continents that implement low-carbon city policies and analyzed the size, economic and social characteristics of each city to identify the degree of dynamic mechanism for carbon reduction. To this end, we quantified the elements of the city and analyzed the basic requirements for low-carbon cities using the TOPSIS method. The study found that most cities were better able to activate institutions and cultural conditions, facilities and functional conditions, and economic and industrial conditions than other engines, and these three were the main forms of power for smart low carbon cities. The results of this study are expected to be used as a basis for suggesting policy recommendations and improvement measures for future smart low carbon cities.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.

• Earthquakes and Structures
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• 제3권5호
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• pp.695-717
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• 2012

Seismic resiliency of new buildings has improved over the years due to better seismic codes and design practices. However, there is still large number of vulnerable and seismically deficient buildings. It is not economically feasible to retrofit and upgrade all vulnerable buildings, thus there is a need for rapid screening tool. Many factors contribute to the damageability of buildings; this makes seismic evaluation a complex multi-criteria decision making problem. Many of these factors are noncommensurable and involve subjectivity in evaluation that highlights the use of fuzzy-based method. In this paper, a risk-based framework earlier proposed by Tesfamariam and Saatcioglu (2008a) is extended using Fuzzy-TOPSIS method and applied to develop an evaluation and ranking scheme for steel buildings. The ranking is based on damageability that can help decision makers interpret the results and take appropriate decision actions. Finally, the application of conceptual model is demonstrated through a case study of 1994 Northridge earthquake data on seismic damage of steel buildings.

• 유통과학연구
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• 제19권12호
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• pp.45-56
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• 2021

Purpose: This study aims to introduce a hybrid MCDM model to support the selection of retail store location. Research design, data, and methodology: The hybrid approach of ANP and TOPSIS was used to address the location selection problem. The ANP technique was employed to compute the weights of the selection criteria, whilst the TOPSIS was used to rank alternatives. The proposed approach was then applied into a fashion company in Vietnam to select the best alternatives to be the retail store. Results: The results showed that Candidate 1 - Hai Ba Trung street is the most appropriate selection for locating retail stores. Conclusions: The proposed approach provides the decision makers with more useful methods than traditional ones. Therefore, the model can be applied to the location selection in all industries. In terms of academic contribution, the selection criteria proposed in the research can devote to the literature in the selection of location along with the concept of distribution channels. Additionally, the research also provides insight and guidelines for firms in making decision on retail store location based on limited resources to avoid the waste of funds. However, the results only answer to the context of Vietnam - a developing country. Thus, future research may be extended to developed countries where have better conditions.

• 유통과학연구
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• 제19권8호
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• pp.25-35
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• 2021

Purpose: This study aims to propose an integrated MCDM model to support the qualified personnel selection in the distribution science. Research design, data, and methodology: The integrated approach of AHP and TOPSIS was employed to address the personnel selection problem. The AHP method was used to define the weights of the selection criteria, whereas the TOPSIS was applied to rank alternatives. The proposed model was then applied into a leading logistics company to select the best alternatives to be the sales deputy manager. Results: The results showed that Candidate 3 is the most qualified personnel for the sales deputy manager position as he is ranked first in the order of preference for recruitment. Conclusions: The proposed model provides the decision makers with more effective and time-saving methods than conventional ones. Therefore, the model can be applied to personnel selection around the world. In terms of theoretical contribution, this study proposes a personnel selection model for choosing the most appropriate candidates. In addition, the study adds to the theory of human resources management and logistics management the full set of personnel selection criteria including education, experience, skills, health, personality traits and foreign language.

• ETRI Journal
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• 제45권3호
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.195-195
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• 2020

이 연구는 기후변화 위험에 노출되어 있는 북한에 대한 잠재증발산량의 미래 변화를 전망하였다. 이를 위해 IPCC AR5의 RCP 기후변화 시나리오로부터 모의된 미래 기온자료를 APCC (APEC Climate Center) Integrated Modeling (AIMS)를 사용하여 25개 관측 지점에 대해서 상세화하여, McGuinness-Borne 방법으로 잠재증발산량을 추정하였다. 6개의 성능 지표와 TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution)로 27개 GCMs 간의 과거 기후 재현성을 비교하여, 관측 지점 규모에서 적정 GCM을 선정하였다. 마지막으로 각 지점에서 선정된 대표 시나리오(representative climate change scenarios, RCCS)로 북한 지역의 잠재증발산량의 미래 변화를 3개의 구간(F1: 2011-2040; F2: 2041-2070; F3: 2071-2100)에서 all CCS(climate change scenario)와 비교하고, 미래 변화를 정량적으로 제시하였다. 그 결과 공간 해상도가 더 높은 GCM이 RCCS로 선정되었으며, 미래로 갈수록 잠재증발산량이 증가하리라 전망되었다. 또한, 여름철 잠재증발산량의 모델 간 변동성은 미래 구간에 따라 점진적으로 증가되었고, 연 평균 증발산량은 과거 기후대비 1.4배(F1), 2.0배(F2) 및 2.6배(F3) 증가하였다.

• 한국융합학회논문지
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• 제9권7호
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• pp.231-240
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• 2018

자동차 산업은 국제유가 상승, 차량가격 상승 등 다양한 위기에 직면해 있다. 정부 규제완화와 더불어 생산 효율성 증가를 위한 경영개선 노력이 필요하다. 본 논문에서는 조달물류 개선을 목표로 실제 회사에서 사용 중인 요인들을 바탕으로 Fuzzy-AHP-TOPSIS를 활용하여 조달물류 평가모델을 구축하였다. G사 자동차 3개 공장을 평가대상으로 Fuzzy-AHP 분석한 결과, 장기 품질문제 해결, 자재결품 정지시간 최소화, 장비사고 방지, 단기 품질문제 해결이 가장 중요한 요인으로 파악되었다. TOPSIS 분석결과 B공장의 조달물류가 가장 잘 이루어지고 있는 것으로 나타났다. 제시된 평가모델을 사용하여 향후 주기적인 조달물류 관련 평가가 가능하며, 이는 자동차 산업 효율화에 기여할 수 있다는 시사점을 갖는다.