• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• 반도체디스플레이기술학회지
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• 제23권1호
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• pp.7-11
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• 2024

In the era of the 4th Industrial Revolution, new technologies such as artificial intelligence, big data, cloud, Internet of Things, and blockchain are being developed and applied to new industries. Blockchain has the characteristics of decentralization, security, and transparency, so it can serve as a core technology for developing new growth industries. Blockchain is provided as BaaS (Blockchain as a Service), but it is not easy for users who are introducing or building blockchain to choose BaaS. In this study, we identify evaluation factors and develop a decision-making model using fuzzy theory and AHP for BaaS selection. Eventually we aim to help companies choose the best BaaS and develop and commercialize blockchain-based services by developing a new decision-making model for BaaS selection.

• 인터넷정보학회논문지
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• 제15권3호
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• pp.109-116
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• 2014

본 논문에서는 분류 성능을 향상하기 위해서 Takagi-Sugeno(T-S) 퍼지 모델 기반의 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions; NEWFM)을 이용한 새로운 인스턴스 선택을 제안하였다. 제안하는 인스턴스 선택은 T-S 퍼지 모델에서의 가중 평균 역퍼지화와 통계학에서 사용하는 정규분포의 신뢰구간과 같은 구간 선택을 이용하여 인스턴스를 선택하였다. 제안하는 인스턴스 선택의 분류 성능을 평가하기 위해서 인스턴스 사용 전/후에 따라서 분류 성능을 비교하였다. 인스턴스 사용 전/후에 따른 분류 성능은 각각 77.33%, 78.19%로 나타났다. 또한 인스턴스 사용 전/후에 따른 분류 성능 간에 차이점을 보여주기 위해서 통계학에서 사용하는 맥니마 검정을 사용하였다. 맥니마 검정의 결과로 유의 확률이 0.05보다 적게 나오므로 인스턴스 선택의 분류 성능이 인스턴스 선택을 하지 않는 경우의 분류 성능보다 우수함을 확인 할 수가 있었다.

• 한국병원경영학회지
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• 제27권3호
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• pp.1-14
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• 2022

Purposes: This purpose of this study was to check how much the online word of mouth influences on customer's hospital selection according to Kano's model. Methodology: Kano classified the attributes that affect customer's satisfaction into attractive, one-dimensional, indifferent, must-be, and reverse attributes. Among them, attractive and one-dimensional attributes make up the largest portion in hospital selection. Based on this, the influence of positive or negative online reviews on the selection of hospitals was investigated. Differentiated service was selected as the attractive attributes, and a kind, sufficient explanation was selected as the one-dimensional attributes. Then a questionnaire was conducted how much the positive or negative online reviews influence on hospital selection, respectively. It was conducted from August 7 to September 7, 2021 for medical consumers in their 20s and older who have used medical services for the past 3 years, and the final 142 questionnaires were analyzed. All data was analyzed by chi-square and two-way ANOVA using SPSS ver 25.0. Findings: The results showed that, in one-dimensional attributes, the difference between positive and negative reviews was not statistically significant, but in attractive attributes, positive and negative reviews showed a statistically significant difference. It suggests that positive reviews on attractive attributes had a greater influence on hospital selection. In terms of hospital selection, when the experimental participants were exposed to the positive reviews, the hospital selection ratio did not differ by Kano's attributes, but to the negative reviews it differed. The hospital selection ratio, even after they were exposed to negative reviews, was higher in the attractive attributes than in the one-dimensional attributes. Practical Implication: This study confirmed that hospital selection is influenced differently depending on the Kano's attributes and the direction of the reviews, and suggests that marketers should respond differently to each Kano's attributes when they deal with online reviews of hospitals.

• 수산경영론집
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• 제40권3호
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• pp.147-167
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• 2009

The objective of this study is to analyze the determinants of at-home laver consumption expenditures using the data from a survey of households implemented in 2009. It happened that non-response ratios of monthly expenditures on dry laver and flavored laver among sampled households are 18.8% and 25.6%. Accordingly, this study tries to analyze the determinants affecting at-home laver consumption expenditures by using type II tobit model, one of sample selection models, to deal with sample selection bias caused from non-response data. Analysis results show the age variable positively affects expenditures on dry laver but negatively contributes to expenditures on flavored laver. In addition, the household size, the household's income, the degree of preference for laver have positive relationships with both expenditures. Household size elasticity and income elasticity of the expenditure on dry laver are estimated as 0.220 and 0.251. In the case of flavored laver, these elasticities are estimated as 0.484 and 0.261. Such analysis results can provide information on division of the at-home laver consumption market into groups with high willingness to expense and implementation of detailed marketing strategies to increase at-home laver consumption. The methodology of this study can be applied to consumer preference analysis on other marine products and other analyses on sample with non-response data in the fishery research.

• 대한기계학회논문집A
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• 제33권8호
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• pp.813-818
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• 2009

Design analysis and computer experiments (DACE) model is widely used to express efficiently nonlinear responses in the field of engineering design. As a DACE model, kriging model can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from the global model. The local model is determined by correlation coefficient with the pre-sampled points, where the accuracy and robustness of the kriging model depends on the selection of proper correlation coefficients. Therefore, to achieve the robust kriging model, the range of the correlation coefficients is explored with respect to the degrees of the global model. Based on this study we propose the proper orders of the global model and range of parameters to make accurate and robust kriging model.

• 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.

• 한국조리학회지
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• 제22권8호
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• International Journal of Control, Automation, and Systems
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• 제1권4호
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1099-1107
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• 2017

본 연구에서는 한국고용정보원에서 실시한 "2014년 대졸자 직업이동 경로조사" 자료를 활용하여 대졸자의 임금결정요인을 분석하였다. 일반적으로 임금은 개인의 취업여부와 임금의 크기에 대한 두 가지의 복합적인 정보를 담고 있으나, 많은 선행연구에서는 임금의 크기에 대한 정보만을 활용하여 선형 회귀분석을 수행함으로써 표본선택에 위한 편의 (sample selection bias) 문제가 발생하게 된다. 이런 문제점을 극복하기 위해 본 연구에서는 Heckman의 표본선택 모형을 분석에 활용하였다. 주요 분석 결과를 요약하면 다음과 같다. 먼저 Heckman의 표본선택 모형에 대한 타당성은 통계적으로 유의함을 알 수 있었다. 남자는 여자에 비해서 취업확률과 임금의 크기 모두 통계적으로 유의하게 높게 나타났으며, 연령이 증가하고 부모의 소득이 증가 할수록 취업확률과 임금의 크기 모두 높게 나타났다. 또한 대학만족도가 높아질수록, 그리고 취득한 자격증 수가 증가할수록 취업확률과 임금 모두 증가하는 경향이 있는 것으로 나타났다.