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김준홍,진달래,이지선,김수지,손영숙,Kim, Junhong,Jin, Dalae,Lee, Jisun,Kim, Suji,Son, Young Sook 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.6
본 연구에서는 다변량시계열모형인 VAR (vector autoregressive regression)모형에 의하여 금리 스프레드의 시계열예측을 수행하였다. 국내외 거시경제변수들 중에서 교차상관분석 및 그랜져인과 검정을 통하여 상호간에 설명력이 있는 변수들을 추출하여 VAR모형의 시계열변수로 사용하였다. 마지막 12개월의 예측치에 대한 MAPE (mean absolute percentage error)와 RMSE (root mean square error)에 근거하여 모형의 예측력을 단일변량 시계열모형인 AR (autoregressive regression) 모형과 비교하였다. In this paper, we predicted the interest spread using the VAR (vector autoregressive) model. Variables used in the VAR model were selected among 56 domestic and foreign macroeconomic time series through crosscorrelation and Granger causality test. The performance of the VAR model was compared with the univariate time series model, AR (autoregressive) model, in view of MAPE (mean absolute percentage error) and RMSE (root mean square error) of forecasts for the last twelve months.
Fuzzy Service FMEA 및 HOQ 행렬 대수를 이용한 서비스 시스템 설계
김준홍(Junhong Kim) 한국산업경영시스템학회 2012 한국산업경영시스템학회지 Vol.35 No.3
This study proposes an integrated approach that uses both a fuzzy service FMEA (failure mode and effect analysis) and HOQ(house of quality) matrix algebra in designing and improving a service system. The fuzzy service FMEA methodology applies the customer satisfaction to the fuzzy RPN model. We fuzzify only the service satisfaction that consist in two failure factors, intangible service and tangible service, to more effectively assess the customer satisfactions on service encounters. Proposed fuzzy service satisfactions with triangle membership function are defuzzified by using the Fuzzy Inference System, and these are eventually identified the ranks on the potential fail points. HOQ matrices are constructed from cause-effect relationships. It is possible for these relationship matrix to find a linear approximation solution on the engineering attributes. Thus, in order to demonstrate how the proposed methods work, practical sample of the A/S part in S Electronic Co. provides for the ranking of the engineering attributes which has been successfully implemented.
인스타그램 기반의 전이학습과 게시글 메타 정보를 활용한 페이스북 스팸 게시글 판별
김준홍(Junhong Kim),서덕성(Deokseong Seo),김해동(Haedong Kim),강필성(Pilsung Kang) 대한산업공학회 2017 대한산업공학회지 Vol.43 No.3
This study develops a text spam filtering system for Facebook based on two variable categories: keywords learned from Instagram and meta-information of Facebook posts. Since there is no explicit labels for spam/ham posts, we utilize hash tags in Instagram to train classification models. In addition, the filtering accuracy is enhanced by considering meta-information of Facebook posts. To verify the proposed filtering system, we conduct an empirical experiment based on a total of 1,795,067 and 761,861 Facebook and Instagram documents, respectively. Employing random forest as a base classification algorithm, experimental result shows that the proposed filtering system yield 99% and 98% in terms of filtering accuracy and F1-measure, respectively. We expect that the proposed filtering scheme can be applied other web services suffering from massive spam posts but no explicit spam labels are available.