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시계열 분해 데이터를 이용한 LSTM 기법 기반 항공기 수리부속 수요예측 방안 연구
김진섭(Jinseob Kim),황재성(Jaesung Hwang),정재우(Jaewoo Chung) 한국경영과학회 2020 經營 科學 Vol.37 No.2
This paper proposes a new deep learning method called H-LSTM (Hybrid Long and Short Term Memory) in order to improve the demand forecasting system of spare parts for ROKAF (Republic of Korea Air Force) aircraft ‘B’. The existing LSTM has been popularly utilized for forecasting stock price or energy demand since it was known to be appropriate for non-linear time series data forecast. This paper applies the H-LSTM for a demand forecast problem of aircraft spare parts, which shows irregular demand patterns. The H-LSTM that combines the existing LSTM model with time series analysis after the seasonality and trend of demand data are decomposed. Based on a preliminary analysis, the Aircraft spare parts demand pattern shows irregularity as Erratic, Lumpy items of irregular demand characteristics take relatively higher percentages. The accuracy of the new method compared with existing stochastic methods show a higher forecast accuracy than ARIMA or Holt Winters. Therefore, if it is applied for the demand forecast system of ROKAF aircraft spare parts, the H-LSTM is expected to not only improve demand forecast accuracy, but also increase aircraft availability and curtail inventory cost through decreasing unnecessary parts stocks. This paper is meaningful in that it is the very first study to offer a working-level improvement resolution in demand forecast through the LSTM, a type of deep learning, by utilizing ROKAF’s practical logistics data.
데이터표준화 사례를 통한 데이터 품질 향상에 대한 연구
김진섭(Kim Jinseob) 한국정보과학회 2006 한국정보과학회 학술발표논문집 Vol.33 No.2C
데이터품질이 기업의 경쟁력에 영향을 주는 핵심요소임에도 불구하고, 현 정보시스템 현실에서는 데이터품질 저하라는 심각한 상황을 맞고 있다. 데이터품질을 개선시키기 위한 여러 가지 방안들이 논의되고 있지만, 대부분 현상 데이터에 대한 품질 평가 및 개선에 한정되거나, 개선방안의 구체성이 부족하여 실무적 적용에 한계를 갖는다. 본 연구에서는 데이터표준화 개념을 데이터베이스 설계와 병행하여 수행할 수 있도록 구체적인 구현방안과 사례를 제시하였다. 데이터표준화는 각 단위시스템의 데이터에 대한 명칭 및 도메인에 대한 표준원칙을 수립하여 표준데이터를 구축한 후 전체 시스템에 적용하는 방법이다. 본 연구의 구현방안은 표준데이터 구축이 선행되지 않은 경우에도 데이터의 구조적 품질수준이 보장된 데이터베이스 설계를 수행하고자 하는 실무에 기여할 수 있다.
고엽제 노출군과 비노출군 노인에서의 신경인지기능 검사 결과 비교
한승규(Seunggyu Han),최진희(Jinhee Choi),소형석(Hyung Seok So),최하연(Hayun Choi),전홍진(Hong Jin Jeon),김진섭(Jinseob Kim),김기원(Kiwon Kim) 대한신경정신의학회 2021 신경정신의학 Vol.60 No.4
Objectives Agent Orange is a defoliant chemical that is widely known for its use by the U.S. military during the Vietnam War. It is known to be associated with the occurrence of various diseases in exposed subjects. However, few previous studies have focused on the effects of exposure to Agent Orange on cognitive dysfunction. Methods A total of 387 male subjects participated in the study. They were divided into those who were exposed to Agent Orange (n=301) and those without exposure (n=86). Both were evaluated with neuropsychological batteries, including the Consortium to Establish a Registry for Alzheimer’s Disease and the Seoul Neuropsychological Screening Battery-Second Edition. Results The group exposed to Agent Orange showed significantly higher scores in the Rey Complex Figure Test copy and recognition compared to those without exposure. Conclusion In this study, we compared the effects of exposure to Agent Orange on cognitive function in groups that had not yet progressed to dementia. The Agent Orange exposure group showed better results in some tests evaluating visuospatial and memory function.