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인공신경망을 이용한 항만터미널에서 컨테이너의 비정상 이송 프로세스 예측
전대욱(Daeuk Jeon),배혜림(Hyerim Bae) 한국SCM학회 2015 한국SCM학회지 Vol.15 No.2
There has been a request for improvement of container transfer in container terminal, because there is a complexity of work processes and cargo features of container. This paper is addressing the issue of abnormal process prediction using event logs which is occurred by an action of container transfer. Event logs contain the historical data from the executed processes. So, the analysis must be performed after the finished container transfer process. Hence, it is hardly avoidable that there is a time gap between analyzing and applying the analysis result. To reduce this time gap, we suggest a usage of ANN (Artificial Neural Network) for forecasting the anomalous container transfer. We use ARM (Activity Relation Matrix), a distance measure and LAPID (Local Anomaly Process Instance Detection) methodology to detect API (Anomaly Process Instance). The effectiveness of the proposed method was verified in a case study using real event logs from domestic container terminal.
류정호(Jeongho Ryu),유영숙(Youngsook Yoo),정성운(Sungwoon Jung),전민선(Minseon Jeon),김대욱(Daeuk Kim),엄명도(Myungdo Eom),김종춘(Jongchoon Kim) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
As the concerns regarding global worming were increased, the pressure of greenhouse gas(GHG) emission reduction on mobile source was also increased. Carbon Dioxides contribute over 90% of total GHG emission and the mobile source occupies about 20% of this CO₂ emission. Therefore automotive exhaust is suspected to be one of the major reasons of the rapid increase in greenhouse effect gases in ambient air. In this study, in order to investigate CO₂ emission characteristics from gasoline passenger cars(PC), which is the most dominant vehicle type in Korea, 106 vehicles were tested on the chassis dynamometer. CO₂ emissions and fuel efficiency were measured. The emission characteristics by displacement, gross vehicle weight, vehicle speed and CVS-75/vehicle speed mode were discussed. Test modes were vehicle speed modes and CVS-75 mode that have been used to develop emission factors and to regulate for light-duty vehicle in Korea. It was found that CO₂ emissions showed higher large displacement, heavy gross vehicle weight, low vehicle speed and CVS-75 mode than small displacement, light gross vehicle weight, high vehicle speed and vehicle speed mode, respectively. From these results, correlation between CO₂ emission and fuel efficiency was also determined. The results of this study will contribute to domestic greenhouse gas emissions calculation and making the national policy for climate change.