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안인석(Ihn Seok Ahn),서기성(Ki Sung Seo),이노성(Noh Sung Lee),최준열(Joon Youl Choi),우광방(Kwang Bang Woo) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
This paper constitutes the simulator of the serial production line using Extended Petri Nets. We analyze each operations and effect of machine down, calculating the performance measures for improving the capability of manufacturing system. The performance measures are system efficiency, average utilization of machines, average buffer level, and so on. We intend to present the information of the operation states and various problem occurring in the systems.
안인석 ( Ihn-seok Ahn ) 한국산업융합학회 2021 한국산업융합학회 논문집 Vol.24 No.3
In this study, the comparative analysis, among the design standard value of distribution power, the calculated value from the measurement data of strand and the empirical data of the distribution line itself, have been performed for the elastic coefficients and linear expansion coefficients of distribution line conductors. The empirical values of elastic coefficients were lower about 10.6%(892㎏f/㎟) than those of the design standard value of the distribution power and there were a little difference between the empirical values of linear expansion coefficients and the design standard value of the distribution power. From the above results, it could be concluded that the empirical values of conductor characteristics should be used in the dip design and installation of distribution line.
김길성(Gil-Sung Kim),안인석(Ihn-Seok Ahn),오성권(Sung-Kwun Oh) 대한전기학회 2009 전기학회논문지 Vol.58 No.8
In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.