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권세혁,Kwon, S.H. 한국전자통신연구원 1994 전자통신동향분석 Vol.9 No.1
경제성과 효율성을 지닌 종합정보 통신망인 ISDN은 음성 또는 비음성의 정보를 모두 디지털 형식으로 통일시켜 각종 서비스가 하나의 회선과 이용자 번호로 제공되는 디지털 전송. 교환방식의 네트워크로 우리나라에서는 1994년 1월 서비스가 상용화 되었다. 전기통신의 국제 표준화 작업은 ITU 회원국의 주관청과 표준화 업무에 희망을 피력하고 회원국의 승인을 받은 공인된 민간 운영회사 및 과학 산업기구의 연구원으로 구성된 ITU-TS에 의해 주관된다. ITU-TS는 사용자-망 인터페이스 본 네트워크 및 단말기와 사용자 측의 필요 조건을 정리하여 1984년과 1988년에 I시리즈 권고를 통해 ISDN의 개관과 서비스, 망 구성 및 운용, 사용자-망 인터페이스, 망간의 인터페이스 등 ISDN에 관한 개념이나 원칙을 국제 표준으로 발표하였다. ITU-TS의 ISDN 일반 원칙에 따르면 네트워크 자체는 각국의 상황에 따라 자유롭게 구성할 수 있다. 본 연구에서는 통신 선진국인 미국의 표준화 과정을 조사.연구함으로써 국내 통신망의 보호와 국내 실정에 맞는 통신망 구축을 위해 보다 나은 ISDN 기술 기준의 제정 방향을 제시하는데 그 목적이 있다. 우리 나라에서도 기술기준에 대한 연구 시기를 앞당기고 현장 시험에서 얻어지는 자료나 연구결과의 교환이 원활히 이루어져야 하며, 상용화 후에도 예측못했던 문제점 및 사후 관리에 대한 연구가 지속적으로 행해져야 겠다. 그리고 기술기준을 제정하는 위원회에는 통신 사업자는 물론 정부 및 이용자까지도 함께 참여하여 모든 사람의 이익을 고려한 기준이 제정될 수 있어야겠다.
권세혁(S H Kwon),오현승(H S Oh) 한국산업경영시스템학회 2015 한국산업경영시스템학회지 Vol.38 No.1
There have been various studies on measurements of flood risk and forecasting models. For river and dam region, PDF and FVI has been proposed for measurement of flood risk and regression models have been applied for forecasting model. For Bo region unlikely river or dam region, flood risk would unexpectedly increase due to outgoing water to keep water amount under the designated risk level even the drain system could hardly manage the water amount. GFI and general linear model was proposed for flood risk measurement and forecasting model. In this paper, FVI with the consideration of duration on GFI was proposed for flood risk measurement at Bo region. General linear model was applied to the empirical data from Bo region of Nadong river to derive the forecasting model of FVI at three different values of Base High Level, 2m, 2.5m and 3m. The significant predictor variables on the target variable, FVI were as follows: ground water level based on sea level with negative effect, difference between ground altitude of ground water and river level with negative effect, and difference between ground water level and river level after Bo water being filled with positive sign for quantitative variables. And for qualitative variable, effective soil depth and ground soil type were significant for FVI.
권세혁(S. H. Kwon),오현승(H. S. Oh) 한국산업경영시스템학회 2014 한국산업경영시스템학회지 Vol.37 No.1
During a flood season, Bo region could be easily exposed to flood due to increase of ground water level and the water drain difficulty even the water amount of Bo can be managed. GFI for the flood risk is measured by mean depth to water during a dry season and minimum depth to water and tangent degree during a flood season. In this paper, a forecasting model of the target variable, GFI and predictors as differences of height between ground water and Bo water, distances from water resource, and soil characteristics are obtained for the dry season of 2012 and the flood season of 2012 with empirical data of Gangjungbo and Hamanbo. Obtained forecasting model would be used for keep the value of GFI below the maximum allowance for no flooding during flooding seasons with controlling the values of significant predictors.
권세혁(S. H. Kwon),오현승(H. S. Oh) 한국산업경영시스템학회 2012 한국산업경영시스템학회지 Vol.35 No.1
Ship handling simulator is a virtual ship navigating system with three dimensional screen system and simulation programs. FTS simulation can produce theoretically infinite experiment tests without time constraint, but which results in collecting determinstic observations. RTS simulation can collect statistical observations but has disadvantage of spending at least 30 minutes for a single experiment. The previous studies suggested that the number of experiment conditions to be tested could be reduced to obtain random data with RTS simulation by focusing on highly difficult experiment condition for ship handling. It has the limitation of not estimating the distribution of ship handling difficulty for the route. In this paper, similarity and clustering analysis are suggested for reduction methodology of experiment conditions. Similarity of experiment conditions are measured as follows: euclidean distance of ship handling difficulty index and correlation matrix of distance differences from the designed route. Clustering analysis and multi-dimensional scaling are applied to classify experiment conditions with measured similarity into reducing the number of RTS simulation conditions. An empirical result on Dangin harbor is shown and discussed.
권세혁(S. H. Kwon),오현승(H. S. Oh) 한국산업경영시스템학회 2016 한국산업경영시스템학회지 Vol.39 No.1
It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer’s perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. ARMA(2, 1, 2)(1, 1, 1)₇ and ARMA(0, 1, 1)(1, 1, 0)₁₂ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.