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全治赫,林惠蘭 한국경영과학회 1991 經營 科學 Vol.8 No.2
A forecasting problem is considered where demands show an apparent seasonal pattern and some prior information is available on the total cumulative demand during a seasonal cycle. Two models-constant level model and changing level model-are proposed and Bayesian forecasting equations are derived. These model are applied to the forecasting of Korean railroad passengers and the results are compared. We also analyze the effects of different prior informations.
시뮬레이션에서 Total Hazard를 이용한 신뢰도 추정
전치혁 한국경영과학회 1991 韓國經營科學會誌 Vol.16 No.1
The hazard estimator is proposed for estimating system failure probability of a general network where all minimal cut sets are given. Theoretical variance of the hazard estimator is derived in a bridge system. It is demonstrated that variance of the hazard estimator is much smaller than that of the raw simulation estimator particularly for small are failure probability.
공정변수의 변동을 고려한 호감도 함수를 통한 다중반응표면 최적화
권준범,이종석,이상호,전치혁,김광재 한국경영과학회 2005 한국경영과학회지 Vol.30 No.1
A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE(propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameter setting, a new desirability function is proposed by considering POE as well as distance-to-target of response and variance. The proposed method is illustrated using a rubber product case in Ribeiro et al. (2000).
여러 종류의 가공물과 배치 기계가 있는 재진입 흐름생산의 평균치분석
박영신,전치혁,김수영 한국경영과학회 2000 韓國經營科學會誌 Vol.25 No.1
We are concerned with estimating the average performance of a re-entrant line with single-job machines and batch machines. The system has multiclass job, which will be processed in predetermined routes. An analytical approach may be intractable since the system would not be modeled by product form quenueing networks due to the inclusion of batch machines and the consideration of multiclass jobs which have different processing times. We propose an approximation method based on the Mean Value Analysis(MVA). Our method obtains the mean waiting time in each buffer of a workstation and the mean cycle time using the MVA and heuristics. Numerical experiments show that the errors of our method are within 5% compared with simulation.
다수의 동일한 입력원을 갖는 ATM Multiplexer의 정확한 셀 손실 확률 분석
최우용,전치혁 한국경영과학회 1995 한국경영과학회 학술대회논문집 Vol.- No.1(2)
We propose a new approach to the calculation of the exact cell loss probability in a shared buffer ATM multiplexer, which is loaded with homogeneous discrete-time ON-OFF sources. Renewal cycles are identified in regard to the state of input sources and the buffer state on each renewal cycle is modelled as a K(shared buffer size)-state Markov chain. We also analyze the behavior of queue build-up at the shared buffer whose distribution together with the steady-state probabilities of the Markov chain leads to the exact cell loss probability. Our approach to obtaining the exact cell loss probability seems to be more efficient than most of other existing ones since our underlying Markov chain has less number of states.
ATM 스위치의 쎌 손실율 추정을 위한 Hybrid 시뮬레이션 기법
김지수,최우용,전치혁 한국경영과학회 1996 韓國經營科學會誌 Vol.21 No.3
An ATM switch must deal with various kinds of input sources having different traffic characteristics and it must guarantee very small value of cell loss probability, about 10^-8~10^-12, to deal with loss-sensitive traffics. In order to estimate such a rare event probability with simulation procedure, a variance reduction technique is essential for obtaining an appropriate level of precision with reduced cost. In this paper, we propose a hybrid simulation technique to achieve reduction of variance of cell loss probability estimator, where hybrid means the combination of analytical method and simulation procedure. A discrete time queueing model with multiple input sources and a finite shared buffer is considered, where the arrival process at an input source is governed by an Interrupted Bernoulli Process and the service rate is constant. We deal with heterogeneous input sources as well as homogeneous case. The performance of the proposed hybrid simulation estimator is compared with those of the raw simulation estimator and the importance sampling estimator in terms of variance reduction ratios.
Process Optimization Using Partial Least Squares and Some Related Techniques
Jun, Chi-Hyuck,Sang-Ho Lee,Hae-Sang Park 대한산업공학회 2008 대한산업공학회 추계학술대회논문집 Vol.2008 No.11
Process engineers are often eager to find the optimal levels of process variables that make the key quality variable as close to its target as possible. The quality of products produced in the process is often affected by a few hundreds to thousands of variables. So, it is difficult to construct a reliable prediction model from the data of many variables and small observations. The selection of important variables becomes a crucial issue naturally as well. In this paper, we introduce the partial least squares (PLS) regression and its use for the process optimization. The variable selection procedure under PLS is introduced with an application to automobile assembly. Also, the patient rule induction method and its variant based on PLS for the process optimization will be proposed. Some simulation results for the proposed rule induction are presented.
Effect of dimension reduction on predictability of multivariate chaotic time series
Jun-Yong Jeong,Jun-Seong Kim,Chi-Hyuck Jun 대한산업공학회 2015 대한산업공학회 춘계학술대회논문집 Vol.2015 No.4
Dimension reduction is an important component of a machine learning area. It transforms input spaces into the reduced spaces with smaller dimensionality. Goal of this paper is to analysis the effect of using various dimension reduction techniques for predicting multivariate chaotic time series. Input space of multivariate chaotic time series which is reconstructed state space usually brings more information of an original strange attractor than one of univariate chaotic time series. When the multivariate chaotic time series are used, however, it exhibits relatively high dimension on time delay coordinates vector which induces curse of dimensionality, statistical dependency and redundancy among features of input spaces which disturb the ability of machine learning techniques. To solve this problem, we apply dimension reduction techniques. After that, least squares support vector regression (LSSVR) of machine learning techniques is used to predict future value of chaotic time series. Our experiment consists of delayed Lorenz series.
Effect of dimension reduction on predictability of multivariate chaotic time series
Jun-Yong Jeong,Jun-Seong Kim,Chi-Hyuck Jun 한국경영과학회 2015 한국경영과학회 학술대회논문집 Vol.2015 No.4
Dimension reduction is an important component of a machine learning area. It transforms input spaces into the reduced spaces with smaller dimensionality. Goal of this paper is to analysis the effect of using various dimension reduction techniques for predicting multivariate chaotic time series. Input space of multivariate chaotic time series which is reconstructed state space usually brings more information of an original strange attractor than one of univariate chaotic time series. When the multivariate chaotic time series are used, however, it exhibits relatively high dimension on time delay coordinates vector which induces curse of dimensionality, statistical dependency and redundancy among features of input spaces which disturb the ability of machine learning techniques. To solve this problem, we apply dimension reduction techniques. After that, least squares support vector regression (LSSVR) of machine learning techniques is used to predict future value of chaotic time series. Our experiment consists of delayed Lorenz series.