• Journal of the military operations research society of Korea
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• v.8 no.1
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• pp.71-76
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• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.553-561
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• 2012

Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.

• Korean Management Science Review
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• v.25 no.2
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• pp.13-23
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• 2008

Top music 10 is a additional service product of the A mobile telephone company. Up to now, A company is just selling it by outbound TM to customers which visit any contents of Top Music 10. In this paper, we proposed a targeting method combining two score models by data mining. The proposed combining model is to find customers more likely to respond to outbound TM. The proposed targeting method is expected to improve both from 32.8% to 44.0% in the response rate and from 54.7% to 61.4% in the retention rate.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.182-185
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• 2003

This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.

• Water for future
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• v.25 no.1
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• pp.93-100
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• 1992

A study on the Derivation of the Unit Hydrograph using Multiple Regression Moe이. The purpose of this study is to deriver an optimal unit hydrograph suing the multiple regression model, particularly when only small amount of data is available. The presence of multicollinearity among the input data can cause serious oscillations in the derivation of the unit hydrograph. In this case, the oscillations in the unit hydrograph ordinate are eliminated by combining the data. The data used in this study are based upon the collection and arrangement of rainfall-runoff data(1977-1989) at the Soyang-river Dam site. When the matrix X is the rainfall series, the condition number and the reciprocal of the minimum eigenvalue of XTX are calculated by the Jacobi an method, and are compared with the oscillation in the unit hydrograph. The optimal unit hydrograph is derived by combining the numerous rainfall-runoff data. The conclusions are as follows; 1)The oscillations in the derived unit hydrograph are reduced by combining the data from each flood event. 2) The reciprocals of the minimum eigen\value of XTX, 1/k and the condition number CN are increased when the oscillations are active in the derived unit hydrograph. 3)The parameter estimates are validated by extending the model to the Soyang river Dam site with elimination of the autocorrelation in the disturbances. Finally, this paper illustrates the application of the multiple regression model to drive an optimal unit hydrograph dealing with the multicollinearity and the autocorrelation which cause some problems.

• Tunnel and Underground Space
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• v.27 no.3
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• pp.132-145
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• 2017

Shock loss was not applied for the tunnel ventilation of road tunnel in the past. However, pressure losses due to the shock loss can be significant in network double-deck road tunnel in which combining and separating road structures exist. For the optimum ventilation design of network double-deck road tunnel, this study conducted 3D CFD numerical analysis for the shock loss at the combining and separating flows. The CFD model was made with the real-scale model that was the standard section of double-deck road tunnel. The shock loss coefficient of various combining and separating angles and road width was obtained and compared to the existing design values. As a result of the comparison, the shock loss coefficient of the $30^{\circ}$ separating flow model was higher and that of the two-lane combining flow model was lower. Since the combining and separating angles and road width can be important for the design of shock loss estimation, it is considered that this study can provide the accurate design factors for the calculation of ventilation system capacity. In addition, this study conducted 3D CFD analysis in order to calculate the shock loss coefficient of both combining and separating flows at flared intersection, and the result was compared with the design values of ASHRAE. The model that was not widened at the intersection showed three times higher at the most, and the other model that was widened at the intersection resulted two times higher shock loss coefficients.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.119-128
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• 1997

Added variable plot and component-plus-residual plot are very useful for studying the role of a predictor in classical regression analysis. The former is usually used to check the effect of adding a new variable to existing model. The latter has been suggested as computationally convenient substitutes for the added variable plots, however, this plot is found to be better in detecting nonlinear relationships of a new predictor. By combining these two plots dynamically, we can take advantages of two plots simultaneously. And even further, we can get some knowledge of collinearity between a new predictor and predictors already in the model, and more accurate information about the possible outliers.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.104-115
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• 2013

For natural human-robot interaction, we need to know location and shape of facial feature in real environment. In order to track facial feature robustly, we can use the method combining particle filter and active appearance model. However, processing speed of this method is too slow. In this paper, we propose two ideas to improve efficiency of this method. The first idea is changing the number of particles situationally. And the second idea is switching the prediction model situationally. Experimental results is presented to show that the proposed method is about three times faster than the method combining particle filter and active appearance model, whereas the performance of the proposed method is maintained.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.3
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• pp.115-121
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• 2003

This paper proposes a radiation model, which considers radiation transport as an important component in hot gas analysis. This radiation model is derived from combining the method of partial characteristics (MPC) with net emission coefficient (NEC), and it covers the drawbacks of existing models. Subsequently, using this proposed model, the arc-flow interaction in an arcing chamber can be efficiently computed. The arc is represented as an energy source term composed of ohmic heating and the radiation transport in the energy conservation equation. Ohmic heating term was computed by the electric field analysis within the conducting plasma region. Radiation transport was calculated by the proposed radiation model. Also, in this paper, radiation models were introduced and applied to the gas circuit breaker (GCB) model. Through simulation results, the efficiency of the proposed model was confirmed.