• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.541-562
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• 2000

The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Magazine of the Korean Society of Agricultural Engineers
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• v.23 no.3
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• pp.78-87
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• 1981

This study was attempted to get dimensionless unit hydrograph by linear model which can be used to the estimation of flood for the development of Agricultural water resources and laid emphasis on the application of dimensionless unit hydrographs for the ungaged watersheds by applying linear model. The results summarized through this study are as follows. 1.Peak discharge is found to be Qp= CAR (C =0. 895A-o.145) having high significance between peak discharge, Qp and effective rainfall, R within the range of small watershed area, 84 to 470km2. consequently, linearity was acknowledged between rainfall and runoff. Reasonability is confirmed for the derivation of dimensionless unit hydrograph by linear model. 2.Through mathematical analysis, formula for the derivation of dimensionless unit hydrograph was derived. qp--p=(tp--t)n-1[e-(n-1)](tp--t-1) 3.Moment method was used for the evaluation of storage constant, K and shape parameter, n for the derivation of dimensionless unit hydrograph. Storage constant, K is more closely related with the such watershed characteristics as length of main stream and slopes. On the other hand, the shape parameter, n was derived with such watershed characteristics as watershed area, river length, centroid distance of the basin and slopes. 4.Time to peak discharge, Tp could be expressed as Tp=1. 25 (√s/L)0.76 having a high significance. 5.Dimensionless unit hydrographs by linear model stood more closely to the observe dimensionless unit hydrographs On the contrary, dimensionless unit hydrographs by S.C. S. method has much difference in comparison with linear model at the falling limb of hydrographs. 6.Relative errors in the q/qp at the point of 0.8 and 1.2 for the dimensionles ratio by linear model and S. C. S. method showed to be 2.41, 1.57 and 4.0, 3.19 percent respectively to the q/qp of observed dimensionless unit hydrographs. 7.Derivation of dimensionless unit hydrograph by linear model can be accomplished by linking the two empirical formulars for storage constant, K, and shape parameter, n with derivation formular for dimensionless unit hydrograph for the ungaged small watersheds.

• Communications of the Korean Mathematical Society
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• v.33 no.3
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• pp.973-983
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• 2018

Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Journal of the Earthquake Engineering Society of Korea
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• v.19 no.1
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• pp.1-11
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• 2015

In order to perform a soil-isolation-structure interaction analysis of seismically isolated nuclear power plant (NPP) structures, the nonlinear behavior of a seismic isolation system may be converted to an equivalent linear model used in frequency domain analysis. Seismic responses for seismically isolated NPP containment structures subjected to a simple artificial acceleration history and different site class earthquakes are evaluated for the equivalent-linear and nonlinear models that have been applied to lead-rubber bearing (LRB) modeling. It can be observed that the maximum displacements of the equivalent linear model are larger than that of the nonlinear model. From the floor response spectrum analysis for the top of NPP containment structures, it can be observed that the spectral acceleration of an equivalent linear model at about 0.5 Hz frequency is about 2~3 times larger than that of a nonlinear model.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.695-708
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• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.47-54
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• 1999

To estimate optimal tree diameter and height growth function in Pinus thunbergii stands with site index of 12 class, quoted from two linear models of linear transformation(1) and linear transformation (2) and four non-linear models of exponential, Gompertz, Chapman-Richards, and Weibull etc.. Analyzed correlation among the estimated tree diameter and height by these function models, and observed diameter and height growth were compared. In the results of tree diameter and height growth estimation by stand age, non-linear models showed better appropriation than linear model and Chapman-Richards model was most fitted for tree height growth but few, if any, differences among their nonlinear models. Therefore, it is consider to be much more study about non-linear model to estimate tree diameter and height growth in the actual stands hereafter.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.3_4
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• pp.34-47
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• 1995

It is experienced fact as a regular annual event that the structure to he designed on unreasonable flood for the agricultural structures including reservoirs have been brought not only loss of lives, but also enormous property damage. For the solution of this problem at issue, this study was conducted to develop an optimal runoff hydrograph model by comparison of the peak flows and time to peak between observed and simulated flows derived by linear time-invariant and linear time-variant models under the condition of having a short duration of heavy rainfall with uniform rainfall intensity at nine small watersheds which are within the range of 55.9 to 140.7 square kilometers in area in Han, Geum, Nagdong and Yeongsan Rivers. The results obtained through this study can be summarized as follows. 1. Storage constants and Gamma function arguments were calculated within the range of 1.2 to 6.42 and of 1.28 to 8.05 respectively by the moment method as the parameters for the analysis of runoff hydrograph based on linear time-invariant model. 2. Parameters for both linear time-invariant and linear time-variant models were calibrated with nine gaged watershed data, using a trial and error method. The resulting parameters including Gamma function argument, N and storage constant, K for linear time-invariant model were related statistically to watershed characteristic variables such as area, slope, length of main stream and the centroid length of the basin. 3. Average relative errors of the simulated peak discharge of calibrated runoff hydrographs by using linear time-variant and linear time-invariant models were shown to be 0.75 and 5.42 percent respectively to the peak of observed runoff hydrographs. Correlation coefficients for the statistical analysis in the same condition were shown to be 0.999 and 0.978 with a high significance respectively. Therefore, it can be concluded that the accuracy of a linear time-variant model is approaching more closely to the observed runoff hydrograph than that of a linear time-invariant model in the applied watersheds. 4. Average relative errors of the time to peak of calibrated runoff hydrographs by using linear time-variant and linear time-invariant models were shown to be 16.44 and 19.89 percent respectively to the time to peak of observed runoff hydrographs. Correlation coefficients in the same condition were also shown to be 0.999 and 0.886 with a high significance respectively. 5. It can be seen that the shape of simulated hydrograph based on a linear time- variant model is getting closer to the observed runoff hydrograph than that of a linear time-invariant model in the applied watersheds. 6. Two different models were verified with different rainfall-runoff events from data for the calibration by relative error and correlation analysis. Consequently, it can be generally concluded that verification results for the peak discharge and time to peak of simulated runoff hydrographs were in good agreement with those of calibrated runoff hydrographs.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1051-1061
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• 2010

This study hydrologically re-analysed the Muskingum channel routing method to represent it as a linear combination of the linear channel model considering only the translation and the linear reservoir model considering only the storage effect. The resulting model becomes a kind of instantaneous unit hydrograph, whose parameters are identical to those of the Muskingum model. That is, the outflow occurs after the routing interval ${\Delta}t$ or concentration time $T_c$, and among the total amount of inflow, the x portion is translated by the linear channel model and the remaining (1-x) portion is routed by the linear reservoir model with the storage coefficient ��$K_c$. The application result of both the Muskingum channel routing method and its corresponding instantaneous unit hydrograph to an imaginary channel showed that these two models are basically identical. This result was also assured by the application to the channel flood routing to the Kumnam and Gongju Station for the discharge from the Daechung Dam.