• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.52-60
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• 2009

This paper proposes a digital controller for a nonlinear multi-input/multi-output(MIMO) system with time-delayed input. A nonlinear system with multi-input time delay is discretized using Taylor's discretization method, and the discretized system can be converted into a general nonlinear system. Consequently, general nonlinear controller synthesis can be applied to the discretized time-delay system We adopted MAC controller synthesis and verified the performance of the proposed method by conducting computer simulations. The results of the simulation showed that the proposed controller synthesis performs well and the proposed method is useful for controlling a nonlinear time-delay system.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.15-21
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• 2017

In this paper, the decentralized fuzzy observer design technique is presented for discrete-time nonlinear interconnected systems, which are assumed to be with unknown interconnections. To design the decentralized fuzzy observer, the design problem is considered and the performance function is defined to solve the design problem. Based on the performance function, the sufficient condition is derived for the observer design, and its condition is formulated into linear matrix inequalities. Finally, by the simulation result, the validity of the proposed observer design technique is shown.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.60-67
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• 1990

This paper proposes a model-based fault detection method for linear/nonlinear system having modelling errors, nonlinearities and measurement noise. The system model is represented by the unified operator [5] in order to apply to both the continuous-time and discrete-time problems. The fault detection method suggested here accounts for the effects of noise, model mismatch and nonlinearities. Modelling errors are depicted by additive forms and the nominal model denominator is fixed via prior experiments in order to quantify the nucertainty bound on the parameter estima-tion. The least square method is used to estimate the numerator parameters of the nominal model. performance than traditional methods.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.624-628
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• 2000

이 논문에서는 이산형 상태 공간 모델에서의 새로운 FIR 필터를 제안한다. 선형성, 무진동성, FIR 구조, 초기 조건과의 무관성등을 디자인 과정에서 고려해서 성능 지표를 최소화 하는 필터를 구한다. 성능지표로는 구간에서 외란 에너지와 현재 추정 에러의 최대 이득으로 생각하며, 이 지표는 일반적인 성능지표와는 다르다. 제안된 필터는 배치 형태로 먼저 구하고, 점화식 형태로 바꿀 수 있음을 보인다. 제안한 필터는 확률론적 시스템의 이동 구간 무편향 FIR 필터(RHUFF)와 유사함을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.865-871
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• 2009

In this paper, a novel feedback linearization is proposed for discrete-time nonlinear systems described by discrete-time T-S fuzzy models. The local linear models of a T-S fuzzy model are transformed to a controllable canonical form respectively, and their T-S fuzzy combination results in a feedback linearizable Tagaki-Sugeno fuzzy model. Based on this model, a nonlinear state feedback linearizing input is determined. Nonlinear state transformation is inferred from the linear state transformations for the controllable canonical forms. The proposed method of this paper is more intuitive and easier to understand mathematically compared to the well-known feedback linearization technique which requires a profound mathematical background. The feedback linearizable condition of this paper is also weakened compared to the conventional feedback linearization. This means that larger class of nonlinear systems is linearizable compared to the case of classical linearization.

• Computational Structural Engineering
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• v.10 no.2
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• pp.255-263
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• 1997

The optimum design of most structural systems used in practice requires considering design variables as discrete quantities. The present paper shows that the linear approximation method is very effective as a tool for the discrete optimum designs of unsymmetric composite laminates. The formulated design problem is subjected to a multiple in-plane loading condition due to shear and axial forces, bending and twisting moments, which is controlled by maximum strain criterion for each of the plys of a composite laminate. As an initial approach, the process of continuous variable optimization by FDM is required only once in operating discrete optimization. The nonlinear discrete optimization problem that has the discrete and continuous variables is transformed into the mixed integer programming problem by SLDP. In numerical examples, the discrete optimum solutions for the unsymmetric composite laminates consisted of six plys according to rotated stacking sequence were found, and then compared the results with the nonlinear branch and bound method to verify the efficiency of present method.

• Journal of Animal Science and Technology
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• v.44 no.2
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• pp.151-164
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• 2002

Genetic variance and covariance components of the linear traits and the ordered categorical traits, that are usually observed as dichotomous or polychotomous outcomes, were simultaneously estimated in a multivariate threshold animal model with concepts of arbitrary underlying liability scales with Bayesian inference via Gibbs sampling algorithms. A multivariate threshold animal model in this study can be allowed in any combination of missing traits with assuming correlation among the traits considered. Gibbs sampling algorithms as a hierarchical Bayesian inference were used to get reliable point estimates to which marginal posterior means of parameters were assumed. Main point of this study is that the underlying values for the observations on the categorical traits sampled at previous round of iteration and the observations on the continuous traits can be considered to sample the underlying values for categorical data and continuous data with missing at current cycle (see appendix). This study also showed that the underlying variables for missing categorical data should be generated with taking into account for the correlated traits to satisfy the fully conditional posterior distributions of parameters although some of papers (Wang et al., 1997; VanTassell et al., 1998) presented that only the residual effects of missing traits were generated in same situation. In present study, Gibbs samplers for making the fully Bayesian inferences for unknown parameters of interests are played rolls with methodologies to enable the any combinations of the linear and categorical traits with missing observations. Moreover, two kinds of constraints to guarantee identifiability for the arbitrary underlying variables are shown with keeping the fully conditional posterior distributions of those parameters. Numerical example for a threshold animal model included the maternal and permanent environmental effects on a multiple ordered categorical trait as calving ease, a binary trait as non-return rate, and the other normally distributed trait, birth weight, is provided with simulation study.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.209-210
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• 2008

본 논문에서는 제어기에 승산형 섭동을 가지는 제어기와 이산시간 특이시스템을 안정화시키는 비약성 제어기 설계방법을 제시한다. 강인 비약성 제어기가 존재할 조건과 제어기 설계방법을 블록최적화가 가능한 선형 행렬부등식 접근방법으로 제안한다. 또한, 제어기의 약성 성도를 표시하는 비약성 척도를 동시에 계산함으로 승산형 섭동의 제어기 최대 변동정도를 제시한다. 제안한 비약성 제어기는 제어기의 이득 변동에도 불구하고 이산 시간 특이시스템의 강인 안정성을 보장한다.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.353-360
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• 2015

Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.