• 한국수학교육학회지시리즈B:순수및응용수학
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• 제22권3호
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• pp.275-283
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• 2015

Let T be a bounded linear operator on a complex Hilbert space H. For a positive integer k, an operator T is said to be a k-quasi-2-isometric operator if T^∗k(T^∗2T² − 2T^∗T + I)T^k = 0, which is a generalization of 2-isometric operator. In this paper, we consider basic structural properties of k-quasi-2-isometric operators. Moreover, we give some examples of k-quasi-2-isometric operators. Finally, we prove that generalized Weyl’s theorem holds for polynomially k-quasi-2-isometric operators.

• 분석과학
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• 제8권4호
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• pp.707-714
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• 1995

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonstrates its excellent utility for the complex depth profile system. It includes the stable restoration of the depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제4권2호
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• pp.41-57
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• 2000

In this paper, we consider first order generalized difference scheme for the two-point boundary value problem and one-dimensional second order parabolic type problem. The optimal error estimates in $L_p$ and $W^{1,p}$ ($2{\leq}p{\leq}{\infty}$) as well as some superconvergence estimates in $W^{1,p}$ ($2{\leq}p{\leq}{\infty}$) are obtained. The main results in this paper perfect the theory of GDM.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1557-1563
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• 2015

Multiclass classification is typically performed using the voting scheme method based on combining binary classifications. In this paper we propose multiclass classification method for large data, which can be regarded as the revised one-vs-all method. The multiclass classification is performed by using the hat matrix of least squares support vector machine (LS-SVM) ensemble, which is obtained by aggregating individual LS-SVM trained on each subset of whole large data. The cross validation function is defined to select the optimal values of hyperparameters which affect the performance of multiclass LS-SVM proposed. We obtain the generalized cross validation function to reduce computational burden of cross validation function. Experimental results are then presented which indicate the performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1353-1360
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• 2008

An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.735-744
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• 2007

Support vector machine(SVM) is capable of providing a more complete description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse kernel regression(SKR) to overcome a weak point of SVM, which is, the steep growth of the number of support vectors with increasing the number of training data. The iterative reweighted least squares(IRWLS) procedure is used to solve the optimal problem of SKR with a Laplacian prior. Furthermore, the generalized cross validation(GCV) function is introduced to select the hyper-parameters which affect the performance of SKR. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제19권3호
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• pp.271-287
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• 2015

In this article, a more generalized form of the impact time and angle control guidance law is proposed based on the linear quadratic optimal control methodology. For the purpose on controlling an additional constraint such as the impact time, we introduce an additional state variable that is defined to be the jerk (acceleration rate). Additionally, in order to provide an additional degree of freedom in choosing the guidance gains, the performance index that minimizes the control energy weighted by an arbitrary order of time-to-go is considered in this work. First, the generalized form of the impact angle control guidance law with an additional term which is used for the impact time control is derived. And then, we also determine the additional term in order to achieve the desired impact time. Through numbers of numerical simulations, we investigate the superiority of the proposed guidance law compared to previous guidance laws. In addition, a salvo attack scenario with multiple missile systems is also demonstrated.

• 한국정밀공학회지
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• 제26권5호
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• pp.120-127
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• 2009

An electric furnace, inside which desired temperatures are kept constant by generating heat, is known to be a difficult system to control and model exactly because system parameters and response delay time vary as the temperature and position are changed. In this study the heating system of ceramic drying furnaces with time-varying parameters is mathematically modeled as a second order system and control parameters are estimated by using a RIV (Recursive Instrumental-Variable) method. A modified bang-bang control with magnitude tuning is proposed in the time optimal temperature control of ceramic drying electric furnaces and its performance is experimentally verified. It is proven that temperature tracking of adaptive time optimal control using a second order model is more stable than the GPCEW (Generalized Predictive Control with Exponential Weight) and rapidly settles down by pre-estimation of the system parameters.

• 제어로봇시스템학회논문지
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• 제4권6호
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• pp.713-721
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• 1998

In this paper a new modeling and an optimal pole-placement control for the suspension system of Macpherson type are investigated. The rotational motion of the unsprung mass is emphasized in the new modeling. The two generalized coordinates selected in the new model are the vortical displacement of sprung mass and the angular displacement of control arm. Both variables are measured from their static equilibrium points. It is shown that the conventional model is a special case of the new model since the transfer function of the new model coincides with that of the conventional one if the lower support point of the shock absorber is located at the mass center of the unsprung mass. It is also shown that the resonance frequencies of the new model agree better with experimental results. Therefore, the new model is more general in the sense that it Provides an extra degree of freedom in determining the plant model for control system design. An optimal pole-placement control which combines LQ control and pole-placement technique is applied to the new model. Simulations are provided.

• 한국지능시스템학회논문지
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• 제10권3호
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• pp.232-241
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• 2000

본 연구는 대량의 데이터에서 효율적으로 최적 규칙을 발견하기 위해 개념 계층과 정보 이득 및 라프셋 이론에 딕반한 통합 방법을 제시하고,이를 최적 규칙 발견 시스템으로 구현한다. 본 방법은 데이터베이스에 있는 데이터에서 일반화된 지식을 추출하기 위한 속성중심의 개념 상승 기법과 불필요한 속성 및 속성값을 제거하기 위한 지식 감축 기법을 적용하며, 최적 규칙의 도출을 위해 속성의 중요도를 사용한다. 본 시스템은 먼저, 속성값 개념의 일반화에 의해 종복 튜플을 제거함으로써 데이터 베이스의 크기를 줄이고, 결정속성에 뎡향을 주지않는 조건속성을 제거하여 간략화된 최적 규칙을 유도한다.그리고 실제 데이터에 적용하여 결정 규칙을 유도하고 그 규칙을 새로운 데이터에 테스트햐 봄으로써 새로운 데이터에도 잘 적용됨을 보인다.