• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.71-79
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• 1999

We consider multi-step quasi-Newton methods for unconstrained optimization. These methods were introduced by Ford and Moghrabi [1, 2], who showed how interpolating curves could be used to derive a generalization of the Secant Equation (the relation normally employed in the construction of quasi-Newton methods). One of the most successful of these multi-step methods makes use of the current approximation to the Hessian to determine the parameterization of the interpolating curve in the variable-space and, hence, the generalized updating formula. In this paper, we investigate new parameterization techniques to the approximate Hessian, in an attempt to determine a better Hessian approximation at each iteration and, thus, improve the numerical performance of such algorithms.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.874-882
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• 2011

The decoupling design for the one-degree-of-freedom controller system is treated within the $H_{\infty}$ framework. In the present study, we demonstrate that the $H_{\infty}$ performance problem in the decoupling design is reduced into interpolation problems on scalar functions. To guarantee the properness of decoupling controllers and the overall transfer matrix, the relative degree conditions on the interpolating scalar functions are derived. To find the interpolating functions with relative degree constraints, Nevanlinna-Pick algorithm with starting function constraint is utilized in the present study. An illustrative example is given to provide details regarding the solution.

• Bulletin of the Korean Mathematical Society
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• v.39 no.3
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• pp.423-430
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• 2002

Given operators X and Y acting on a Hilbert space H, an interpolating operator is a bounded operator A such that AX = Y. An interpolating operator for n-operators satisfies the equation $AX_{}i$ = $Y_{i}$ for i/ = 1,2,…, n. In this article, we obtained the following : Let X ＝ ($x_{i\sigma(i)}$ and Y ＝ ($y_{ij}$ be operators in B(H) such that $X_{i\sigma(i)}\neq\;0$ for all i. Then the following statements are equivalent. (1) There exists an operator A in Alg L such that AX = Y, every E in L reduces A and A is a self-adjoint operator. (2) sup ${\frac{\parallel{\sum^n}_{i=1}E_iYf_i\parallel}{\parallel{\sum^n}_{i=1}E_iXf_i\parallel}n\;\epsilon\;N,E_i\;\epsilon\;L and f_i\;\epsilon\;H}$ < $\infty$ and $x_{i,\sigma(i)}y_{i,\sigma(i)}$ is real for all i = 1,2, ....

• Journal of Mechanical Science and Technology
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• v.15 no.2
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• pp.192-198
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• 2001

We consider an interpolation problem with nonuniform rational B-spline curves given ordered data points. The existing approaches assume that weight for each point is available. But, it is not the case in practical applications. Schneider suggested a method which interpolates data points by automatically determining the weight of each control point. However, a drawback of Schneiders approach is that there is no guarantee of avoiding undesired poles; avoiding negative weights. Based on a quadratic programming technique, we use the weights of the control points for interpolating additional data. The weights are restricted to appropriate intervals; this guarantees the regularity of the interpolating curve. In a addition, a knot placement is proposed for pleasing interpolation. In comparison with integral B-spline interpolation, the proposed scheme leads to B-spline curves with fewer control points.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.255-258
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• 1999

This paper describes a 40-Msample/s 10-bit CMOS folding and interpolating analog-to-digital converter (ADC). A new 2-step architecture is proposed. The proposed architecture is composed of a coarse ADC bloch for the 6bits of MSBs and a fine ADC block for the remaining 4bits. The amplified folding analog signals in the coarse ADC are selectively chosen for the fine ADC. In the fine ADC, the bubble errors of the comparators are corrected by using the BGM(binary-gray-mixed) code[1] and extra two comparators are used to correct underflow and overflow errors. The proposed ADC was simulated using CMOS 0.25${\mu}{\textrm}{m}$ parameters and occupies 1.0mm$\times$1.0mm. The power consumption is 48㎽ at 40MS/s with 2.5-V power supply. The INL is under $\pm$2.0LSB and the DNL. is under $\pm$1.0LSB by Matlab simulations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.4
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• pp.279-294
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• 2013

We are interested in an adaptive grid method for hyperbolic equations. A multiresolution analysis, based on a biorthogonal family of interpolating scaling functions and lifted interpolating wavelets, is used to dynamically adapt grid points according to the physical field profile in each time step. Traditional finite-difference schemes with fixed stencils produce high oscillations around sharp discontinuities. In this paper, we hybridize high-resolution schemes, which are suitable for capturing singularities, and apply a finite-difference approach to the scaling functions at non-singular points. We use a total variation diminishing Runge-Kutta method for the time integration. The computational cost is proportional to the number of points present after compression. We provide several numerical examples to verify our approach.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.27-36
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• 1983

The 12-Channe1 TDM/FDM translator is proposed which uses a periodically varying digital filter and the multiplexing weaver modulators. The general 12-Channel TDM/FDM translator using the Weaver modulators requires 24 interpolating FIR(finite impulse response) filters and 24 sinusoidal modulators, however the TDM/FDM translator proposed in this paper consists of one interpolating periodically varying digital filter and 12 sinusoidal modulators. The results obtained in this paper show that the system is simplified and the computation time is reduced. These facts are verified by the computer simulation.

• Journal of computational fluids engineering
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• v.20 no.4
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• pp.44-50
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• 2015

The computational efficiency of flow simulations with Multi-resolution analysis (MRA) was enhanced via the boundary treatment of the computational domain. In MRA, an adaptive dataset to a solution is constructed through data decomposition with interpolating polynomial and thresholding. During the decomposition process, the basis points of interpolation should exceed the boundary of the computational domain. In order to resolve this problem, the weight coefficients of interpolating polynomial were adjusted near the boundaries. By this boundary treatment, the computational efficiency of MRA was enhanced while the numerical accuracy of a solution was unchanged. This modified MRA was applied to two-dimensional steady Euler equations and the enhancement of computational efficiency and the maintenance of numerical accuracy were assessed.

• Communications of the Korean Mathematical Society
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• v.17 no.3
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• pp.487-493
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• 2002

Given operators X and Y acting on a Hilbert space H, an interpolating operator is a bounded operator A such that AX = Y. An interpolating operator for the n-operators satisfies the equation AX$\_$i/ : Y$\_$i/, for i = 1, 2 …, n. In this article, we obtained the following : Let X = (x$\_$ij/) and Y = (y$\_$ij/) be operators acting on H such that $\varkappa$$\_$ i$\sigma$ (i)/ 0 for all i. Then the following statements are equivalent. (1) There exists a unitary operator A in Alg(equation omitted) such that AX = Y and every E in (equation omitted) reduces A. (2) sup{(equation omitted)}<$\infty$ and (equation omitted) = 1 for all i = 1, 2, ….

• The Pure and Applied Mathematics
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• v.14 no.3
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• pp.161-166
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• 2007

Given operators X and Y acting on a Hilbert space H, an interpolating operator is a bounded operator A such that AX = Y. An interpolating operator for n-operators satisfies the equation $AX_i=Y_i$, for i = 1,2,...,n. In this article, we showed the following: Let L, be a subspace lattice on a Hilbert space H and let X and Y be operators in B(H). Then the following are equivalent: (1) $$sup\{\frac{{\parallel}E^{\bot}Yf{\parallel}}{{\overline}{\parallel}E^{\bot}Xf{\parallel}}\;:\;f{\epsilon}H,\;E{\epsilon}L}\}\;<\;{\infty},\;sup\{\frac{{\parallel}Xf{\parallel}}{{\overline}{\parallel}Yf{\parallel}}\;:\;f{\epsilon}H\}\;<\;{\infty}$$ and $\bar{range\;X}=H=\bar{range\;Y}$. (2) There exists an invertible operator A in AlgL such that AX=Y.