• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Bulletin of the Korean Mathematical Society
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• v.54 no.4
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• pp.1255-1280
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• 2017

In this paper we present a new family of identities for parametric Euler sums which generalize a result of David Borwein et al. [2]. We then apply it to obtain a family of identities relating quadratic and cubic sums to linear sums and zeta values. Furthermore, we also evaluate several other series involving harmonic numbers and alternating harmonic numbers, and give explicit formulas.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.589-592
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• 1996

It is well known that robust compensators designed by the block-diagonal Lyapunov function approaches are conservative while they are popular in practice because of their computational easiness. In this note, we develop a systematized version of conventional block-diagonal Lyapunov function approaches by deriving two separated optimizations based on the guaranteed cost control method. The proposed method generates reasonable robust compensators in practice.

• Journal of the Korean Mathematical Society
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• v.44 no.3
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• pp.525-539
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• 2007

Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.19-26
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• 2001

Previously, an improved crack analysis technique based on Element-Free Galerkin Method (EFGM) which includes a discontinuity function and a singular basis function was presented. The technique needs neither addition of nodes nor modification of the model, but it shows some dependency on the formulation and modeling parameters such as the class of weight function, the size of compact support, dilation parameter and the range controlled by the singular basis function. For those parameters, a parametric study was performed on the calculation of a discrete error and then, a guideline for the choice of adequate parameters in the technique was proposed.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.93-101
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• 1995

We present a brief survey concerning the relations between mean residual life and failure rate. Change points of mean residual life and failure rate are known to be different in general and we explore such situations in this paper. A few parametric models which show bathtub-shaped failure rate are examined in details, including the shape of its corresponding mean residual life function. We give some graphical comparisons of trend changes of mean residual life and failure rate for various choices of parameters for each parametric model.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2516-2518
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• 2004

This paper proposes a stability condition in discrete-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties and then, introduces the design method of a fuzzy-model-based controller which guarantees the stability. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of linear matrix inequalities (LMIs).

• Journal of Aerospace System Engineering
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• v.8 no.4
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• pp.18-23
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• 2014

In preliminary design phase, the wing geometry of the civil aircraft was determined using the empirical equation and historical data. To make wing geometry more aerodynamically efficient, an aerodynamic shape optimization was conducted. For this purpose the parametric modeling, high fidelity CFD analysis and metamodel-based optimal design technique were adopted. The parametric modeling got the design process to achieve the improvement by generating the configuration outputs easily for the major design variables. The optimal design equations were formularized as the type of the multi-objective functions considering low/high speed and lift/drag coefficient. The optimal solution was explored with the help of the kriging metamodel and the desirability function, therefore the optimal wing planform was sought to be excellent at both low and high speed region. Additionally the optimal wing planform was validated that it was excellent not only at the specific AOA, but also all over the range of AOA.

• IE interfaces
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• v.8 no.3
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• pp.133-139
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• 1995

Free from curves and surfaces are frequently used in designing engineering products such as car, ship, airplane, and hosing of electronic households. In many aspect, it is very nice to use the cutter radius compensation function of CNC controller when contour machining a 2-dimensional curve. However, if the 2D curve is a parametric spline, it is not easy to apply the cutter radius compensation function of CNC controller to the NC data obtained from many commercial CAM system. This is mainly due to the error magnification effect when offsetting line segments with inevitable round-off error at their vertices. Proposed in this paper is an approach to contour machining a 2D parametric spline while using cutter radius compensation. Some implementation results are included.