• Title/Summary/Keyword: weighted function

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Reverse Inequalities through k-weighted Fractional Operators with Two Parameters

  • Bouharket Benaissa;Noureddine Azzouz
    • Kyungpook Mathematical Journal
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    • v.64 no.1
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    • pp.31-46
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    • 2024
  • The aim of this paper is to present an approach to improve reverse Minkowski and Hölder-type inequalities using k-weighted fractional integral operators a+𝔍𝜇w with respect to a strictly increasing continuous function 𝜇, by introducing two parameters of integrability, p and q. For various choices of 𝜇 we get interesting special cases.

A CHARACTERIZATION OF M-HARMONICITY

  • Lee, Jae-Sung
    • Bulletin of the Korean Mathematical Society
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    • v.47 no.1
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    • pp.113-119
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    • 2010
  • If f is M-harmonic and integrable with respect to a weighted radial measure $\upsilon_{\alpha}$ over the unit ball $B_n$ of $\mathbb{C}^n$, then $\int_{B_n}(f\circ\psi)d\upsilon_{\alpha}=f(\psi(0))$ for every $\psi{\in}Aut(B_n)$. Equivalently f is fixed by the weighted Berezin transform; $T_{\alpha}f = f$. In this paper, we show that if a function f defined on $B_n$ satisfies $R(f\circ\phi){\in}L^{\infty}(B_n)$ for every $\phi{\in}Aut(B_n)$ and Sf = rf for some |r|=1, where S is any convex combination of the iterations of $T_{\alpha}$'s, then f is M-harmonic.

GENERAL LAWS OF PRECISE ASYMPTOTICS FOR SUMS OF RANDOM VARIABLES

  • Meng, Yan-Jiao
    • Journal of the Korean Mathematical Society
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    • v.49 no.4
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    • pp.795-804
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    • 2012
  • In this paper, we obtain two general laws of precise asymptotics for sums of i.i.d random variables, which contain general weighted functions and boundary functions and also clearly show the relationship between the weighted functions and the boundary functions. As corollaries, we obtain Theorem 2 of Gut and Spataru [A. Gut and A. Sp$\check{a}$taru, Precise asymptotics in the law of the iterated logarithm, Ann. Probab. 28 (2000), no. 4, 1870-1883] and Theorem 3 of Gut and Sp$\check{a}$taru [A. Gut and A. Sp$\check{a}$taru, Precise asymptotics in the Baum-Katz and Davids laws of large numbers, J. Math. Anal. Appl. 248 (2000), 233-246].

DEGREE OF APPROXIMATION OF A FUNCTION ASSOCIATED WITH HARDY-LITTLEWOOD SERIES IN WEIGHTED ZYGMUND W(Z(𝜔)r)-CLASS USING EULER-HAUSDORFF SUMMABILITY MEANS

  • Tejaswini Pradhan;G V V Jagannadha Rao
    • Nonlinear Functional Analysis and Applications
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    • v.28 no.4
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    • pp.1035-1049
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    • 2023
  • Approximation of functions of Lipschitz and Zygmund classes have been considered by various researchers under different summability means. In the proposed study, we investigated an estimation of the order of convergence of a function associated with Hardy-Littlewood series in the weighted Zygmund class W(Z(𝜔)r)-class by applying Euler-Hausdorff summability means and subsequently established some (presumably new) results. Moreover, the results obtained here represent the generalization of several known results.

Weighted Histogram Equalization Method adopting Weber-Fechner's Law for Image Enhancement (이미지 화질개선을 위한 Weber-Fechner 법칙을 적용한 가중 히스토그램 균등화 기법)

  • Kim, Donghyung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.7
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    • pp.4475-4481
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    • 2014
  • A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.

Weighted Value Sharing and Uniqueness of Entire Functions

  • Sahoo, Pulak
    • Kyungpook Mathematical Journal
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    • v.51 no.2
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    • pp.145-164
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    • 2011
  • In the paper, we study with weighted sharing method the uniqueness of entire functions concerning nonlinear differential polynomials sharing one value and prove two uniqueness theorems, first one of which generalizes some recent results in [10] and [16]. Our second theorem will supplement a result in [17].

A Univariate Loss Function Approach to Multiple Response Surface Optimization: An Interactive Procedure-Based Weight Determination (다중반응표면 최적화를 위한 단변량 손실함수법: 대화식 절차 기반의 가중치 결정)

  • Jeong, In-Jun
    • Knowledge Management Research
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    • v.21 no.1
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    • pp.27-40
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    • 2020
  • Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

Robust varying coefficient model using L1 regularization

  • Hwang, Changha;Bae, Jongsik;Shim, Jooyong
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.1059-1066
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    • 2016
  • In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.