• Title/Summary/Keyword: Additive uniqueness

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A General Uniqueness Theorem concerning the Stability of AQCQ Type Functional Equations

  • Lee, Yang-Hi;Jung, Soon-Mo
    • Kyungpook Mathematical Journal
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    • v.58 no.2
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    • pp.291-305
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    • 2018
  • In this paper, we prove a general uniqueness theorem which is useful for proving the uniqueness of the relevant additive mapping, quadratic mapping, cubic mapping, quartic mapping, or the additive-quadratic-cubic-quartic mapping when we investigate the (generalized) Hyers-Ulam stability.

SOME STABILITY RESULTS FOR SEMILINEAR STOCHASTIC HEAT EQUATION DRIVEN BY A FRACTIONAL NOISE

  • El Barrimi, Oussama;Ouknine, Youssef
    • Bulletin of the Korean Mathematical Society
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    • v.56 no.3
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    • pp.631-648
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    • 2019
  • In this paper, we consider a semilinear stochastic heat equation driven by an additive fractional white noise. Under the pathwise uniqueness property, we establish various strong stability results. As a consequence, we give an application to the convergence of the Picard successive approximation.

Phase Retrieval Using an Additive Reference Signal: I. Theory (더해지는 기준신호를 이용한 위성복원: I. 이론)

  • Woo Shik Kim
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.5
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    • pp.26-33
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    • 1994
  • Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing. In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded. This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with the Fourier transform magnitude of the desired signal and the information of the additive reference signal. In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented. In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal(s) is considered.

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Phase Retrieval Using an Additive Reference Signal: II. Reconstruction (더해지는 기준신호를 이용한 위성복원: II. 복원)

  • Woo Shik Kim
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.5
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    • pp.34-41
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    • 1994
  • Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with Fourier transform magnitude of the desired signal and the information of the additive reference signal In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal (s) is considered

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MULTIPLICATIVE FUNCTIONS COMMUTABLE WITH BINARY QUADRATIC FORMS x2 ± xy + y2

  • Poo-Sung, Park
    • Bulletin of the Korean Mathematical Society
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    • v.60 no.1
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    • pp.75-81
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    • 2023
  • If a multiplicative function f is commutable with a quadratic form x2 + xy + y2, i.e., f(x2 + xy + y2) = f(x)2 + f(x) f(y) + f(y)2, then f is the identity function. In other hand, if f is commutable with a quadratic form x2 - xy + y2, then f is one of three kinds of functions: the identity function, the constant function, and an indicator function for ℕ \ pℕ with a prime p.

A Study on the Characteristics of IT Innovations (정보기술 혁신 특성에 관한 연구)

  • 백상용;박경수
    • Journal of Information Technology Application
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    • v.3 no.3
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    • pp.71-90
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    • 2001
  • Research on innovation has a long tradition from various disciplines. MIS research has also been interested in the innovation adoption because IS development and implementation can be regarded as an innovation process. To deal with IT innovations, MIS research adopted existing innovation(diffusion) theories which have been developed on general innovation phenomena. However, the MIS innovation research revealed that there exist some limitations in applying the innovation theories to IT innovations and proposed that research on IT innovations should reflect the unique characteristics of IT. The purpose of this study is twofold; first, to synthesize the past research and propose the uniqueness of IT innovations and second, to survey the Perceptions of managers on If innovations. Three unique characteristics of IT innovations are derived from the past research which are (1) knowledge barriers, (2) increasing returns, (3) organizational decision. Data collected from 68 Korean managers showed that the perceptions of managers support the uniqueness of IT innovations. Some recommendations to facilitate IT innovations are discussed and further research directions are suggested.

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SPARSE GRID STOCHASTIC COLLOCATION METHOD FOR STOCHASTIC BURGERS EQUATION

  • Lee, Hyung-Chun;Nam, Yun
    • Journal of the Korean Mathematical Society
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    • v.54 no.1
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    • pp.193-213
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    • 2017
  • We investigate an efficient approximation of solution to stochastic Burgers equation driven by an additive space-time noise. We discuss existence and uniqueness of a solution through the Orstein-Uhlenbeck (OU) process. To approximate the OU process, we introduce the Karhunen-$Lo{\grave{e}}ve$ expansion, and sparse grid stochastic collocation method. About spatial discretization of Burgers equation, two separate finite element approximations are presented: the conventional Galerkin method and Galerkin-conservation method. Numerical experiments are provided to demonstrate the efficacy of schemes mentioned above.