• Title/Summary/Keyword: random field

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SIZE DISTRIBUTION OF ONE CONNECTED COMPONENT OF ELLIPTIC RANDOM FIELD

  • Alodat, M.T.
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.479-488
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    • 2007
  • The elliptic random field is an extension to the Gaussian random field. We proved a theorem which characterizes the elliptic random field. We proposed a heuristic approach to derive an approximation to the distribution of the size of one connected component of its excursion set above a high threshold. We used this approximation to approximate the distribution of the largest cluster size. We used simulation to compare the approximation with the exact distribution.

A FUNCTIONAL CENTRAL LIMIT THEOREM FOR LINEAR RANDOM FIELD GENERATED BY NEGATIVELY ASSOCIATED RANDOM FIELD

  • Ryu, Dae-Hee
    • Journal of the Chungcheong Mathematical Society
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    • v.22 no.3
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    • pp.507-517
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    • 2009
  • We prove a functional central limit theorem for a linear random field generated by negatively associated multi-dimensional random variables. Under finite second moment condition we extend the result in Kim, Ko and Choi[Kim,T.S, Ko,M.H and Choi, Y.K.,2008. The invariance principle for linear multi-parameter stochastic processes generated by associated fields. Statist. Probab. Lett. 78, 3298-3303] to the negatively associated case.

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Image analysis using a markov random field and TMS320C80(MVP) (TMS320C80(MVP)과 markov random field를 이용한 영상해석)

  • 백경석;정진현
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1722-1725
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    • 1997
  • This paper presents image analysis method using a Markov random field(MRF) model. Particulary, image esgmentation is to partition the given image into regions. This scheme is first segmented into regions, and the obtained domain knowledge is used to obtain the improved segmented image by a Markov random field model. The method is a maximum a posteriori(MAP) estimation with the MRF model and its associated Gibbs distribution. MAP estimation method is applied to capture the natural image by TMS320C80(MVP) and to realize the segmented image by a MRF model.

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A Weak Convergence of the Linear Random Field Generated by Associated Randomvariables ℤ2

  • Kim, Tae-Sung;Ko, Mi-Hwa;Kim, Hyun-Chull
    • Communications for Statistical Applications and Methods
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    • v.15 no.6
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    • pp.959-967
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    • 2008
  • In this paper we show the weak convergence of the linear random(multistochastic process) field generated by identically distributed 2-parameter array of associated random variables. Our result extends the result in Newman and Wright (1982) to the linear 2-parameter processes as well as the result in Kim and Ko (2003) to the 2-parameter case.

Wireless Power Transfer System using Semi-random Magnetic Field (semi-random 자장을 사용하는 적합한 무선전력전송 시스템)

  • Lim, Dong-Nam;Lee, Dong-Su;Jeon, Seong-Jeub
    • Proceedings of the KIPE Conference
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    • 2014.07a
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    • pp.480-481
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    • 2014
  • In this paper, a feeder system for wireless power transfer is investigated, which generates semi-random magnetic field using three currents with different frequencies. A semi-random field is very useful to magnetizing a pickup irrespective of its posture.

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SOME SMALL DEVIATION THEOREMS FOR ARBITRARY RANDOM FIELDS WITH RESPECT TO BINOMIAL DISTRIBUTIONS INDEXED BY AN INFINITE TREE ON GENERALIZED RANDOM SELECTION SYSTEMS

  • LI, FANG;WANG, KANGKANG
    • Journal of applied mathematics & informatics
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    • v.33 no.5_6
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    • pp.517-530
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    • 2015
  • In this paper, we establish a class of strong limit theorems, represented by inequalities, for the arbitrary random field with respect to the product binomial distributions indexed by the infinite tree on the generalized random selection system by constructing the consistent distri-bution and a nonnegative martingale with pure analytical methods. As corollaries, some limit properties for the Markov chain field with respect to the binomial distributions indexed by the infinite tree on the generalized random selection system are studied.

Large Deviations for random walks with time stationary random distribution function

  • Hong, Dug-Hun
    • Journal of the Korean Mathematical Society
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    • v.32 no.2
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    • pp.279-287
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    • 1995
  • Let $F$ be a set of distributions on R with the topology of weak convergence, and let $A$ be the $\sigma$-field generated by the open sets. We denote by $F_1^\infty$ the space consisting of all infinite sequence $(F_1, F_2, \cdots), F_n \in F and R_1^\infty$ the space consisting of all infinite sequences $(x_1, x_2, \cdots)$ of real numbers. Take the $\sigma$-field $F_1^\infty$ to be the smallest $\sigma$-field of subsets of $F_1^\infty$ containing all finite-dimensional rectangles and take $B_1^\infty$ to be the Borel $\sigma$-field $R_1^\infty$.

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Application of Probability Density Function in SFEM and Corresponding Limit Value (추계론적 유한요소해석에서의 확률밀도함수 사용과 수렴치)

  • Noh Hyuk-Chun
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2006.04a
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    • pp.857-864
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    • 2006
  • Due to the difficulties in numerical generation of random fields that satisfy not only the probabilistic distribution but the spectral characteristics as well. it is relatively hard to find an exact response variability of a structural response with a specific random field which has its features in the spatial and spectral domains. In this study. focusing on the fact that the random field assumes a constant over the domain under consideration when the correlation distance tends to infinity, a semi-theoretical solution of response variability is proposed for in-plane and plate bending structures. In this procedure, the probability density function is used directly resulting in a semi-exact solution for the random field in the state of random variable. It is particularly noteworthy that the proposed methodology provides response variability for virtually any type of probability density functions.

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A FUNCTIONAL CENTRAL LIMIT THEOREM FOR ASSOCIATED RANDOM FIELD

  • KIM, TAE-SUNG;KO, MI-HWA
    • Honam Mathematical Journal
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    • v.24 no.1
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    • pp.121-130
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    • 2002
  • In this paper we prove a functional central limit theorem for a field $\{X_{\underline{j}}:{\underline{j}}{\in}Z_+^d\}$ of nonstationary associated random variables with $EX{\underline{j}}=0,\;E{\mid}X_{\underline{j}}{\mid}^{r+{\delta}}<{\infty}$ for some $r>2,\;{\delta}>0$and $u(n)=O(n^{-{\nu}})$ for some ${\nu}>0$, where $u(n):=sup_{{\underline{i}}{\in}Z_+^d{\underline{j}}:{\mid}{\underline{j}}-{\underline{i}}{\mid}{\geq}n}{\sum}cov(X_{\underline{i}},\;X_{\underline{j}}),\;{\mid}{\underline{x}}{\mid}=max({\mid}x_1{\mid},{\cdots},{\mid}x_d{\mid})\;for\;{\underline{x}}{\in}{\mathbb{R}}^d$. Our investigation implies and analogous result in the case associated random measure.

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