• 제목/요약/키워드: Recurrent set

검색결과 102건 처리시간 0.026초

DISTRIBUTIONAL CHAOS AND DISTRIBUTIONAL CHAOS IN A SEQUENCE OCCURRING ON A SUBSET OF THE ONE-SIDED SYMBOLIC SYSTEM

  • Tang, Yanjie;Yin, Jiandong
    • 대한수학회보
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    • 제57권1호
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    • pp.95-108
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    • 2020
  • The aim of this paper is to show that for the one-sided symbolic system, there exist an uncountable distributively chaotic set contained in the set of irregularly recurrent points and an uncountable distributively chaotic set in a sequence contained in the set of proper positive upper Banach density recurrent points.

SOME PROPERTIES OF THE STRONG CHAIN RECURRENT SET

  • Fakhari, Abbas;Ghane, Fatomeh Helen;Sarizadeh, Aliasghar
    • 대한수학회논문집
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    • 제25권1호
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    • pp.97-104
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    • 2010
  • The article is devoted to exhibit some general properties of strong chain recurrent set and strong chain transitive components for a continuous map f on a compact metric space X. We investigate the relation between the weak shadowing property and strong chain transitivity. It is shown that a continuous map f from a compact metric space X onto itself with the average shadowing property is strong chain transitive.

A NOTE ON MINIMAL SETS OF THE CIRCLE MAPS

  • Yang, Seung-Kab;Min, Kyung-Jin
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제5권1호
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    • pp.13-16
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    • 1998
  • For continuous maps f of the circle to itself, we show that (1) every $\omega$-limit point is recurrent (or almost periodic) if and only if every $\omega$-limit set is minimal, (2) every $\omega$-limit set is almost periodic, then every $\omega$-limit set contains only one minimal set.

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RECURRENCE AND STABILITY OF POINTS IN DISCRETE FLOWS

  • KOO, KI-SHIK
    • Journal of applied mathematics & informatics
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    • 제37권3_4호
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    • pp.251-257
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    • 2019
  • We study the orbit behaviours of recurrent, uniformly recurrent and Poisson stable points. we give conditons that a point is to be recurrent or uniformly recurrent by analyzing the behaviours of their orbits. Also, we study dynamical properties of equicontinuous points and points of characteristic $0^+$.

ON THE LIMIT SETS AND THE BASIC SETS OF CHAIN RECURRENT SETS

  • Koo, Ki-Shik
    • Journal of applied mathematics & informatics
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    • 제7권3호
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    • pp.1029-1038
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    • 2000
  • In this paper, we show that if x is a positively Lyapunov stable point of an expansive homeomorphism with the pseudo-orbit-tracing-property, then x is a periodic point or its positive limit set consists of only one periodic orbit, and their periods are predictable. We give a necessary and sufficient condition that a basic set is to be a sink or source. Also, we consider some dynamical properties of basic sets.

Analysis of bivariate recurrent event data with zero inflation

  • Kim, Taeun;Kim, Yang-Jin
    • Communications for Statistical Applications and Methods
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    • 제27권1호
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    • pp.37-46
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    • 2020
  • Recurrent event data frequently occur in clinical studies, demography, engineering reliability and so on (Cook and Lawless, The Statistical Analysis of Recurrent Events, Springer, 2007). Sometimes, two or more different but related type of recurrent events may occur simultaneously. In this study, our interest is to estimate the covariate effect on bivariate recurrent event times with zero inflations. Such zero inflation can be related with susceptibility. In the context of bivariate recurrent event data, furthermore, such susceptibilities may be different according to the type of event. We propose a joint model including both two intensity functions and two cure rate functions. Bivariate frailty effects are adopted to model the correlation between recurrent events. Parameter estimates are obtained by maximizing the likelihood derived under a piecewise constant hazard assumption. According to simulation results, the proposed method brings unbiased estimates while the model ignoring cure rate models gives underestimated covariate effects and overestimated variance estimates. We apply the proposed method to a set of bivariate recurrent infection data in a study of child patients with leukemia.

Development and cross-sectional morphology of the recurrent laryngeal nerves in human fetuses

  • Maria Cecilia Baratela;William Paganini Mayer;Josemberg da Silva Baptista
    • Anatomy and Cell Biology
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    • 제57권3호
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    • pp.392-399
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    • 2024
  • The recurrent laryngeal nerve is a bilateral branch of the vagus nerve that is mainly associated with the motor innervation of the intrinsic muscles of the larynx. Despite its bilateral distribution, the right and left recurrent laryngeal nerves display unequal length due to embryological processes related to the development of the aortic arches. This length asymmetry leads to theories about morphological compensations to provide symmetrical functions to the intrinsic muscles of the larynx. In this study we investigated the developmental and cross-sectional morphometrics of the recurrent laryngeal nerves in human fetuses. Fifteen stillbirth fetuses donated to anatomical and medical research were used for investigation. Fetuses had intrauterine age ranging from 30 to 40 weeks estimated by biometry methods. Specialized anatomical dissection of the visceral block of the neck was performed to prepare histological samples of the recurrent laryngeal nerves in its point of contact with the larynx, and morpho-quantitative techniques were applied to evaluate the epineurium and perineural space of the recurrent laryngeal nerves. No statistical difference in the cross-sectional morphology of the epineurium and perineural space between right and left recurrent laryngeal nerves intra-individually was confirmed, however, we found evidence that these structures are under greater development in the left recurrent laryngeal nerve during 30 to 40 weeks of intrauterine life. Our data suggest that the nerves are under morphological development that possibly set the stage for accommodation of larger diameter and myelinization of the left recurrent laryngeal nerve during post-natal life.

시계열 자료의 예측을 위한 베이지안 순환 신경망에 관한 연구 (A Study on the Bayesian Recurrent Neural Network for Time Series Prediction)

  • 홍찬영;박정훈;윤태성;박진배
    • 제어로봇시스템학회논문지
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    • 제10권12호
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    • pp.1295-1304
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    • 2004
  • In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

THE CHAIN RECURRENT SET ON COMPACT TVS-CONE METRIC SPACES

  • Lee, Kyung Bok
    • 충청수학회지
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    • 제33권1호
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    • pp.157-163
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    • 2020
  • Conley introduced attracting sets and repelling sets for a flow on a topological space and showed that if f is a flow on a compact metric space, then 𝓡(f) = ⋂{AU ∪ A*U |U is a trapping region for f}. In this paper we introduce chain recurrent set, trapping region, attracting set and repelling set for a flow f on a TVS-cone metric space and prove that if f is a flow on a compact TVS-cone metric space, then 𝓡(f) = ⋂{AU ∪ A*U |U is a trapping region for f}.