• 한국수학교육학회지시리즈B:순수및응용수학
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• 제17권4호
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• pp.307-319
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• 2010

We provide a new semilocal convergence analysis of the Gauss-Newton method (GNM) for solving nonlinear equation in the Euclidean space. Using our new idea of recurrent functions, and a combination of center-Lipschitz, Lipschitz conditions, we provide under the same or weaker hypotheses than before [7]-[13], a tighter convergence analysis. The results can be extented in case outer or generalized inverses are used. Numerical examples are also provided to show that our results apply, where others fail [7]-[13].

• 천문학회보
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• 제44권1호
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• pp.50.4-50.4
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• 2019

We detect a new population of chromospheric jets in a polar coronal hole observed by Hinode/SOT. The propagating speed of the jets ranges in 30 - 490 km/s whose duration time is 3 - 52 s. The recurrent rate is approximately 3/min for a give segment of 1 arc-second horizontal interval. These jets are seemed to be more transient and faster than type II spicules at chromosphere, while the properties are compatible with the network jets seen in emission lines of transition region. We will discuss the implication of these jets for a coronal heating.

• 충청수학회지
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• 제35권1호
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• pp.91-101
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• 2022

We study some properties of nonwandering set Ω(𝜙) and chain recurrent set CR(𝜙) for an expansive flow which has the POTP on a compact TVS-cone metric spaces. Moreover we shall prove a spectral decomposition theorem for an expansive flow which has the POTP on TVS-cone metric spaces.

• 대한수학회논문집
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• 제34권4호
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• pp.1315-1328
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• 2019

The object of the present paper is to study ${\eta}$-recurrent ${\ast}$-Ricci tensor, ${\ast}$-Ricci semisymmetric and globally ${\varphi}-{\ast}$-Ricci symmetric contact metric generalized (${\kappa}$, ${\mu}$)-space form. Besides these, ${\ast}$-Ricci soliton and gradient ${\ast}$-Ricci soliton in contact metric generalized (${\kappa}$, ${\mu}$)-space form have been studied.

• 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.

• 대한수학회지
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• 제52권1호
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• pp.23-41
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• 2015

We present a semilocal convergence analysis of a third order method for approximating a locally unique solution of an equation in a Banach space setting. Recently, this method was studied by Chun, Stanica and Neta. These authors extended earlier results by Kou, Li and others. Our convergence analysis extends the applicability of these methods under less computational cost and weaker convergence criteria. Numerical examples are also presented to show that the earlier results cannot apply to solve these equations.

• 천문학회지
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• 제47권6호
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• pp.311-317
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• 2014

Solar chromospheric surges are often reported to contain rotational motion. However, the details of the motion and driving mechanism of the surges are not yet fully understood. Recurrent surges with rotational motion at AR 10930 on the west limb are observed by Hinode Solar Optical Telescope (SOT) continuously from 11:21 UT on December 18 to 09:58 UT on December 19, 2006, using the $Ca_{II}$ H broadband filter. We analyze details of the motion including number of turns from the rise of the surge to the fall, axial speed and acceleration. During the observation, rise and fall motion accompanying rotation appears recurrently. There occur a total of 14 surges at AR 10930 over 17 hours. The average duration is 45 minutes, and the average width, and length are 8 Mm, and 39 Mm, respectively. We speculate that the surges occurred by recurrent reconnections between the twisted prominence and large untwisted flux tube.

• East Asian mathematical journal
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• 제27권3호
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• pp.319-337
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• 2011

We approximate a locally unique solution of a nonlinear equation in a Banach space setting using an inexact two-step Newton-type method. It turn out that under our new idea of recurrent functions, our semilocal analysis provides tighter error bounds than before, and in many interesting cases, weaker sufficient convergence conditions. Applications including the solution of nonlinear Chandrasekhar-type integral equations appearing in radiative transfer and two point boundary value problems are also provided in this study.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• 대한족부족관절학회지
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• 제24권4호
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• pp.168-172
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• 2020

Chronic recurrent peroneal dislocation often responds poorly to conservative treatment. Surgical treatment has been reported to be more effective than conservative treatment, and various surgical treatment methods are available: superior peroneal retinaculum repair or reattachment, peroneal groove deepening procedures, rerouting procedures, or bone block procedures. Although various treatment options have been reported, there is no consensus regarding which treatment is better. This paper proposes a distal fibular rotational plasty that can prevent recurrent peroneal dislocations and recover its function well by securing a stable peroneal tendon excursion space.