• Bulletin of the Korean Mathematical Society
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• v.45 no.4
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• pp.689-700
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• 2008

In this paper we prove that a $\phi$-recurrent (k, $\mu$)-contact metric manifold is an $\eta$-Einstein manifold with constant coefficients. Next, we prove that a three-dimensional locally $\phi$-recurrent (k, $\mu$)-contact metric manifold is the space of constant curvature. The existence of $\phi$-recurrent (k, $\mu$)-manifold is proved by a non-trivial example.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.267-272
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• 2010

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.

• Journal of Korean Neurosurgical Society
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• v.47 no.1
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• pp.26-29
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• 2010

Objective: Recurrent lumbar disc herniation has been reported to occur in 5% to 15% of surgically treated primary lumbar disc herniation cases. We investigated the molecular biologic characteristics of primary herniated discs and recurrent discs to see whether the recurrent discs has the similar biological features with primary herniated discs. Methods: Primary hemiated disc and recurrent disc cells were obtained by discectomy of lumbar disc patients and cells were isolated and then taken through monolayer cultures. We compared chondrogenic and osteogenic mRNA gene expression, and western blot between the two groups. Results: The mRNA gene expression of recurrent disc cells were increased 1.47* times for aggrecan, 1.38 times for type I collagen, 2.04 times for type II collagen, 1.22 times for both Sox-9 and osteocalcin, and 1.31 times for alkaline phosphatase, respectively, compared with the primary herniated lumbar disc cells (*indicates p < 0.05). Westem blot results for each aggrecan, type I collagen, type II collagen, Sox-9, osteocalcin, and alkaline phosphatase were similar between the primary herniated disc cells and recurrent disc cells. Conclusion: These results indicate that the recurrent disc cells have similar chondrogenic and osteogenic gene expression compared to primary herniated disc cells. Therefore, we assumed that the regeneration of remaining discs could fill the previous discectomy space and also it could be one of the factors for disc recurrence especially in the molecular biologic field.

• Archives of Plastic Surgery
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• v.36 no.6
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• pp.714-719
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• 2009

Purpose: Recurrent ischial pressure sore is troublesome for adequate soft tissue coverage, because usually its pocket has a very large deep space and adjacent donor tissue have been scarred in the previous surgery. However, the conventional reconstructive methods are very difficult to overcome them. Modified gluteus maximus myocutaneous V - Y advancement flap from buttock can be successfully used in these circumstances. Methods: From February 2007 to October 2008, modified gluteus maximus myocutaneous V - Y advancement flaps were perfomed in 10 paraplegic patients with recurrent ischial pressure sore. The myocutaneous flap based on the inferior gluteal artery was designed in V - shaped pattern toward the superolateral aspect of buttock and was elevated from adjacent tissue. Furthermore, when additional muscular bulk was required to obliterate dead space, the flap dissection was extended to the inferolateral aspect which can included the adequate amount of the gluteal muscle. After the advanced flap was located in sore pocket, donor defect was repaired primarily. Results: The patients' mean age was 46.9 and the average follow - up period was 12.4 months. The immediate postoperative course was uneventful. But, two patients were treated through readvancement of previous flap due to wound dehiscence or recurrence after 6 months. The long - term results were satisfied in proper soft tissue bulk and low recurrence rate. Conclusions: The modified gluteus maximus myocutaneous V - Y advancement flap may be a reliable method in reconstruction of recurrent ischial pressure sore, which were surrounded by scarred tissue because of its repetitive surgeries and were required to provide sufficient volume of soft tissue to fill the large pocket.

• Communications of the Korean Mathematical Society
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• v.2 no.2
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• pp.229-234
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• 1987

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need 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, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, 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 network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of the Chungcheong Mathematical Society
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• v.36 no.2
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• pp.99-105
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• 2023

In this paper we study some topological modes of recurrent sets of linear homeomorphisms of a finite-dimensional topological vector space. More precisely, we show that there are no chain transitive linear homeomorphisms of a finite-dimensional Banach space having the shadowing property. Then, we give examples to illustrate our results.

• Communications of the Korean Mathematical Society
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• v.32 no.1
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• pp.119-133
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• 2017

In this paper, we study half lightlike submanifolds of an indefinite Kaehler manifold with a semi-symmetric non-metric connection. First, we characterize the geometry of two types of half lightlike submanifolds of such an indefinite Kaehler manifold. Next, we investigate the geometry of half lightlike submanifolds of an indefinite complex space form with a semi-symmetric non-metric connection.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.534-538
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• 1987

State-space techniques are employed to derive an equivalent nonlinear recurrent time-domain model that describes the series resonant converter behavior exactly. This model is employed effectively to analyze large signal behavior by propagating the recurrent equation and matching boundary conditions through digital computation. The model is verified with a laboratory converter for a steady-state operation.