• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.491-498
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• 2022

In this paper, as a liberal arts course for non-major students, a case study of recurrent neural network SW practice, which is essential for designing a basic recurrent neural network subject curriculum, was developed. The developed SW practice case focused on understanding the operation principle of the recurrent neural network, and used a spreadsheet to check the entire visualized operation process. The developed recurrent neural network practice case consisted of creating supervised text completion training data, implementing the input layer, hidden layer, state layer (context node), and output layer in sequence, and testing the performance of the recurrent neural network on text data. The recurrent neural network practice case developed in this paper automatically completes words with various numbers of characters. Using the proposed recurrent neural network practice case, it is possible to create an artificial intelligence SW practice case that automatically completes by expanding the maximum number of characters constituting Korean or English words in various ways. Therefore, it can be said that the utilization of this case of basic practice of recurrent neural network is high.

• Korean Journal of Mathematics
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• v.31 no.2
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• pp.139-144
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• 2023

In this paper, we show that a recurrent manifold with harmonic curvature tensor is locally symmetric and that an Einstein and conformally recurrent manifold is locally symmetric. As a consequence, Einstein and recurrent manifolds must be locally symmetric. On the other hand, we have obtained some results for a (conformally) recurrent manifold with parallel vector field and also investigated some results for a (conformally) recurrent manifold with concircular vector field.

• Annual Conference of KIPS
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• 2017.11a
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• pp.835-838
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• 2017

최근 다양한 네트워크 서비스와 응용들이 생겨나면서, 네트워크상에 다양한 네트워크 트래픽이 발생하고 있다. 이로 인하여, 네트워크에 불필요한 네트워크 트래픽도 많이 발생하면서 네트워크 성능에 저하를 발생 시키고 있다. 따라서, 네트워크 트래픽 분류를 통하여 빠르게 제공되어야 하는 네트워크 서비스를 빠르게 전송 할 수 있도록 각 네트워크 트래픽마다의 분류가 필요하다. 본 논문에서는 Deep Learning 기법 중 Recurrent Neural Network를 이용한 플로우 기반의 네트워크 트래픽 분류를 제안한다. Deep Learning은 네트워크 관리자의 개입 없이 네트워크 트래픽 분류를 할 수 있으며, 이를 위하여 네트워크 트래픽을 Recurrent Neural Network에 적합한 데이터 형태로 변환한다. 변환된 데이터 세트를 이용하여 훈련시킴으로써 네트워크 트래픽을 분류한다. 본 논문에서는 훈련시킨 결과를 토대로 비교 분석 및 평가를 진행한다.

• Honam Mathematical Journal
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• v.35 no.2
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• pp.119-128
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• 2013

In the present paper, we define a product-symmetric recurrent-metric connection in an almost Hermitian manifold and study some properties of this connection, in particular, its curvature properties.

• The Pure and Applied Mathematics
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• v.24 no.2
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• pp.53-68
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• 2017

The object of the present paper is to study generalized Z-recurrent manifolds. Some geometric properties of generalized Z-recurrent manifolds have been studied under certain curvature conditions. Finally, we give an example of a generalized Z-recurrent manifold.

• Korean Journal of Adult Nursing
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• v.20 no.6
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• pp.905-916
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• 2008

Purpose: The purpose of this study was to identify the influencing factors on the single and recurrent falls in community-dwelling older women. Methods: Seventy eight volunteers aged over 65 were included in the study. The participants experienced at least one fall within the past one year. Data were measured on each participant from May 2007 to September 2007, collected using structured researcher-administered sheets and measuring their physical strengths and analyzed by descriptive statistics, t-test, chi-square test, Mann-Whitney U test and logistic regression analysis. Results: The prevalence of recurrent falls were 53.8%. The level of education(Z = -2.455, p = .014) and the presence of spouse($x^2$ = 4.843, p = .044) showed significant differences between the single-fall group and the recurrent-fall group in the study. Significantly predicting factor on the recurrent falls was the level of education and the variable explained 20.1% of variants in the occurrence of recurrent falls. Conclusion: Although a variety of factors affected the single fall in the elderly women, the level of education and the presence of spouse proved to be the significant factors in their recurrent falls. These factors proven to be significant as the result of this should be reflected in the development of effective programs for preventing the elderly from recurrent falls.

• Honam Mathematical Journal
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• v.43 no.3
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• pp.539-545
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• 2021

In this paper, we study some symmetric and recurrent conditions of nearly cosymplectic manifolds. We prove that Ricci-semisymmetric and Ricci-recurrent nearly cosymplectic manifolds are Einstein and conformal flat nearly cosymplectic manifold is locally isometric to Riemannian product ℝ × N, where N is a nearly Kähler manifold.

• Communications of the Korean Mathematical Society
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• v.38 no.4
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• pp.1249-1260
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• 2023

In the present paper, we study a semi-symmetric recurrent metric connection and verify its various geometric properties.

• Journal of the Korean Mathematical Society
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• v.39 no.6
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• pp.953-961
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• 2002

Generalized Ricci-recurrent trans-Sasakian manifolds are studied. Among others, it is proved that a generalized Ricci-recurrent cosymplectic manifold is always recurrent Generalized Ricci-recurrent trans-Sasakian manifolds of dimension $\geq$ 5 are locally classified. It is also proved that if M is one of Sasakian, $\alpha$-Sasakian, Kenmotsu or $\beta$-Kenmotsu manifolds, which is gener-alized Ricci-recurrent with cyclic Ricci tensor and non-zero A (ξ) everywhere; then M is an Einstein manifold.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1380-1397
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• 2018

The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.