• Archives of Craniofacial Surgery
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• v.20 no.6
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• pp.416-420
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• 2019

Reconstruction method choice in recurrent head and neck cancer depends on surgical history, radiation therapy dosage, conditions of recipient vessels, and general patient condition. Furthermore, when defects are multiple or three dimensional in nature, reconstruction and flap choice aimed at rebuilding the functional structure of the head and neck are difficult. We experienced successful reconstruction of recurrent laryngeal cancer requiring reconstruction of esophageal and tracheostomy stroma defects using a chimeric two-skin anterolateral thigh flap with a single pedicle.

• Annals of Clinical Neurophysiology
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• v.7 no.2
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• pp.114-116
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• 2005

Idiopathic facial nerve palsy, or Bell's palsy(BP) is an acute paralysis of the facial muscles innervated by the seventh cranial nerve. The cause and prognosis of recurrent BP are various. The frequency and heterogenicity of etiology suggest a predisposing factor or immune mechanisms. About 10% to 15% of patients with BP will suffer a recurrence, and less than 1.5% will have more than 4 episodes. We report four patients of chronic recurrent BPs.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.2
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• pp.241-250
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• 2010

A recurrent adaptive model-free intelligent control with a friction estimation law is proposed to enhance the positioning performance of the mover in PMLSM system. For the PMLSM with nonlinear friction and uncertainty, an adaptive recurrent fuzzy neural network(ARFNN) and compensated control law in $H_{\infty}$ performance criterion are designed to mimic a perfect control law and compensate the approximated error between ideal controller and ARFNN. Combined with friction observer to estimate nonlinear friction parameters of the LuGre model, on-line adaptive laws of the controller and observer are derived based on the Lyapunov stability criterion. To analyze the effectiveness our control scheme, some simulations for the PMLSM with nonlinear friction and uncertainty were executed.

• Journal of Pharmacopuncture
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• v.23 no.1
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• pp.37-41
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• 2020

Objectives: Round Acupuncture having blunt end has developed from acupotomy. This case report is to find out that Round Acupuncture is effective in treating patients with recurrent carpal tunnel syndrome (CTS), which has not improved by steroid injection or acupotomy. Methods: Round Acupuncture was inserted into the distal fibers of transverse carpal ligament and released toward the proximal fibers. Treatment was performed three times in total. T ingling, numbn ess, night pain and swelling sensation were assessed, and provocative maneuvers were also used. Results: After treat ment, all symptoms completely disappeared and the patient had no recurrence until 3 months after treatment. Conclusion: Round Acupuncture co uld be an effective treatment for recurrent CTS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.185-188
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• 1997

In this paper, we execute identification, linearization, and control of a nonlinear system using recurrent neural networks. In general nonlinear control system become complex because of nonlinearity and uncertainty. And though we compose nonlinear control system based on the model, it is difficult to get good control ability. So we identify the nonlinear control system using the recurrent neural networks and execute feedback linearization of identified model, In this process we choose the optional linear system, and the system which will have to be feedback linearized if trained to follow the linearity between input and output of the system we choose. We the feedback linearized system by applying standard linear control strategy and simulation. And we evaluate the effectiveness by comparing the result which is linearized theoretically.

• Nuclear Engineering and Technology
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• v.41 no.10
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• pp.1293-1306
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• 2009

In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships.

• Proceedings of the Korea Information Processing Society Conference
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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.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• 대한생식의학회:학술대회논문집
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• 2001.11a
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• pp.76-76
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• 2001

• 대한생식의학회:학술대회논문집
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• 2002.05a
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• pp.95-95
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• 2002