• 대한한의학방제학회지
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• 제13권1호
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• pp.103-121
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• 2005

Alzheimer's disease(AD) is a geriatric dementia that is widespread in old age. In the near future AD will be the biggest problem in public health service. It has been widely believed that $A{\beta}$ peptide devided from APP causes apoptotic neurotoxicity in AD brain. However, recent evidence suggests that n99 may be an important factor causing neurotoxicity in AD. Mouse Neuro 2A cells expressed with CT99 exhibited remarkable apoptotic cell damage. We invesgated the protective effects of Ikgiansintang water extract(IGA). Findings from our experiment have shown that IGA inhibits the activities of CT99, which has neurotoxicities and apoptotic activities in cell line. In addition treatment of IGA($50{\mu}g/ml$ for 24 hours) partially prevented CT99-induced cytotoxicity in Neuro 2A cells. As the result of this study, In IGA group, the apoptosis in the nervous system is inhibited, the repair against the degerneration of Neuro 2A cells by n99 expression is promoted. Base on these findings, IGA may be beneficial for the treatment of AD.

• 디지털융복합연구
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• 제17권1호
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• pp.239-247
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• 2019

감정을 정확히 예측하는 것은 환자중심의 의료디바이스 개발 및 감성관련 산업에서 매우 중요한 이슈이다. 감정예측에 관한 많은 연구 중 감정 예측에 심박 변동성과 뉴로-퍼지 접근법을 적용한 연구는 없다. 본 연구는 HRV를 이용한 ANFEP(Adaptive Neuro Fuzzy system for Emotion Prediction)을 제안한다. ANFEP의 핵심 기능은 인공 신경망과 퍼지시스템을 통합해 예측 모델을 학습하는 ANFIS(Adaptive Neuro-Fuzzy Inference System)에 기반한다. 제안 모형의 검증을 위해 50명의 실험자를 대상으로 청각자극으로 감정을 유발하고, 심박변이도를 구하여 ANFEP 모형에 입력하였다. STDRR과 RMSSD를 입력으로 하고 입력변수 당 2개의 소속함수로 하는 ANFEP모형이 가장 좋은 결과를 나타났다. 제안한 감정예측 모형을 선형회귀 분석, 서포트 벡터 회귀, 인공신경망, 랜덤 포레스트와 비교한 결과 본 제안모형이 가장 우수한 성능을 보였다. 연구 결과는 보다 적은 입력으로 신뢰성 높은 감정인식이 가능함을 입증했고, 이를 활용해 보다 정확하고 신뢰성 높은 감정인식 시스템 개발에 대한 연구가 필요하다.

• Clinical and Experimental Pediatrics
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• 제55권9호
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• pp.354-357
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• 2012

Behçet disease (BD) is rare in childhood. We report a 9-year-old boy with neuro-Behçet disease who presented diplopia and weakness on the left side after a cerebral concussion. Brain magnetic resonance imaging (MRI) revealed hyperintensity of the right mesodiencephalic junction on T2-weighted and fluid attenuated inversion recovery images. Prednisolone administration resulted in complete remission and normalization of abnormal MRI finding. Brain MRI is a useful diagnostic tool when the neurological sign is the first symptom of subclinical BD.

• 상하수도학회지
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• 제26권4호
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• pp.547-554
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• 2012

As a national river remediation project has been completed, this study has a special interest on the capabilities to predict water levels at various points of the Geum River. To be endowed with intelligent forecasting capabilities, the author formulate the neuro-genetic algorithm associated with the short-term water level prediction model. The results show that neuro-genetic algorithm has considerable potentials to be practically used for water level forecasting, revealing that (1) model optimization can be obtained easily and systematically, and (2) validity in predicting one- or two-day ahead water levels can be fully proved at various points.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1021-1024
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• 2000

In this paper, a neuro-fuzzy system for quantitative characterization of chaotic signals is proposed. The proposed system is differ from the previous methods in that the nonlinear functions of the nonlinear dynamical systems are calculated as the invariant factor. In the proposed neuro-fuzzy system, the nonlinear functions are determined by supervised learning. From the reconstructed nonlinear functions, the proposed system can generate extrapolated chaotic signals. This feature will help the study of nonlinear dynamical systems which require large number of chaotic data. To confirm the validity of the proposed system, nonlinear functions are reconstructed from 1-dimensional and 2-dimensional chaotic signals.

• E2M - 전기 전자와 첨단 소재
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• 제11권11호
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

• 전자공학회논문지B
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• 제31B권4호
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• pp.155-165
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• 1994

An analog neural network circuit of rhigh density integration is introduced. It's prototype chip is designed in 3 by 3 mm2 die. It uses only one MOSFET to implement a synapse. The number of synapses per neuron can be expanded by cascading several chips. The influence of nonlinearity in synapses is analyzed. A formalization of the back propagation which can be applied to this circuit is shown. Some simulation results are shown and disscussed.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

• The Korean Journal of Pain
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• 제10권2호
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• pp.301-303
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• 1997

A 35-year-old female patient was referred to our hospital with neurologic symptoms after continuous epidural block performed 2 days earlier. She die not have any prior no previous lumbar surgery or experience trauma, intraspinal hemorrhage, infections or other known causative factors to associate with neurologic symptoms. Continuous epidural block is widely used for postoperative pain control. Complications can occur with this block including postduralpuncture headache, epidural abscess and rare cases of arachnoiditis etc. We experienced such a case of spinal arachnoiditis after continuous epidural block. Neurologic examination revealed painful bilateral hypoesthesia below $S_2$ level dermatomes, urinary and fecal incontinence and various degrees of leg weakness. The following day, the patient was noted to have bilateral sacral radiculopathies and lesion on proximal portion of both tibial nerve. CSF study reported: protein 264 mg/dl, sugar 64 mg/dl, WBC $7/mm^3$. L-spine MyeloCTscan results were unremarkable. She was discharged after a month of hospitalization and has regular checkups but her neurologic symptoms show no signs of improvement.