• 디지털융복합연구
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• 제4권2호
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• pp.143-151
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2006년도 춘계학술대회
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• pp.451-459
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.22.3-22
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• 2001

An attention task, in which context information should be extracted from the first presented pattern, and the recognition answer of the second presented pattern should be generated using the context information, is employed in this paper. An Elman-type recurrent neural network is utilized to extract and keep the context information. A reinforcement signal that indicates whether the answer is correct or not, is only a signal that the system can obtain for the learning. Only by this learning, necessary context information became to be extracted and kept, and the system became to generate the correct answers. Furthermore, the function of an associative memory is observed in the feedback loop in the Elman-type neural network.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Earthquakes and Structures
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• 제25권1호
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• pp.15-26
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• 2023

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.

• 한국컴퓨터정보학회논문지
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• 제25권3호
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• pp.51-55
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• 2020

본 논문에서는 이미지 복원에 사용되는 기존의 FAM (Fuzzy Associative Memory)에 유사성 학습을 채택하여 개선된 FAM을 제안한다. 이미지 복원은 노이즈가 존재하는 버전에서 원 이미지에 가깝게 복원하는 것을 의미한다. 얼굴 인식과 같은 중요한 적용 문제에서 이 프로세스는 잡음에 강하고 견고하며 빠르며 확장 가능해야한다. 기존의 FAM 은 강력한 퍼지 제어를 통하여 도메인에 적용 할 수 있지만 실제 응용 프로그램에서는 용량 문제가 있지만 단순한 단일 계층 신경망이다. 유사성 측정은 복구 된 이미지와 원본 이미지 사이의 제곱 평균 오차를 최소화하기 위해 FAM 구조의 연결 강도와 관련이 있다. 제안된 알고리즘의 효과는 실험에서 랜덤 노이즈로 인한 오류 크기가 현저히 낮아지는 것을 확인하였다.

• 대한한방내과학회지
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• 제27권1호
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• pp.253-264
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• 2006

Object: This research investigated effects of Jujadokseo-hwan on mice with Alzheimer's Disease induced by $amyloid-{\beta}$. According to Dongyibogam, Jujadokseo-hwan can cure amnesia. Amyloid-B is believed to induce oxidative stress and inflammation in the brain, postulated to play important roles in the pathogenesis of Alzheimer's disease. In this way $Amyloid-{\beta}$ induces Alzheimer's Disease. Methods : In order to make an efficient prescription and cope with dementia, learning and memory functions of mice were tested on passive avoidance test and V-maze task. $NF-{\kappa}B$ were measured from protein derived from the brain. RT-PCR was done for !gene analysis. Primers were protein kinase Band $NGF-{\alpha}$. Results : 1. Jujadokseo-hwan was effective for memory capacity on passive avoidance test. but noneffective for spatial memory capacity and locomotor activity on Y -maze task. 2. The measurement of $NF-{\kappa}B$ showed upward tendancies and the result of RT-PCR showed up-regulation when given Jujadokseo-hwan by mouth. Conclusion: Results suggest that Jujadokseo-hwan is effective on mice with Alzheimer's Disease induced by $amyloid-{\beta}$.

• 정보처리학회논문지B
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• 제11B권4호
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• pp.449-456
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• 2004

본 논문에서는 모호성을 포함하고 있는 시계열 패턴인식을 위한 새로운 인식모델인 순환퍼지기억장치를 제안한다. 순환퍼지기억장치는 기존의 퍼지기억장치에 순차적인 입력패턴를 처리하고 시간적 관련성을 표현할 수 있는 순환층을 추가함으로써 확장된 모델이다. 본 논문에서 제안하는 순환퍼지기억장치는 입력과 출력사이의 관련정도를 설정하기 위해 헤비안 방식의 학습알고리즘을 사용한다. 그리고 순환퍼지기억장치의 순환층에 필요한 가중치를 학습하기 위해서 오류역전파 알고리즘을 이용한다. 본 논문에서는 제안하는 모델을 음성신호의 경계를 추출하는 문제에 적용하여 성능을 평가한다.

• The Journal of Korean Physical Therapy
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• 제19권4호
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• pp.25-32
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• 2007

Purpose: The purpose of this study was to evaluate the effect of computerized neurocognitive function program on cognitive function about memory and attention with stroke. Methods: 24subjects with stroke were recruited. Twelve of subjects received conventional therapy including physical therapy, occupational therapy and language therapy. Another subjects received additional computer assisted cognitive training using Computer-aided Cognitive rehabilitation training system(COMCOG, MaxMedica Inc., 2004). All patients were assessed their cognitive function of memory and attention using Computerized Neurocognitive Function Test(CNT, MaxMedica Inc., 2004) before treatment and 6 weeks after treatment. Results: Before the treatment, two groups showed no difference in cognitive function(p>0.05). After 6 weeks, two groups showed significantly difference in digit span (forward, backward), verbal learning(A5, $A1{\sim}A5$), auditory CPT(n), visual CPT(n)(p<0.05). After treatment, the experimental group showed a significant improvement of digit span(forward, backward), verbal learning(A5, $A1{\sim}A5$), visual span (forward, backward), auditory CPT(n, sec), visual CPT(n, sec), and trail-making (A, B)(p<0.05). Conclusion: Computerized neurocognitive function program would be improved cognitive function of memory and attention in patients with stoke.