• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.457-464
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• 2014

Foreign language learners want to remember newly learned vocabularies for as long as possible. As demand for learning English as a second language grows, effective ways of memorizing English vocabularies also attract much interest so that various methods and apparatus are developed in order to support effective memorization. In this research, we introduce a new way of memorizing English vocabularies and evaluate the performance compared to an existing qualified method. Our study result shows that learners who memorize English words using our suggested method maintained a higher retention rate than those who studied using the existing method. From this research, we expect to gain new insights of effective way in learning English vocabularies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.448-459
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• 2012

It is important to understand how we can deal with elements for the modeling of neural networks when we are investigating the dynamical performance and the information processing capabilities. The information processing capabilities of model neural networks will change for different response, synaptic weights or learning rules. Using the statistical neurodynamics method, we evaluate the capabilities of neural networks in order to understand the basic concept of parallel distributed processing. In this paper, we explain the results of theoretical analysis of the effects of the hysteretic property on the performance of sequential associative neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1569-1572
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• 1997

In this paper, we present an associative memory network (AMN) controller for dynamic robot control. The purpose of using AMN is to reduce the size of required memory in storing and recalling large of daa representing input relationship of nonlinear functions. With the capability AMN can be used to dynamic robot control, which has nonlinear properties inherently. The proposed AMN control scheme has advantages for the inverse dynamics learning no limitatiion of inpur range, and insensitive of payload change. Computer simulations show the effectiveness and feasibility of proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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• pp.34-41
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• 1992

A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism stores a priori an initial knowledge base via approximate learning and utilizes this information for decision-making systems via fuzzy inferencing. We called this fuzzy computer architecture a 'fuzzy hypercube' processing all the rules in one clock period in parallel. Fuzzy hypercubes can be applied to control of a class of complex and highly nonlinear systems which suffer from vagueness uncertainty. Moreover, evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability together with parameter sensitivity.

• Journal of the Korean Institute of Gas
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• v.7 no.3 s.20
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• pp.24-31
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• 2003

To operate process plant safely and economically, process monitoring is very important. There are a great number of data acquired through distributed control system and process information system. Fault monitoring is the task with difficulties owing to not only the huge amount of data, but also nonlinearity of chemical processes. In this research, the program, REFA, based on PCA and functional link associative neural network has developed. REFA has better learning capabilities, generalization abilities, and shorter learning time than existing neural network programs. In this work its usefulness has proven by application to Tennessee Eastman process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.120-123
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• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.37-45
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• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.181-184
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• 2001

인간 두뇌의 연상과 기억 작용의 모델링을 통한 구현의 일부분으로, 본 논문에서는 Hebb 의 학습방법과 non-cloning template를 사용하여 discrete-time cellular neural networks의 연상메모리 기능을 구현한다. 본 논문에서 사용된 학습방법은 각 셀의 인접한 셀과의 연결상태에 따라 하중값 메트릭스를 구현한다. 이러한 방법은 새로운 패턴의 추가 학습과 삭제가 쉽고, 또한 쉽게 구현 할 수 있는 장점이 있다. 이 방법으로 모의 실험에서는 교통표지판의 분류에 사용한다.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.77-88
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• 1993

In this paper, we propose a stereo vision system to solve correspondence problem with large disparity and sudden change in environment which result from small distance between camera and working objects. First of all, a specific feature is divided by predfined elementary feature. And then these are combined to obtain coded data for solving correspondence problem. We use Neural Network to extract elementary features from specific feature and to have adaptability to noise and some change of the shape. Fourier transformation and Log-polar mapping are used for obtaining appropriate Neural Network input data which has a shift, scale, and rotation invariability. Finally, we use associative memory to obtain coded data of the specific feature from the combination of elementary features. In spite of specific feature with some variation in shapes, we could obtain satisfactory 3-dimensional data from corresponded codes.

• Journal of Digital Convergence
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• v.4 no.2
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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.