• School Mathematics
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• v.4 no.3
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• pp.361-373
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• 2002

In this paper, a series of tasks related on polygonal numbers and pyramidal numbers are suggested for using them as teaching unit materials for teaching and learning of sequences in junior high school mathematics. Especially, finding n-th term in those seque-nces, relations among polygonal numbers, and relations among Pyramidal numbers are focused on. A series of tasks related on polygonal numbers and pyramidal numbers have three math-eucational values. First, they have a value as natural materials for teaching and teaming of finding nth term of original sequences using pro-gression of differences. Second, they have a value as materials for teaching and learning of mathematical thinking such as general-ization, analogy, etc. Third, they have a value as materials for teaching and learning of algebraic operation, proof, and connecting mathematical knowledges.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.441-446
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• 2000

This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta teaming method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.81-90
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• 2004

This paper presents new testability measure based on learning, which can be useful in the deterministic process of test pattern generation algorithms. This testability measure uses the structural information that are obtained by teaming. The proposed testability measure searches for test pattern that can early detect the conflict in case of the hardest decision problems. On the other hand in case of the easiest decision problem, it searches for test pattern that likely results in the least conflict. The proposed testability measure reduces CPU time to generate test pattern that accomplishes the same fault coverage as that of the distance-based measure.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.610-613
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• 2003

Ensuring technical ability is essential in the construction industry to increase competitiveness in the global market. A new paradigm is coming up in academic education system to cultivate the competent engineers. The major objective of this research is to suggest a positive learning pattern In order to overcome the limitations of the passive learning style. A case study, technical upgrading with a tower crane, us explained in terms of active learning process, results, and evaluation of students' performance.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.43-59
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• 2002

There has been a long debate whether the Sun revolves the Earth or the Earth revolves the Sun. Also, students are very interested in the solar systems, which means the entire system of planets, satellites, minor planets, comets, and meteoroids that orbit the sun. However, students are not sure about them. New models which enhance teaming about them are strongly needed. This study is intended to develop the new models on the solar systems and to investigate how the preservice elementary teachers' concepts are affected by them. Subjects are 20 preservice elementary teachers, One instrument including 11 items is used. Data are collected before using the new models and after using them through the tests. As a result, learning through the new models has a positive effects on the preservice elementary teachers' concepts on the solar system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.279-282
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• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.267-270
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• 2001

In this paper, we propose the self-autonomous algorithm for mobile robot system. The proposed mobile robot system which is teamed by learning with the neural networks can trace the target at the same distances. The mobile robot can evaluate the distance between robot and target with ultrasonic sensors. By teaming the setup distance, current distance and command velocity, the robot can do intelligent self-autonomous drive. We use the neural network and back-propagation algorithm as a tool of learning. As a result, we confirm the ability of tracing the target with proposed mobile robot.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.43-46
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• 2003

In this paper, a novel rotor flux estimation method of an induction motor using support vector machine(SVM) is presented. Two veil-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. The theory of the SVM algorithm is based on statistical teaming theory. Training of SVH leads to a quadratic programming(QP) problem. The proposed SVM rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of Proposed algorithm are throughly verified through numerical simulation.

• Journal of Korea Multimedia Society
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• v.2 no.4
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• pp.468-475
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• 1999

Learning algorithm of SRNN doesn't use analytic maximum epoch, so that its performance is inefficient and its cost is high. In this paper, with the proper maximum epoch, we improve teaming efficiency. We first describe cost function of maximum epoch and computation time theoretically Then, using it, we propose that maximum epoch must be between 400 and 500. Estimated maximum epoch is verified by experiment.