• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.112-115
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• 2022

The process of deep learning usually needs to deal with massive data which has greatly limited the development of deep learning technologies today. Convolutional Neural Network (CNN) structure is often used to solve image classification problems. However, a large number of images may be required in order to train an image in CNN, which is a heavy burden for existing computer systems to handle. If the image data can be compressed under the premise that the computer hardware system remains unchanged, it is possible to train more datasets in deep learning. However, image compression usually adopts the form of lossy compression, which will lose part of the image information. If the lost information is key information, it may affect learning performance. In this paper, we will analyze the effect of image compression on deep learning performance on CNN-based cat and dog classification. Through the experiment results, we conclude that the compression of images does not have a significant impact on the accuracy of deep learning.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.509-514
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• 2000

A structural control algorithm using CMAC(Cerebellar Model Articulation Controller) neural network is proposed Learning rule for CMAC is derived based on cost function. Learning convergence of CMAC is compared with MLNN(Multilayer Neural Network). Numerical examples are shown to verify the proposed control algorithm. Examples show that CMAC can be applicable to structural control with fast learning speed.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.495-511
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• 2013

In this paper, a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.25-32
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• 1996

This paper will propose the direct adaptive learning control scheme based on adaptive control technique and intelligent control theory for a nonlinear system. Using the proposed learning control scheme, we will apply to on-line control an uncertain but for model perfect matching, it's structure condition is known. The effectiveness of the proposed control schem will be illustrated by simulations of a robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.842-846
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• 1993

Because of complexity, neural network is difficult to learn. So if internal representation[1] can be performed successfully, it is possible to use perceptron learning rule. As a result, learning is easier. Therefore the method of internal representations applied to the "XOR" problem, and the "spirals" problem. And then using the above results, the structure of neural network for computing is embodied.mputing is embodied.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.2-42
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• 2002

We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.172-176
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• 1989

In this paper, a simple method for designing iterative learning control scheme is proposed. The proposed learning algorithm is designed based on series expansion of inverse plant model. The proposed scheme has simple structure and fast convergency so that it is suitable for implementing it on conventional micro processor based controllers. The effectiveness of the proposed algorithm is investigated through a series of computer simulations.

• East Asian mathematical journal
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• v.29 no.2
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• pp.137-168
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• 2013

In this paper we study activity theory and Davydov's learning activity theory. We analyze brief history of activity theory in Russia, structure of human activity, and Davydov's studies in activity theory. Especially we analyze Davydov's 1st grade mathematics textbook, and try to investigate embodiment of Davydov's learning activity theory in his mathematics textbook.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.484-489
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• 1992

The inference of fuzzy controller can be considered a mapping from the controller input to membership value. The membership value, a kind of weight, has a role to decide if the input is appropriate to the rule. The membership function is described by several values, which are decided by a learning method. The learning method is adopted from adaptive filtering theory. The simulation shows the proposed fuzzy controller can learn linear and nonlinear functions. the structure of the proposed fuzzy controller becomes a kind of neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.364-367
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• 1995

In this paper, a design method of learning flight control system via input matching is proposed. The proposed learning control system is a simple structure which has an artificial neural network and feedback mechanism, and it is a useful method to control nonlinear systems.