• 정보화사회
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• s.38
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• pp.44-49
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• 1991

통신망의 디지탈화에 의한 정보통신시스템 또는 종합정보통신망의 구축이 세계적인 추세인 가운데 교환 및 전송시스템의 디지탈화가 급속도로 진행되고 있다. 본고에서는 동기의 기본개념과 동기망 품질 등 망동기 기술 전반에 관해 3회로 나눠 소개한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.95-101
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• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.11
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• pp.3507-3515
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• 1996

This paper presents an attempt to find the physical structure of dynamic systems which achieves the behavior of a given system function. The scheme pursued by the paper would be regarded as synthesizing dynamic systems, and a method to synthesize them analytically is proposed by means of bond graph prototypes. The method adopts several conceptsused to synthesize networks in the electrical field, but yet deconstrates its own strengths such as the freedom from assigning causality and determining junction types. Also, itis shown that this method has further advantages in reticulating a given specification into feedforward and feedback components relative to network synthesis and the method is examined though an example to trace the outline of the analytical synthesis of dynamic systems using bond graph prototypes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.521-530
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• 1989

Broadband ATM(Asynchronous Transfer Mode) networking techniques based on lightwave technology and high speed integrated circuits appear to be the choice of transport technology for broadband ISDN. Among other problems, the issue of video transport over broadband packet(ATM) networks still requries further investigation. In this paper, the problems of transporting video signals over a broaband packet network are investigated together with possible solutions. In particular, clock recovery packet loss compensation and transport technique based on hierarchical video coding scheme are described in detail. This would allow efficient bandwidth sharing and minimum degradation in video quality.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.322-327
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• 2017

The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment's result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers.