• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.1039-1042
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• 2003

웹 정보의 급격한 양적 팽창은 네트워크 병목 현상과 사용자의 지연시간 증가 및 웹 서버의 과부하 등의 문제를 야기하고 있다. 이를 완화시키기 위한 방법으로 웹 캐슁이 이용되는데, 전통적인 캐슁과는 달리 문서의 종류와 크기가 가변적이며 많은 사용자의 요구를 처리해야하는 특성이 있다. 따라서 본 논문에서는 동적인 웹 환경과 한정된 크기의 웹 캐쉬 공간의 사용 효율을 향상시켜 캐쉬 적중률을 증가시키기 위한 방법으로, 서비스되는 각 파일의 메타정보를 Main Server의 캐쉬에 리스트 형태로 유지하는 CRBM(Client Request Buffer Manager)을 제안한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1456-1462
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• 2016

In this paper, we apply a chord progression learning model to a rule-based generation of a four-part chorus. The proposed system is given a 32-note melody line and completes the four-part chorus based on the rule of harmonics, predicting the chord progression with the CRBM model. The data for the training model was collected from various harmony textbooks, and chord progressions were extracted with key-independent features so as to utilize the given data effectively. It was shown that the output piece obtained with the proposed learning model had a more natural progression than the piece that used only the rule-based approach.

• Journal of IKEEE
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• v.23 no.1
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• pp.127-133
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• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.