• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.35-36
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• 2017

단백질의 구조가 무질서한 것을 예측하는 문제는 단백질 시퀀스 구조의 비교 시간을 단축할 수 있으며 단백질 구조 분석 영역을 표시할 수 있기 때문에 중요하게 다루어진다. 이 논문에서는 단백질의 무질서한 구조 예측을 신경회로망을 이용하여 해결하고자 하였으며, 시뮬레이션 결과 일반적인 신경회로망 보다 심층신경회로망이 더 좋은 성능을 보임을 확인하였다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.4
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• pp.402-409
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• 2017

Selection of optimal cutting conditions is important for improving productivity and implementing efficient process control in metal machining. In this study, improvement of cutting conditions in machining using end-mills is studied by using deep-layered neural networks, which comprise an input layer, output layer, and two hidden layers. System networks are designed with inputs as cutting conditions, and they output the cutting force. A pseudo-inverse network is designed that has the adjustable cutting condition as output and cutting force and other cutting conditions as input. The combination of the system network and pseudo-inverse network enables selection or improvement of cutting conditions that results in the expected cutting force.