• Title/Summary/Keyword: Deep Belief Networks

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EPS Gesture Signal Recognition using Deep Learning Model (심층 학습 모델을 이용한 EPS 동작 신호의 인식)

  • Lee, Yu ra;Kim, Soo Hyung;Kim, Young Chul;Na, In Seop
    • Smart Media Journal
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    • v.5 no.3
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    • pp.35-41
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    • 2016
  • In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

Forecasting the Precipitation of the Next Day Using Deep Learning (딥러닝 기법을 이용한 내일강수 예측)

  • Ha, Ji-Hun;Lee, Yong Hee;Kim, Yong-Hyuk
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.2
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    • pp.93-98
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    • 2016
  • For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.