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Author: search.filters.author.Arunsuriyasak, Peerachai

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    Hand Movement Classification Base on EEG Signals using Deep Learning and Dimensional Reduction Technique
    (2019-11-01)
    Boonme, Phattraporn
    ;
    Thongserm, Petchanon
    ;
    Arunsuriyasak, Peerachai
    ;
    Phasukkit, Pattarapong
    This research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm. It's well-known that Electroencephalography (EEG) shows neural oscillation behaviors in electrical voltage form. Also, Brain-Computer Interface (BCI) is direct communication between neural oscillation and computer to control machines without physical movements. So, this paper aims to present the classification method of EEG signals data to develop a BCI in the future. By using deep learning to classification data is classified into raise the right arm, raise the left arm. And decrease EEG signal data by using Principal Component Analysis (PCA). PCA can reduce the data size of EEG signal from 1000x28 to 28x28. Experimental result of classification has accuracy 90.86% and 94.71%
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    Investigation of deep learning optimizer for water pipe leaking detection
    (2019-07-01)
    Arunsuriyasak, Peerachai
    ;
    Boonme, Phattraporn
    ;
    Phasukkit, Pattarapong
    Nowadays, Deep learning plays an important role in complex problems. Thus, one of important algorithm part is an optimizer. This paper aims to improve algorithm using optimizers. Adam optimizer, a powerful and effective optimizer, was used to adjust parameters in Deep Neural Networks model. Which, object datasets consist leaking water pipe, non-leaking water pipe are used to classify 2 object labels. Nevertheless, RMSprop and Adadelta are alternative optimizers that can be used in Deep Neural Network. Other than that, this experiment has been shown Adam gave an accuracy at 98.973% for leaking water pipe and 97.466% for non-leaking water pipe. While, Adadelta gave 76.755% and 70.448%. And RMSprop gave 98.973% and 97.466%.