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Author: search.filters.author.Phasukkit, PattarapongSubject: search.filters.subject.Component

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    Using doppler radar classify respiration by MFCC
    (2019-07-01)
    Tantasirin, Chayanan
    ;
    Khaemphukhieo, Ratchaphon
    ;
    Phasukkit, Pattarapong
    Respiration rate is an important parameter to indicate whether the respiratory system is normal or abnormal. In general, respiration rate can be calculated and counted the number of inhale and exhale when a person is in resting condition in 1 minute. There are many methods that can evaluate respiratory data from a various biomedical signal such as Electrocardiogram (ECG) Signals, Blood Pressure (BP) and Photo-plethysmography (PPG). But it can not be used in all condition like sleeping all night comfortably. According to all of the previous methods, they are all contact devices. Therefore, this paper studies non-contact respiration measured by using doppler radar to receive the signal data, then use MFCC to extract the feature of the signal and feed the feature to DCNN. In the result, we got 85% of accuracy.
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    Validation of acoustic emission railway track crack analysis using MFCC
    (2019-07-01)
    Vongserewattana, Narate
    ;
    Suwansin, Wara
    ;
    Phasukkit, Pattarapong
    ;
    Phatsornsiri, Punnavich
    Nowadays, railway track crack detection system has many techniques, such as Ultrasonic Techniques [1], Electromagnetic Techniques [2] and GSM Techniques [3]. For the propose of this research, we study characteristics of railway track using acoustic emission, the device which is widely used for application such as crack detection in aerospace space grade steel [4], detection of defect s in rolling element bearing [5], fatigue crack growth detection [6]. And use the MFCC ('Mel - Frequency Cepstral Coefficients') [7] method which is generally used for low frequency to extract the feature of each railway track. According to that, we chose to adjust the MFCC method for an acoustic feature extraction in order for condition to be suitable used for this system, because the railway track crack detection system, we are interested in concentrates at 100-400 kHz. According to the result of the experiment, it shows that this technique is able to extract and classify feature of the railway track crack.