Time-Frequency Distribution Analysis of the Stress Relaxation of Sandstones Affected by Dynamic Disturbance

2018 ◽  
Vol 43 (4) ◽  
pp. 415-428 ◽  
Author(s):  
S. Li ◽  
W.C. Zhu ◽  
T. Xu ◽  
R.X. He
Keyword(s):  
Stress Relaxation ◽  
Frequency Distribution ◽  
Distribution Analysis ◽  
Dynamic Disturbance ◽  
Time Frequency ◽  
Time Frequency Distribution

An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
M.F. Habban ◽  
M. Manap ◽  
A.R. Abdullah ◽  
M.H. Jopri ◽  
T. Sutikno
Keyword(s):  
Fault Detection ◽  
Frequency Distribution ◽  
Linear Time ◽  
Harmonic Distortion ◽  
Voltage Source ◽  
Distribution Analysis ◽  
Voltage Source Inverter ◽  
Time Frequency ◽  
S Transform ◽  
Time Frequency Distribution

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT  and S-transform method are analyzed the switch fault of VSI.  To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.

scholarly journals Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

2019 ◽  
Vol 9 (16) ◽  
pp. 3433 ◽  
Author(s):  
Rami Alazrai ◽  
Saifaldeen AL-Rawi ◽  
Hisham Alwanni ◽  
Mohammad I. Daoud
Keyword(s):  
Frequency Distribution ◽  
Support Vector ◽  
Distribution Analysis ◽  
Eeg Signals ◽  
Cold Pain ◽  
Time Frequency ◽  
Evaluation Procedures ◽  
Pain Detection ◽  
Frequency Features ◽  
Time Frequency Distribution

Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.

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