Waveform Analysis for Human Handling Force Data Using Wavelet Transform

Author(s):  
Atsushi Sugama ◽  
Akihiko Seo
Author(s):  
Sameh El-Sharo ◽  
Amani Al-Ghraibah ◽  
Jamal Al-Nabulsi ◽  
Mustafa Muhammad Matalgah

<p>The use of pulse wave analysis may assist cardiologists in diagnosing patients with vascular diseases. However, it is not common in clinical practice to interpret and analyze pulse wave data and utilize them to detect the abnormalities of the signal. This paper presents a novel approach to the clinical application of pulse waveform analysis using the wavelet technique by decomposing the normal and pathology signal into many levels. The discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave (CAPW) signal, and the continuous wavelet transform (CWT) creates images of the decomposed signal. The wavelet analysis technique in this work aims to strengthen the medical benefits of the pulse wave. The obtained results show a clear difference between the signal and the images of the arterial pathologies in comparison with normal ones. The certain distinct that were achieved are promising but further improvement may be required in the future.</p>


Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 456
Author(s):  
Jonggeun Kim ◽  
Hansoo Lee ◽  
Jeong Woo Jeon ◽  
Jong Moon Kim ◽  
Hyeon Uk Lee ◽  
...  

Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.


2008 ◽  
Vol 47 (2) ◽  
pp. 165-173 ◽  
Author(s):  
Mirko De Melis ◽  
Umberto Morbiducci ◽  
Ernst R. Rietzschel ◽  
Marc De Buyzere ◽  
Ahmad Qasem ◽  
...  

2005 ◽  
Vol 117 (4) ◽  
pp. 2122-2133 ◽  
Author(s):  
Jörg Enders ◽  
Weihua Geng ◽  
Peijun Li ◽  
Michael W. Frazier ◽  
David J. Scholl

2014 ◽  
Vol 668-669 ◽  
pp. 1166-1169
Author(s):  
Zhen Guang Liang ◽  
Xue Gu

With good repeatability and simple structure, transmission line pulse (TLP) has been used in immunity test of integrated circuit and printed circuit board. A TLP generator is first manufactured and its output waveform is presented. By using wavelet transform, the waveform is denoised and discriminated to components inherent to system function and parasitic parameters. Frequency spectrum changed with time is also obtained by continuous wavelet transform of complex morlet. Decomposed damping oscillation component and high frequency component in instant frequency spectrum show influence of inductance in circuit on the waveform. Improvement of rising time and overshoot is achieved by change of probe connection with shorter grounding line.


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