scholarly journals DETERMINATION OF EPILEPTIC DISORDER WITH DISCRETE WAVELET TRANSFORMS AND NEURAL NETWORK CLASSIFIER

2014 ◽  
Vol 10 (1) ◽  
pp. 66-72 ◽  
Author(s):  
S. T. Sadish Kumar ◽  
N. Kasthuri
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Neural Network Classifier ◽  
Epileptic Disorder

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

2010 ◽  
Vol 08 (06) ◽  
pp. 905-912 ◽  
Author(s):  
MICHEL ALVES LACERDA ◽  
RODRIGO CAPOBIANCO GUIDO ◽  
LEONARDO MENDES DE SOUZA ◽  
PAULO RICARDO FRANCHI ZULATO ◽  
JUSSARA RIBEIRO ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Finite Impulse Response ◽  
Speaker Verification ◽  
Fir Filters ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Verification Algorithm ◽  
Support Size ◽  
Artificial Neural Network Ann

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

Detection of Wrap-Up Incidents in a Napping Machine

1999 ◽  
Author(s):  
Shahzad A. Dad ◽  
Sagar V. Kamarthi ◽  
Thomas P. Cullinane
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Woven Fabric ◽  
Planetary Motion ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Detection Scheme ◽  
Vibration Signals ◽  
The Neural Network ◽  
The Web

Abstract In the textile industry, a napping machine is used to raise pile on the surface of the web (knit or woven fabric). As a result of the napping machine’s high speed planetary motion, the web can get tangled in the machine and induces damage to both the machine and the web. By averting wrap-up incidents, it is possible to save maintenance costs, reduce machine downtime, and make the work environment safer. This paper introduces a method for predicting wrap-up incidents in a napping machine to avoid costly damage to the machine. The task of detecting wrap-up incidents is achieved by using indirect sensing of vibration signals from the napping machine. The data collected from the napping machine are represented by discrete wavelet transforms. The features extracted from the coefficients of the discrete wavelet transforms are used as inputs to a multilayer neural network. Once the neural network is trained by using the data specific to the napping machine, data from the machine are processed and fed to the neural network for online wrap-up incident prediction. Several experiments are conducted on a test napping machine, to verify and validate the proposed wrap-up detection scheme. It was found that the vibration signals along the horizontal direction of the main shaft of the napping machine provides an impressive 100% correct wrap-up detection signal.

Fault detection in rotor system by discrete wavelet neural network algorithm

2021 ◽  
pp. 107754632110307
Author(s):  
K Babu Rao ◽  
D Mallikarjuna Reddy
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Groove Depth ◽  
Mode Shapes ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Response Curves ◽  
Crack Location ◽  
Stepped Shaft ◽  
Rotational Response

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

Image Weight Models Based on Discrete Wavelet Transforms

2021 ◽  
Author(s):  
Maya M. Lyasheva ◽  
Stella A. Lyasheva ◽  
Mikhail P. Shleymovich
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms
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