A Digital Watermarking Algorithm Based on Wavelet Packet Transform and BP Neural Network

2011 ◽  
Author(s):  
Qiao Baoming ◽  
Zhang Pulin ◽  
Kang Qiao
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform

Research on Flaw Detection for 3-D Braided Composite Material Using Ultrasonic Testing Based on Wavelet Packet Transform

2010 ◽  
Vol 146-147 ◽  
pp. 394-399 ◽  
Author(s):  
Xiao Li He ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Ultrasonic Testing ◽  
Wavelet Packet ◽  
Flaw Detection ◽  
Wavelet Packet Transform ◽  
Braided Composite ◽  
Micro Cracks

A method used to recognize the inner defects of 3-D braided composite materials is discussed. Firstly, the link between UT signals and the defects of 3-D braided composite material is analyzed. Then, the wavelet packet transform is used to process the ultrasonic scanning pulse signals of the defects. The characteristic quantities of signal are extracted into the BP neural network as samples. Through training the BP neural network, the recognize system of micro-cracks and pores is achieved. Finally, according to the results of experiment this classification system based on wavelet packet transform is proved to be feasible.

Envelope Signal of Partial Discharge Pattern Recognition Based on Wavelet Packet Transform

2013 ◽  
Vol 823 ◽  
pp. 536-540
Author(s):  
Dong Song Luo ◽  
Kun Peng Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Singular Value Decomposition ◽  
Bp Neural Network ◽  
High Frequency ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Envelope Signal ◽  
Value Decomposition

In order to achieve the GIS fault detection and defect type recognition, four typical defect models were designed and discharge tests are carried out aiming at insulation defect as well as discharge characteristics in the GIS .With a large number of ultra high frequency envelope signal ,a method of domain feature extraction was proposed based on wavelet packet transform with singular value decomposition .The envelope signal was decomposed through wavelet packet transform first in the method, then the coefficient matrix of wavelet packet transform was built in the scale ,after that feature vectors of matrix were extracted by means of singular value decomposition. On this basis, BP neural network was took advantage of for pattern recognition .The results show that the good recognition effect was obtained with that method . Keyword: Ultra high frequency; Envelope signal; Wavelet packet transform; Singular value decomposition; BP neural network

scholarly journals State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
QingHui Song ◽  
QingJun Song ◽  
Linjing Xiao ◽  
HaiYan Jiang ◽  
LiNa Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Nondestructive Technique ◽  
Operation Conditions ◽  
Mode Decomposition ◽  
Before And After

Vibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algorithm (MEA), and the backpropagation (BP) neural network is proposed for elevator control transformer fault diagnosis. Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. Then, the denoised signal is transformed by empirical wavelet packet transform to extract the energy ratio and energy entropy features in the wavelet packet coefficients. Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. The experimental results show that the proposed model has a remarkable performance with an average root mean square error of 0.00672 and the average diagnosis accuracy of 90.8%, which is better than classic BP neural network.

scholarly journals An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2701 ◽  
Author(s):  
Masoud Ahmadipour ◽  
Hashim Hizam ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Neural Network ◽  
Shannon Entropy ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection Technique ◽  
Islanding Detection ◽  
Energy Entropy ◽  
Cost Parameters ◽  
Logarithmic Energy

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

Power Quality Disturbance Classification Using Wavelet Packet Transform and Back Propagation Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 7099-7109
Author(s):  
M. K Elango ◽  
A Jagadeesan ◽  
K. Mohana Sundaram
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Virtual Instrument ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Power Quality Disturbances

This paper develops a real time solution for detecting the Power Quality events. Fourteen events are generated through experimental setup and the signals are acquired through a voltage Data Acquisition Card, NI DAQ-9225, controlled by a Virtual Instrument software package. The features extracted from the Wavelet Transformation are fed into the Back Propagation Neural Network for training. By the virtue of a Neural Network property, it gets self-adapted and self-learned aiding in automatic classification of Power Quality Events. A combination of Wavelet Transform technique and Neural Networks are employed to detect and characterize the Power Quality Disturbances. The result obtained shows the effectiveness of the Wavelet Packet Transform based Back Propagation algorithm in classifying the Power Quality Disturbances. The results produced by the proposed methodology based Back Propagation Algorithm is verified with the Power Quality Analyser.

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