scholarly journals The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanyan Zhang ◽  
Gang Wang ◽  
Chaolin Teng ◽  
Zhongjiang Sun ◽  
Jue Wang
Keyword(s):  
Frequency Band ◽  
Classification Accuracy ◽  
Relative Frequency ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Band Energy ◽  
Frequency Band Energy ◽  
Semg Signals

For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.

Feature Extraction and Analysis of Passive Underwater Acoustic Signals for Different Species and Quantities of Freshwater Fish

2018 ◽  
Vol 61 (5) ◽  
pp. 1505-1513
Author(s):  
Qunzi Tu ◽  
Hanying Huang ◽  
Lu Li ◽  
Shanbai Xiong
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Peak Frequency ◽  
Main Peak ◽  
Wavelet Packet Decomposition ◽  
Band Energy ◽  
Frequency Band Energy ◽  
Peak Value

Abstract. The underwater signals from one and six breams, crucians, grass carps, and cyprinoids using a hydrophone were preprocessed by Wiener filtering. Three features were extracted: frequency band energy based on wavelet packet decomposition, average mel cepstral coefficient, and main peak frequency and main peak value based on the power spectrum. The effects of fish species and quantity on these features were analyzed. The results show that fish species had significant effects on the frequency band energy based on wavelet packet decomposition, average mel cepstral coefficient, and main peak frequency and main peak value based on the power spectrum. The fish quantity had significant effects on the frequency band energy based on wavelet packet decomposition and main peak value based on the power spectrum, but had no significant effects on the average mel cepstral coefficient and main peak frequency based on the power spectrum. Keywords: Feature extraction, Freshwater fish, Passive underwater acoustic technology, Significance analysis.

Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet

2012 ◽  
Vol 472-475 ◽  
pp. 795-798
Author(s):  
Min Yong Tong
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Automobile Engine ◽  
Band Energy ◽  
Diagnosis Method ◽  
S Model ◽  
Frequency Band Energy

A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

scholarly journals Researching Coal and Rock Character Recognition Based on Wavelet Packet Frequency Band Energy

2014 ◽  
Vol 6 (1) ◽  
pp. 1793-1797 ◽  
Author(s):  
Guanghui Xue ◽  
Xinying Zhao ◽  
Ermeng Liu ◽  
Weijian Ding ◽  
Baohua Hu
Keyword(s):  
Frequency Band ◽  
Character Recognition ◽  
Wavelet Packet ◽  
Band Energy ◽  
Frequency Band Energy

Application of Wavelet Packet Energy Spectrum in Micro Motor Fault Diagnosis

2013 ◽  
Vol 726-731 ◽  
pp. 3159-3162
Author(s):  
Sheng Yi Chen ◽  
Gui Tang Wang ◽  
Shou Lei Sun ◽  
Qiang Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Reconstruction Algorithm ◽  
Mechanical Equipment ◽  
Vibration Signals ◽  
Band Energy ◽  
Status Information ◽  
Frequency Band Energy

To diagnosis vibration signals of micro motor in several different fault types a method based on wavelet packet energy spectrum is presented, the energy on each Sub-frequency band, which are Calculated by Wavelet packet decomposition and reconstruction algorithm, are used to normalization process.Under both circumstances of normal working and unmoral working of mechanical equipment,there exist evident differences among the Sub-frequency band energy after the decomposition of wavelet packet, which energy contains a wealth of micro motor running status information and the eigenvectors is structured by the Sub-frequency band energy spectrum can establish energy and Fault mapping relationship.The preliminary experimental results show that it is effective to use the wavelet packet-energy spectrum in micro motor fault diagnosis .

scholarly journals Lubrication State Recognition Based on Energy Characteristics of Friction Vibration with EEMD and SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hai-jie Yu ◽  
Hai-jun Wei ◽  
Jing-ming Li ◽  
Da‐ping Zhou ◽  
Li‐dui Wei ◽  
...  
Keyword(s):  
Frequency Band ◽  
Ensemble Empirical Mode Decomposition ◽  
Experimental Results ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Energy Characteristics ◽  
State Recognition ◽  
Band Energy ◽  
Frequency Band Energy

In order to identify different lubrication states, lubrication experiments were carried out on a Bruker UMT-3 tester. The experimental results show that the frequency band energy characteristics of friction vibration signals are different under different lubrication states. Based on this, a lubrication state recognition method with ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) was proposed. The vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMFs) with the EEMD method. The first six IMF components containing the main friction information were retained to calculate the energy ratio and construct the feature vector. The experimental results show that the mixed lubrication state can be identified by hundred percent, and there is a slight confusion between boundary lubrication and dry friction. The results show that frequency band energy of friction vibration signals is an effective feature to identify different lubrication states, and the proposed method can be used to identify different lubrication states.

Automatic classification of normal and sick patients with crackles using wavelet packet decomposition and support vector machine

2021 ◽  
Vol 67 ◽  
pp. 102521
Author(s):  
Paweł Stasiakiewicz ◽  
Andrzej P. Dobrowolski ◽  
Tomasz Targowski ◽  
Natalia Gałązka-Świderek ◽  
Teresa Sadura-Sieklucka ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Automatic Classification ◽  
Support Vector ◽  
Wavelet Packet Decomposition
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