A High Frequency Vibration Compensation Approach in Terahertz SAR Based on Wavelet Multi-Resolution Analysis

2018 ◽  
Author(s):  
Huiting Xia ◽  
Qian Chen ◽  
Yinwei Li ◽  
Chaowei Fu ◽  
Haitao Wang
Keyword(s):  
High Frequency ◽  
High Frequency Vibration ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Compensation Approach ◽  
Vibration Compensation

Research and Analysis of ECG Signal Feature Detection Based on Multi-Resolution Analysis and Baselines Wander

2014 ◽  
Vol 926-930 ◽  
pp. 1827-1830
Author(s):  
Hui Yu Yang
Keyword(s):  
Wavelet Transform ◽  
Error Detection ◽  
High Frequency ◽  
Feature Detection ◽  
Frequency Noise ◽  
Ecg Signal ◽  
Area Method ◽  
Average Value ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Electrocardiosignal feature extraction is the base of electrocard iologic automatic diagnosis. By using wavelet transform multi-resolution analysis, the noise in electrocard iosignal is removed; and by using proximity signals of wavelet transform the base linew ander is filtered. The high frequency noise is handled and eliminated with the default threshold; and the average value of the electrocardiosignals is set to zero. In detection of rpeak, because leak detection will occur when only 23 detail signals is considered, thus the 23 and 24 detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time voltage method and amplitude method, the area method offers the highest accuracy.

Stall Analysis of the Compressor Based on Discrete Wavelet

2012 ◽  
Vol 580 ◽  
pp. 74-77
Author(s):  
Jiang Tao Xu ◽  
Rui Xia Guo
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Pressure Pulsation ◽  
Pressure Fluctuations ◽  
Performance Criterion ◽  
Discrete Wavelet ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Engine Compressor

This paper, based on the discrete wavelet thoughts, using the pressure fluctuations signal of the engine compressor, effectively remove high frequency interference, and keep useful information to signal. According to the different position of different states of the signal pressure pulsation multi-resolution analysis, reconstruction of the relevant frequency band pressure component signals, thus judgment that whether compressor enters a stall, the conventional only through the stall of engine after expressed by changes to the method of performance criterion.

scholarly journals Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

2019 ◽  
pp. 1-14
Author(s):  
Jude I. Aneke ◽  
O. A. Ezechukwu ◽  
P. I. Tagboh
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Transmission Lines ◽  
High Frequency ◽  
Fault Location ◽  
Power Transmission ◽  
Low Frequency ◽  
Power Transmission Lines ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

This paper proposes a fault (line-to-line) location on Ikeja West – Benin 330kV electric power transmission lines using wavelet multi-resolution analysis and neural networks pattern recognition abilities. Three-phase line-to-line current and voltage waveforms measured during the occurrence of a fault in the power transmission-line were pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high-frequency details and low-frequency approximations. The patterns formed based on high-frequency signal components were arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low-frequency approximations were arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The new method uses both low and high-frequency information of the fault signal to achieve an exact location of the fault. The neural network was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was carried using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrate that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and locating faults on transmission lines as the average percentage error in fault location was just 0.1386%. This showed that satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.

Tool wear analysis in high-frequency vibration-assisted drilling of additive manufactured Ti6Al4V alloy

Wear ◽  
2021 ◽  
pp. 203814
Author(s):  
Marco Sorgato ◽  
Rachele Bertolini ◽  
Andrea Ghiotti ◽  
Stefania Bruschi
Keyword(s):  
Tool Wear ◽  
High Frequency ◽  
Ti6al4v Alloy ◽  
High Frequency Vibration ◽  
Wear Analysis
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