scholarly journals An Advanced Nonlinear Signal Analysis Method For Damage Detection in Geomaterials

1997 ◽  
Vol 150 (3-4) ◽  
pp. 647-659 ◽  
Author(s):  
B. Bazargan-Sabet ◽  
H. Liu ◽  
S. Chanchole
Keyword(s):  
Damage Detection ◽  
Signal Analysis ◽  
Analysis Method ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis ◽  
Signal Analysis Method

scholarly journals Protein Aggregation/Folding: The Role of Deterministic Singularities of Sequence Hydrophobicity as Determined by Nonlinear Signal Analysis of Acylphosphatase and Aβ(1–40)

2003 ◽  
Vol 85 (6) ◽  
pp. 3544-3557 ◽  
Author(s):  
Joseph P. Zbilut ◽  
Alfredo Colosimo ◽  
Filippo Conti ◽  
Mauro Colafranceschi ◽  
Cesare Manetti ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Signal Analysis ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

Research on Applied-Information Technology in Digital Speech Based on LMD Algorithm

2014 ◽  
Vol 1014 ◽  
pp. 447-451
Author(s):  
Dong Kang He ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Production Function ◽  
Signal Analysis ◽  
Training Model ◽  
Recognition System ◽  
Analysis Method ◽  
End Effects ◽  
Local Mean ◽  
Non Stationary Signal ◽  
Signal Analysis Method

As a new nonlinear and non-stationary signal analysis method,local mean decomposition (LMD) has a good adaptability. We decompose the original non-stationary acceleration vibration signals into several stationary production function (PF).But performing LMD will produce end effects which make results distorted. A hidden Markov model (HMM)-based speech recognition system for Chinese spell.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.Experiments in speech recognition to the production function as the training model, the more traditional identification method to identify higher rates. LMD is an effective method. It is feasible to extract the feature from speech signals with LMD.

Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis

2018 ◽  
Vol 155 ◽  
pp. 11-17 ◽  
Author(s):  
Maie Bachmann ◽  
Laura Päeske ◽  
Kaia Kalev ◽  
Katrin Aarma ◽  
Andres Lehtmets ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Single Channel ◽  
Linear And Nonlinear ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

Loudspeaker Nonlinear Distortion Signal Instantaneous Frequency Measurement and Analysis

2012 ◽  
Vol 203 ◽  
pp. 83-87
Author(s):  
Yu Tian Wang ◽  
Hui Wang ◽  
Qin Zhang
Keyword(s):  
Instantaneous Frequency ◽  
Nonlinear Distortion ◽  
Frequency Measurement ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Strongly Nonlinear ◽  
Measurement And Analysis ◽  
Strongly Nonlinear System ◽  
Nonlinear Signal ◽  
Signal Analysis Method

Loudspeaker is a strongly nonlinear system which is associated with several situations such as electronic, magnetic, mechanical and acoustic. Recently, most of the methods used to measure and analysis loudspeaker are based on the FFT. Unfortunately, traditional Fourier transform based signal analysis method usually causes meaningless results when it is used to analysis non-stationary and nonlinear signal. In this paper, we use Hilbert-Huang transform (HHT) to review the instantaneous frequency of loudspeaker output. Experiments demonstrate that the distortion of loudspeaker can be recognized as intrawave frequency modulation caused by wave profile deformation. Then a novel nonlinear distortion measurement method is proposed which can reveal more accurate and physical meaningful characteristic of loudspeaker.

scholarly journals Bolt Detection Signal Analysis Method Based on ICEEMD

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhui Guo ◽  
Zhan Zhang ◽  
Xin Xie ◽  
Zhengyu Yang
Keyword(s):  
Signal Analysis ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Analysis Method ◽  
Détection Signal ◽  
Mode Function ◽  
Mode Decomposition ◽  
Signal Analysis Method ◽  
Detection Signal

The construction quality of the bolt is directly related to the safety of the project, and, as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold denoising technique. Based on the approximate entropy and the wavelet denoising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions and also that the IMF can effectively identify the reflection signal of the end of the bolt.

Sign in / Sign up

Export Citation Format

Share Document