Parameter Identification for the Dynamic Load Modeling Based on Denoising Method of the Measurement Data

2011 ◽  
Vol 217-218 ◽  
pp. 907-910
Author(s):  
Li Di Wang ◽  
Jiang Feng Tang ◽  
Jun Sheng Shi
Keyword(s):  
Parameter Identification ◽  
Dynamic Load ◽  
Measurement Data ◽  
Wavelet Denoising ◽  
Load Modeling ◽  
Denoising Method ◽  
Specific Approach ◽  
Identification Process ◽  
Filtering Method ◽  
Mean Filtering

Different denoising methods are used in parameter identification for the dynamic load modeling and the specific approach is proposed. The effects of different denoising methods including mean filtering, medial filtering and wavelet denoising are discussed. Mean filtering method is not helpful to contain the step changes of the measurement voltage, thus is unsuitable for the parameter identification process. Medial filtering method and wavelet denoising methods are suitable for the parameter identification in dynamic load modeling. Furthermore, experiment results based on the measurement data show that the wavelet denoising method is more efficient in some aspects such as the accuracy of identification and SSE.

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

scholarly journals A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 53
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Time Delay ◽  
Empirical Mode Decomposition ◽  
Gastrocnemius Muscle ◽  
Wavelet Denoising ◽  
Filtering Method ◽  
Mode Decomposition ◽  
Low Pass ◽  
Eemd Method ◽  
Muscle Activations ◽  
Stance Duration

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

scholarly journals Optimizing Signal De-Noising Algorithm for Acoustic Emission Leakage of Wavelet

2016 ◽  
Vol 16 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Xin-Hua Wang ◽  
Yu-Lin Jiao ◽  
Yong-Chao Niu ◽  
Jie Yang
Keyword(s):  
High Pressure ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Weight Coefficient ◽  
Wavelet Denoising ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Pressure Pipe ◽  
Optimal Linear ◽  
Mean Square Error Criterion

Abstract Traditional wavelet denoising method cannot eliminate complex high-pressure pipe signals effectively. In the updated wavelet adaptive algorithm, this thesis defines the constraints in order to reconstruct the signals accurately. According to the minimum mean square error criterion, the results predict the weight coefficient and get the optimal linear predictive value. Adopting the improved algorithm under the same condition, this thesis concluded that Db6 increased the complexity of wavelet algorithm by 50% by comparative experiments. It will be more conducive to the realization of hardware and the feasibility of real-time denoising. Dual adaptive wavelet denoising method improved SNR by 50%. This denoising method will play a key role in the detection rate of high-pressure pipe in the online leakage detection system.

scholarly journals Dynamic load modeling of some low voltage devices

2009 ◽  
Vol 22 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Lidija Korunovic ◽  
Dobrivoje Stojanovic
Keyword(s):  
Induction Motor ◽  
Dynamic Load ◽  
Low Voltage ◽  
Supply Voltage ◽  
Incandescent Lamp ◽  
Mercury Lamp ◽  
Load Modeling ◽  
Load Model ◽  
Fluorescent Lamps

This paper presents the results of dynamic load modeling for some frequently used low voltage devices. The modeling of long-term dynamics is performed on the basis of step changes of supply voltage of the heater, incandescent lamp, mercury lamp, fluorescent lamps, refrigerator, TV set and induction motor. Parameters of dynamic exponential load model of these load devices are identified, analyzed and mutually compared.

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