scholarly journals An identification method of melting layer using the covariance wavelet transform based on GPM‐DPR observations

2021 ◽  
Author(s):  
Junqi Qiao ◽  
Weihua Ai ◽  
Xiong Hu ◽  
Maohong Liu ◽  
Shensen Hu
Keyword(s):  
Wavelet Transform ◽  
Melting Layer ◽  
Identification Method

scholarly journals A Parameter Identification Method for Stewart Manipulator Based on Wavelet Transform

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 257
Author(s):  
Chenyang Zhang
Keyword(s):  
Wavelet Transform ◽  
Parameter Identification ◽  
Coulomb Friction ◽  
Velocity Analysis ◽  
Dynamical Parameter ◽  
New Approach ◽  
Identification Methods ◽  
Identification Method ◽  
True Value ◽  
Joint Velocity

Aiming at inertial and viscous parameter identification for the Stewart manipulator regardless of the influence of Coulomb friction, a simple and effective dynamical parameter identification method based on wavelet transform and joint velocity analysis is proposed in this paper. Compared with previously known identification methods, the advantages of the new approach are that (1) the excitation trajectory is easy to design, and (2) it can not only identify the inertial matrix, but also the viscous matrix accurately regardless of the influence of Coulomb friction. Comparison is made among the identification method proposed in this paper, another identification method proposed previously, and the true value calculated with a formula. The errors from results of different identification methods demonstrate that the method proposed in this paper shows great adaptability and accuracy.

scholarly journals A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hakan Gökdağ
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Particle Swarm ◽  
Dynamic Responses ◽  
Crack Identification ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Coupled Equations ◽  
Identification Method ◽  
Bridge Type

In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.

A Misfire Identification Method using Wavelet Transform and its Application

2011 ◽  
Vol 5 (8) ◽  
pp. 389-397
Author(s):  
Li Qiyue ◽  
Dong Longjun ◽  
Qian Shouyi ◽  
Xu Min ◽  
Liu Gao
Keyword(s):  
Wavelet Transform ◽  
Identification Method

An Identification Method for Cracked Eggs Based on Image Wavelet Transform and Multi-Features Synthesis

2014 ◽  
Vol 704 ◽  
pp. 412-418
Author(s):  
Li Rong Xiong ◽  
Zhi Hui Zhu
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
High Frequency ◽  
Vision System ◽  
Joint Probability ◽  
Principal Component ◽  
Ccd Camera ◽  
Classification Model ◽  
Support Vector ◽  
Identification Method

An identification method for cracked eggs by means of the digital image technology was proposed in this paper. Firstly, an ideal machine vision system was built and the images of good eggs and cracked eggs were obtained by CCD camera. Secondly, each image was decomposed on two layers of wavelet, so 6 high-frequency sub-images and 2 low-frequency sub-images were extracted. Then joint probability matrix after wavelet transform had been calculated and 5 parameters for each high-frequency sub-images were extracted, so the total of the joint probability matrix parameters was 30 for 6 high-frequency sub-images. At the same time, 10 wavelet energy parameters were obtained. Thirdly, four main factor component scores were selected from above 40 feature parameters after principal component analysis, which were input to support vector machine. Finally, classification model was built by support vector machine. Experiments show that the proposed method was effective to identify the cracked eggs from good eggs and the identification rate was 93.75%.

A New Method of Dimensional Synthesis of Function Generator for Planar 4-BAR Linkage Based on Wavelet Characteristic Parameters

1998 ◽  
Author(s):  
Wu Xin ◽  
Chu Jinkui ◽  
Cao Weiqing
Keyword(s):  
Wavelet Transform ◽  
New Method ◽  
Output Function ◽  
Dimensional Synthesis ◽  
Characteristic Parameters ◽  
Orthogonal Wavelet ◽  
Fuzzy Identification ◽  
Function Generator ◽  
Identification Method

Abstract The wavelet description of output function of planar 4-bar linkage is presented in the light of wavelet transform theory. By the aid of Daubechies orthogonal wavelet to transform and analyze the linkage’s output function, a new conception of Wavelet Characteristic Parameters (in short WCP) about output function is originated. Based on this conception, a resolution to function synthesis of planar linkage is provided according to WCP with fuzzy identification method employed. It helps to synthesize function generator that will approximately realize “infinite points” of the output function specially for output requirement with given range. Two examples will illustrate the efficiency and advantages of this method.

Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification

2012 ◽  
Vol 605-607 ◽  
pp. 2265-2269
Author(s):  
Rui Kun Gong ◽  
Ya Nan Zhang ◽  
Chong Hao Wang ◽  
Li Jing Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Bp Neural Network ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Training Set ◽  
Identification Method ◽  
Compound Model ◽  
Image Definition

First, the background, significance and general implementation of the image definition identification are introduced. Then, basic theory of wavelet transform and neural network is expounded. An identification method of image definition based on the composite model of wavelet analysis and neural network is suggested．The two—dimensional discrete wavelet transformation is used to filter image signal and extract its brim character which is input into BP neural network for identification. 4 layers of BP neural network are constructed to perform image definition identification. The compound model is first trained by 90 images from the training set, and then is tested by 87 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

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