scholarly journals A sparse representation based image steganography using Particle Swarm Optimization and wavelet transform

2018 ◽  
Vol 57 (4) ◽  
pp. 2343-2356 ◽  
Author(s):  
S.I. Nipanikar ◽  
V. Hima Deepthi ◽  
Nikita Kulkarni
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Sparse Representation ◽  
Particle Swarm ◽  
Image Steganography ◽  
Swarm Optimization

New Research on Identification and Classification of Grid Power Quality Disturbances

2014 ◽  
Vol 986-987 ◽  
pp. 1431-1434
Author(s):  
Ning Xia Yang ◽  
Mao Fa Gong ◽  
Xiao Fei Wang ◽  
Hui Ting Ge ◽  
Yu Qing Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Power Quality ◽  
Particle Swarm ◽  
Interference Signal ◽  
Swarm Optimization ◽  
Frequency Information ◽  
Power Quality Disturbances

To improve accuracy and speed of recognising and classifying grid power quality disturbances, this paper presents a new method which combines complex wavelet transform and particle swarm optimization (PSO) neural network to identify and classify the disturbance . This method extract both amplitude-frequency and phase frequency information of the interference signal to make up for the lack of traditional wavelet transform which only extract the amplitude-frequency information. And on this basis, using particle swarm optimization, we seek the optimal solution of neural network weights and thresholds for the identification and classification of power quality. The MATLAB simulation result has verified the accuracy and rapidity of this method compared with the traditional method .

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.

De-noising nonstationary signal based on sparse representation and particle swarm optimization

2018 ◽  
Vol 47 (7) ◽  
pp. 726005 ◽  
Author(s):  
叶 华 Ye Hua ◽  
谭冠政 Tan Guanzheng ◽  
李 广 Li Guang ◽  
刘晓琼 Liu Xiaoqiong ◽  
李 晋 Li Jin ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Sparse Representation ◽  
Particle Swarm ◽  
Nonstationary Signal ◽  
Swarm Optimization
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