Application of particle swarm optimization for microwave imaging of lossy dielectric objects

2005 ◽  
Author(s):  
T. Huang ◽  
A.S. Mohan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Microwave Imaging ◽  
Swarm Optimization ◽  
Lossy Dielectric ◽  
Dielectric Objects

Microwave imaging based on two hybrid particle swarm optimization approaches

2018 ◽  
Vol 11 (3) ◽  
pp. 268-275 ◽  
Author(s):  
Bouzid Mhamdi
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Scattering ◽  
Scattering Problem ◽  
Particle Swarm ◽  
Inverse Scattering Problem ◽  
Microwave Imaging ◽  
Swarm Optimization ◽  
Extremal Optimization ◽  
Hybrid Approaches ◽  
Hybrid Particle Swarm Optimization

AbstractIn this paper, the solution of the inverse scattering problem for determining the shape and location of perfectly conducting scatterers by making use of electromagnetic scattered fields is presented. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization one. Then, two evolutionary algorithms are used to solve the inverse scattering problem. To further clarify, our contribution is to test two well-known algorithms in the literature to the problem of microwave imaging. The hybrid approaches combine the standard particle swarm optimization (PSO) with the ideas of the simulated annealing and extremal optimization algorithms, respectively. Both of them are shown to be more efficient than original PSO technique. Reconstruction results by using the two presented schemes are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

INVERSION OF LOSSY DIELECTRIC PROFILES USING PARTICLE SWARM OPTIMIZATION

2009 ◽  
Vol 9 ◽  
pp. 93-105 ◽  
Author(s):  
Amr Mohammad Emad Eldin ◽  
Essam A. Hashish ◽  
Mohamed I. Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Lossy Dielectric

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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