A compact circularly polarized high gain S-band nanosatellite antenna using ramped convergence particle swarm optimization

2015 ◽  
Vol 57 (6) ◽  
pp. 1503-1508 ◽  
Author(s):  
M. Samsuzzaman ◽  
Mohammad Tariqul Islam ◽  
Salehin Kibria ◽  
Mengu Cho
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Gain ◽  
Swarm Optimization ◽  
Circularly Polarized

scholarly journals Topological Design of Planar Circularly Polarized Directional Antenna with Low Profile Using Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaonan Zhao ◽  
Junping Geng ◽  
Ronghong Jin ◽  
Yao Jin ◽  
Xiang Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Impedance Matching ◽  
Particle Swarm ◽  
High Gain ◽  
Directional Pattern ◽  
Directional Antenna ◽  
Antenna Gain ◽  
Swarm Optimization ◽  
Circularly Polarized ◽  
Low Profile

A topological method for the design and optimization of planar circularly polarized (CP) directional antenna with low profile was presented. By inserting two parasitic layers, generated by particle swarm optimization, between the equiangular spiral antenna and the ground, a low-profile wideband CP antenna with directional radiation pattern and high gain is achieved. The optimized antenna shows an impedance matching band (S11<-10 dB) of 4–12 GHz with a whole-band stable directional pattern in 4–11.5 GHz, and the antenna gain peak is 8 dBi, which work well in the available band. Measured return loss, antenna gain, and far field patterns agree well with simulation results.

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

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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