A novel method to improve dose assessment due to severe NPP accidents based on field measurements and particle swarm optimization

2017 ◽  
Vol 110 ◽  
pp. 148-159 ◽  
Author(s):  
Cláudio M.N.A. Pereira ◽  
Andre Przewodowski Filho ◽  
Roberto Schirru
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Field Measurements ◽  
Dose Assessment ◽  
Swarm Optimization ◽  
Novel Method

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

2012 ◽  
Vol 239-240 ◽  
pp. 1027-1032 ◽  
Author(s):  
Qing Guo Wei ◽  
Yan Mei Wang ◽  
Zong Wu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Channel Selection ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Channel Reduction ◽  
Novel Method

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

Particle Swarm Optimization and Adaptive Wavelet Theory for Vibration Signal Application in Fault Diagnosis of Gearbox System

2013 ◽  
Vol 791-793 ◽  
pp. 958-961
Author(s):  
Han Xin Chen ◽  
Yan Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Pso Algorithm ◽  
Economic Losses ◽  
Early Age ◽  
Wavelet Theory ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Novel Method

Gearbox system is widely used in mechanical industry,but serious failure is always occurred in the gearbox system. So it is very necessary to diagnose the fault of gearbox in the early-age avoiding economic losses. In this paper, a novel method for extracting the characteristic information from the vibration signal of gearbox system based on the particle swarm optimization (PSO) algorithm and adaptive wavelet theory is proposed.

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