Image reconstruction for a partially immersed perfectly conducting cylinder using the steady state genetic algorithm

Radio Science ◽  
2004 ◽  
Vol 39 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ching-Lieh Li ◽  
Shao-Hon Chen ◽  
Chih-Ming Yang ◽  
Chien-Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Conducting Cylinder

Frequency Dependence on Image Reconstruction for a Buried Conducting Cylinder

2013 ◽  
Vol 284-287 ◽  
pp. 473-477
Author(s):  
Wei Chien ◽  
Hsien Wei Tseng ◽  
Yung Wen Lee ◽  
Liang Yu Yen ◽  
Chien Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Resonant Frequency ◽  
Frequency Dependence ◽  
Incident Wave ◽  
Optimization Problem ◽  
Quantitative Information ◽  
Frequency Range ◽  
Conducting Cylinder

Frequency dependence on image reconstruction for a buried conducting cylinder is investigated. A conducting cylinder of unknown shape scatters the incident wave in free space and measured the scattered field. By using measured fields, the imaging problem is reformulated into an optimization problem and solved by the steady-state genetic algorithm (SSGA). Numerical results show that the reconstruction is quite well in the resonant frequency range. This work provides both comparative and quantitative information

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

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