Genetic Algorithms for Incore Fuel Management and Other Recent Developments in Optimisation

2005 ◽  
pp. 113-154 ◽  
Author(s):  
Jonathan N. Carter
Keyword(s):  
Genetic Algorithms ◽  
Fuel Management ◽  
Recent Developments

Nuclear Fuel Management Optimization Using Genetic Algorithms

1995 ◽  
Vol 111 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Michael D. DeChaine ◽  
Madeline Anne Feltus
Keyword(s):  
Genetic Algorithms ◽  
Nuclear Fuel ◽  
Fuel Management ◽  
Management Optimization

Multiobjective fuel management optimization for self-fuel-providing LMFBR using genetic algorithms

1999 ◽  
Vol 26 (9) ◽  
pp. 783-802 ◽  
Author(s):  
Vladimir G. Toshinsky ◽  
Hiroshi Sekimoto ◽  
Georgy I. Toshinsky
Keyword(s):  
Genetic Algorithms ◽  
Fuel Management ◽  
Management Optimization

scholarly journals POINT CLOUD GENERATION FROM GAUSSIAN DECOMPOSITION OF THE WAVEFORM LASER SIGNAL WITH GENETIC ALGORITHMS

2018 ◽  
Vol 24 (2) ◽  
pp. 270-287
Author(s):  
Andrey Augusto Alves de Oliveira ◽  
Jorge Antonio Silva Centeno ◽  
Fabiano Scheer Hainosz
Keyword(s):  
Genetic Algorithms ◽  
Point Cloud ◽  
Detection Method ◽  
Local Maximum ◽  
Maximum Point ◽  
Decomposition Approach ◽  
Laser Signal ◽  
Recent Developments ◽  
Local Maximum Point ◽  
Gaussian Decomposition

Abstract: Recent developments in LIDAR technology lead to the availability of the waveform systems, which capture and digitize the whole return of the emitted LASER pulse. As many objects may cause multiple returns in the same echo, one task is to detect and separate different echoes within the same digitized measurement. In this paper the results of a study aimed at LASER signal waveform decomposition using genetic algorithms are introduced. The proposed method is based on the Gaussian decomposition approach and analyzes each digitized return to compute one or more points. Initially, the number of peaks contained in the waveform is determined by a simple peak detection method, with a local maximum point algorithm. When more than one peak is detected, genetic algorithms are applied to estimate the amplitude, time and standard deviation of each peak within the digitized signal. With this methodology it was possible to increase the number of points by approximately 17 % compared to the point cloud obtained using commercial software. The best results were obtained in areas with high vegetation, and thus the methodology can be applied to the generation of denser points cloud in forest areas.

Fuel Management Optimization Using Genetic Algorithms and Expert Knowledge

1996 ◽  
Vol 124 (1) ◽  
pp. 188-196 ◽  
Author(s):  
Michael D. DeChaine ◽  
Madeline A. Feltus
Keyword(s):  
Genetic Algorithms ◽  
Expert Knowledge ◽  
Fuel Management ◽  
Management Optimization
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