Energy Threshold Detection in Neutronic Signal by Automatic Classification and Linear Programming

Philippe Durand (1) , Luan Jaupi (2) , Dariush Ghorbanzadeh (3)

1 M2N - Modélisation mathématique et numérique
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
3 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques

It is proposed in this paper the automatic detection thresholds in the neutronic noise from a nuclear reactor chamber. The proposed method combines a pattern recognition algorithm, known as dynamic cluster and a linear programming algorithm. Euclidean distance, often adopted by default, in the dynamic cluster algorithm; is here replaced by the distance L 1 , also called least absolute deviations (LAD).

Keywords: Index Terms-EMC; Dynamic Cluster Algorithm; Linear Programming; Neutronic noise

Domains: Mathematics [math]; Statistics [math.ST]

Philippe Durand, Luan Jaupi, Dariush Ghorbanzadeh. Energy Threshold Detection in Neutronic Signal by Automatic Classification and Linear Programming. World Congress on Engineering 2015, IAENG, Jul 2015, London, United Kingdom. pp.86-89. ⟨hal-02464414⟩