Open AccessMulti Channels Data Fusion Algorithm on Quantum Genetic Algorithm for Sealed Relays
Author(s) -
Xin Qian,
Shujuan Wang,
Chao Li,
Guotao Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/2/022130
Subject(s) - algorithm , sensor fusion , computer science , relay , variance (accounting) , channel (broadcasting) , signal (programming language) , genetic algorithm , fusion , noise (video) , measure (data warehouse) , data mining , artificial intelligence , telecommunications , machine learning , physics , power (physics) , linguistics , philosophy , accounting , quantum mechanics , business , image (mathematics) , programming language
Particle Impact Noise Detection (PIND) is a screening test that must be done before the sealed relay is released. PIND signals are mostly occasional weak, which are difficult to measure and identify. In this paper, the three channel sensor is used to detect the remainder signals, and the weak signal is enhanced by weighted data fusion. For the first time, quantum genetic algorithm (QGA) is used to self-adaptively configure the weight, taking the place of arti-ficial settings. Experiments show that after data fusion, and the variance is only about 20% of the single input signal, which verifies the effectiveness of the algorithm. The computational complexity of QGA is offered.