| Title |
Classification and recognition of electrical tracking signal by means of LabVIEW |
| Authors |
김대복(Kim, Dae-Bok) ; 김정태(Kim, Jung-Tae) ; 오성권(Oh, Sung-Kwun) |
| Keywords |
Electrical tracking ; Particle Swarm Optimization ; Neural Networks ; Radial Basis Function Neural Networks ; LabVIEW |
| Abstract |
In this paper, We introduce electrical tracking generated from surface activity associated with flow of leakage current on insulator under wet and contaminated conditions and design electrical tracking pattern recognition system by using LabVIEW. We measure the leaking current of contaminated wire by using LabVIEW software and the NI-c-DAQ 9172 and NI-9239 hardware. As pattern recognition algorithm and optimization algorithm for electrical tracking system, neural networks, Radial Basis Function Neural Networks(RBFNNs) and particle swarm optimization are exploited. The designed electrical tracking recognition system consists of two parts such as the hardware part of electrical tracking generator, the NI-c-DAQ 9172 and NI-9239 hardware and the software part of LabVIEW block diagram, LabVIEW front panel and pattern recognition-related application software. The electrical tracking system decides whether electrical tracking generate or not on electrical wire. |