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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Àü±âÇÐȸ³í¹®Áö (The Transactions of The Korean Institute of Electrical Engineers)

Àü±âÇÐȸ³í¹®Áö (The Transactions of The Korean Institute of Electrical Engineers)

Current Result Document : 18 / 27 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) LabVIEW¿¡ ÀÇÇÑ Tracking ½ÅÈ£ ºÐ·ù ¹× ÀνÄ
¿µ¹®Á¦¸ñ(English Title) Classification and recognition of electrical tracking signal by means of LabVIEW
ÀúÀÚ(Author) ±è´ëº¹   ±èÁ¤Å   ¿À¼º±Ç   Dae-Bok Kim   Jung-Tae Kim   Sung-Kwun Oh  
¿ø¹®¼ö·Ïó(Citation) VOL 59 NO. 04 PP. 0779 ~ 0787 (2010. 04)
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(Korean Abstract)
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(English 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.
Ű¿öµå(Keyword) Electrical tracking   Particle Swarm Optimization   Neural Networks   Radial Basis Function Neural Networks   LabVIEW  
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