Intelligent diagnosis system for transmission line: Fuzzy-Bayesian classifier approach

Hwa Chang Sung, Jin Bae Park, Young Hoon Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We develop an intelligent diagnosis system which is based on fuzzy-classifier. The term of intelligent diagnosis system (IDS) is a real-time fault monitoring system for transmission line. Based on the Time-Frequency Domain Reflectometry (TFDR) algorithm, we implement the wire detecting system which shows the condition of the wires. The concrete processes are represented as follows: 1) the reflected signals which are sent from the fault of wires are obtained and saved in main server; 2) IDS classifies the fault type of the wires into damage and normal. For classifying the fault type efficiently, we use the fuzzy-Bayesian classifier which is merged the IF-THEN rules with Bayesian algorithms. Simulation results convincingly validate the effectiveness of our algorithms.

Original languageEnglish
Title of host publicationProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09
Pages136-139
Number of pages4
Publication statusPublished - 2009
Event14th International Symposium on Artificial Life and Robotics, AROB 14th'09 - Oita, Japan
Duration: 2008 Feb 52009 Feb 7

Publication series

NameProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09

Other

Other14th International Symposium on Artificial Life and Robotics, AROB 14th'09
Country/TerritoryJapan
CityOita
Period08/2/509/2/7

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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