Rough set theory applied to pattern recognition of Partial Discharge in noise affected cable data

IEEE Transactions on Dielectrics and Electrical Insulation

Volumn 24, Issue 1, 2017, Pages 147-156

  Peng, Xiaosheng ^a   Wen, Jinyu ^a   Li, Zhaohui ^a   Yang, Guangyao ^a   Zhou, Chengke ^b   Reid, Alistair ^b,d   Hepburn, Donald M ^b   Judd, Martin D ^c   Siew, W H ^c  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b GLASGOW CALEDONIAN UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^d CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Cables; Knowledge Acquisition; Partial Discharges; Pattern Recognition; Rough Set; Signal Processing

Indexed keywords

BACKPROPAGATION; CABLES; DATA HANDLING; ETHYLENE; FEATURE EXTRACTION; KNOWLEDGE ACQUISITION; NEURAL NETWORKS; PARTIAL DISCHARGES; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; RUBBER TESTING; SIGNAL INTERFERENCE; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

ATTRIBUTE REDUCTION METHOD; BACK-PROPAGATION NEURAL NETWORKS; ETHYLENE PROPYLENE RUBBER CABLES; INTERFERENCE SIGNAL; LOWER AND UPPER APPROXIMATIONS; PARTIAL DISCHARGE SIGNAL; PATTERN RECOGNITION METHOD; RECOGNITION METHODS;

ROUGH SET THEORY;

EID: 85015748274     PISSN: 10709878     EISSN: None     Source Type: Journal    
DOI: 10.1109/TDEI.2016.006060     Document Type: Article

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