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- W2350874504 abstract "In the research of pattern recognition on partial discharge(PD) in GIS,the traditional decision tree method faces problems of complex structure,low recognition rate and vulnerability to noise data due to the single features and limited training pattern modes.In this paper,a method of using AdaBoost decision tree integrating with composited features is presented.Features are extracted from three aspects including statistical distribution of p-q-n diagram,moment distribution of q-t diagram and Weibull distribution parameters of q-n diagram and samples are collected from the typical discharges from high voltage needle,floating electrode,void,free particle in GIS and interferences from mobile phone and light.The influence of single features and composited features on the recognition effects of C4.5 decision tree and AdaBoost decision tree is studied.Recognition results of laboratory test and field test show that AdaBoost decision tree made with features composited with three aspects can effectively optimize the recognition rate and improve the efficiency of its time and space use." @default.
- W2350874504 created "2016-06-24" @default.
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- W2350874504 date "2011-01-01" @default.
- W2350874504 modified "2023-09-25" @default.
- W2350874504 title "Pattern recognition based on AdaBoost decision tree for partial discharge" @default.
- W2350874504 hasPublicationYear "2011" @default.
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