2005Unpublished venueRequires access

Recognizing different aging stages of oil-paper based on Fisher discriminant

Lijun Yang, Ruijin Liao, Jian Li, Youyuan Wang, Shuaiwei Liang

Open publisher page 1 citations

Abstract

In this paper, the discriminant analysis method is introduced to identify the aging stages of oil-paper. An identification method based on Fisher discriminant function and multiple PD characteristic parameters is proposed. The 30 vectors distracted from 6 samples at 5 different aging stages are used as reference samples to obtain Fisher discriminant functions. The characteristic vector with eleven-dimension is mapped to two-dimension and the other 30 vectors are analyzed as samples need to be identified using discriminant functions. It is concluded that a reasonable result can be achieved using the Fisher discriminant functions.

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What this paper is about

In this paper, the discriminant analysis method is introduced to identify the aging stages of oil-paper. An identification method based on Fisher discriminant function and multiple PD characteristic parameters is proposed. The 30 vectors distracted from 6 samples at 5 different aging stages are used as reference samples to obtain Fisher discriminant functions. The characteristic vector with eleven-dimension is mapped to two-dimension and the other 30 vectors are analyzed as samples need to be identified using discriminant functions. It is concluded that a reasonable result can be achieved using the Fisher discriminant functions.

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Available abstract

In this paper, the discriminant analysis method is introduced to identify the aging stages of oil-paper. An identification method based on Fisher discriminant function and multiple PD characteristic parameters is proposed. The 30 vectors distracted from 6 samples at 5 different aging stages are used as reference samples to obtain Fisher discriminant functions. The characteristic vector with eleven-dimension is mapped to two-dimension and the other 30 vectors are analyzed as samples need to be identified using discriminant functions. It is concluded that a reasonable result can be achieved using the Fisher discriminant functions.

Key concepts: Kernel Fisher discriminant analysis, Linear discriminant analysis, Discriminant, Optimal discriminant analysis, Discriminant function analysis, Fisher kernel, Pattern recognition (psychology), Dimension (graph theory)

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