<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The analysis and structural transformation of power-related picture elements is an essential result of regional power grid research. This paper proposes a new idea for extracting monolithic insulator images based on analysing the characteristics of scanned colour grid power insulators. At the same time, the article extracts the RGB colour matrix of the insulator based on the BP neural network algorithm. Then, it uses it as a characteristic parameter for training and analysis. Combining the characteristics of image data, it is found that the model proposed in this paper enhances the ability to express images, thereby improving the accuracy of image classification. Furthermore, many experiments on the accurate data set of insulator monoliths show the effectiveness of this model.</p>
</abstract>

The analysis and structural transformation of power-related picture elements is an essential result of regional power grid research. This paper proposes a new idea for extracting monolithic insulator images based on analysing the characteristics of scanned colour grid power insulators. At the same time, the article extracts the RGB colour matrix of the insulator based on the BP neural network algorithm. Then, it uses it as a characteristic parameter for training and analysis. Combining the characteristics of image data, it is found that the model proposed in this paper enhances the ability to express images, thereby improving the accuracy of image classification. Furthermore, many experiments on the accurate data set of insulator monoliths show the effectiveness of this model.

Mathematical model of transforming image elements to structured data based on BP neural network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The analysis and structural transformation of power-related picture elements is an essential result of regional power grid research. This paper proposes a new...

Feature amount	Training time/s^-1	Forecast accuracy rate/%	Overview
Colour histogram	590.160333	87.58	Short training time, average prediction accuracy
Colour moment	10.764295	87.82	Longest training time, average prediction accuracy
Centreline colour matrix	46.679	750, 93.37	Short training time and high prediction accuracy

Number of input neurons	Colour moment accuracy rate/%	Colour histogram accuracy rate/%	Centreline colour matrix accuracy rate/%
5	86.87	83.88	93.37
10	87.82	87.58	93.37
15	87.70	85.37	93.13
20	87.76	82.69	93.13
30	87.88	85.67	92.84

Mathematical model of transforming image elements to structured data based on BP neural network

Data publikacji: 15 gru 2021

Zakres stron: 257 - 266

Otrzymano: 16 cze 2021

Przyjęty: 24 wrz 2021

DOI: https://doi.org/10.2478/amns.2021.1.00084

Słowa kluczowe
BP neural network, image elements, feature extraction, insulator monolithic, structured, data conversion

© 2021 Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Fig. 10

Comprehensive comparison of three diagnostic methods

Comparison of the accuracy of the number of neurons in different hidden layers

Diagnosis results based on the feature amount of the colour histogram

Diagnosis results based on the colour moment feature quantity

Table 4 Diagnosis results based on the colour matrix of the centreline of the insulator

Mathematical model of transforming image elements to structured data based on BP neural network

Data publikacji: 15 gru 2021

Zakres stron: 257 - 266

Otrzymano: 16 cze 2021

Przyjęty: 24 wrz 2021

DOI: https://doi.org/10.2478/amns.2021.1.00084

Słowa kluczoweBP neural network, image elements, feature extraction, insulator monolithic, structured, data conversion

© 2021 Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Comprehensive comparison of three diagnostic methods

Comparison of the accuracy of the number of neurons in different hidden layers

Diagnosis results based on the feature amount of the colour histogram

Diagnosis results based on the colour moment feature quantity

Table 4 Diagnosis results based on the colour matrix of the centreline of the insulator

Słowa kluczowe
BP neural network, image elements, feature extraction, insulator monolithic, structured, data conversion