Principle of electric shock in communication base stations

Tags: Principle Electric Shock

6 FAQs about Principle of electric shock in communication base stations

Do we need a classification model for electric shock current identification?

Abstract: Electric shock current identification is essential for the safety in power distribution network. Moreover, as different categories of object have different electric shock current characteristic, a classification model for shock current is essential to be proposed before identification.

How to improve the accuracy of electric shock current identification?

The two-stage framework is proposed to improve the accuracy of the electric shock current identification by introducing the classification stage and the identification stage. In the classification stage, different styles of electric shock current are classified based on the AdaBoost method.

Can a biological electric shock detection method improve the identification accuracy?

Results show that the proposed method can significantly improve the identification accuracy of electric shock current signal comparing to traditional methods. Biological electric shock are common harmful accidents in the power system.

How AdaBoost is used in electric shock classification?

Therefore, the authors proposed a two-stage framework, including the AdaBoost for the classification and an improved support vector machine (SVM) method for the identification. In the classification stage, the AdaBoost learns the hidden pattern of different electric shock current and generates a predictive model for current classification.

How can a two-stage framework improve the accuracy of electric shock current identification?

effect on the algorithm, and it has better robustness in practical project applications. The two-stage framework is proposed to improve the accuracy of the electric shock current identification by introducing the classification stage and the identification stage.

How are different types of electric shock current classified?

In the classification stage, different styles of electric shock current are classified based on the AdaBoost method. In the identification stage, SVM–NN method is proposed taking advantage of the SVM and NN model.

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