Classification of electrical appliances using magnetic field and probabilistic neural network

Nurul Aishah Mohd Rosdi, F. H. Nordin, A. Ramasamy, Nur Badariah Ahmad Mustafa
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引用次数: 1

Abstract

Many researches have proven that power lines and electrical appliances do emit electromagnetic fields and can be harmful to human's health. However, research on the effect of the magnetic fields on human's health is not yet conclusive. Instead of letting the magnetic fields emit by the electrical appliances be wasted, this paper aims to use the magnetic fields to classify or identify the electrical appliances being used. Table fans, blenders and hairdryers are the electrical appliances used for this purpose where they are divided into three different categories of usage i.e. (i) used less than 1year (ii) used between 1 to 5 years and (iii) used more than 5 years. The magnetic fields are measured from all the nine appliances. Then, the features of the magnetic fields are extracted and trained offline using the Probabilistic Neural Network (PNN). From the results, it is shown that the PNN is able to identify the type of electrical appliance being used regardless of the appliances years of usage using magnetic fields emitted by the appliances.
利用磁场和概率神经网络对电器进行分类
许多研究已经证明,电力线和电器确实会发射电磁场,对人体健康有害。然而,关于磁场对人体健康影响的研究尚无定论。本文旨在利用磁场对正在使用的电器进行分类或识别,而不是让电器发出的磁场被浪费。表扇、搅拌机和吹风机是为此目的而使用的电器,它们分为三种不同的使用类别,即(i)使用不到一年(ii)使用1至5年及(iii)使用超过5年。从所有9个设备测量磁场。然后,利用概率神经网络(PNN)对磁场特征进行提取和离线训练。从结果来看,PNN能够利用电器发出的磁场识别正在使用的电器的类型,而不管电器的使用年限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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