Unsupervised Machine Learning for Blind Separation of Multiple PD Sources | IEEE Conference Publication | IEEE Xplore

Unsupervised Machine Learning for Blind Separation of Multiple PD Sources


Abstract:

We tested the ability of unsupervised machine learning approaches to separate an unknown number of artificial PD sources simultaneously acting, recorded by a multi-antenn...Show More

Abstract:

We tested the ability of unsupervised machine learning approaches to separate an unknown number of artificial PD sources simultaneously acting, recorded by a multi-antenna system in the Ultra-High Frequeny range. Each pulse has been described with a minimum set of features and successively undergone to an unsupervised clustering procedure via Hierarchical Agglomerative Clustering (HAC) technique. The results have been compared, in a fully blind performance test, with those obtained from a cross-correlation analysis of the fully sampled pulses that clearly separates three groups of pulses radiated from the three PD sources. A limited set of mono-dimensional features (maximum amplitude, standard deviation, phase, power) shows a cluster detection accuracy of 58%. A different set of features (including maximum amplitude, standard deviation, phase, power, skewness value, kurtosis value), shows an accuracy of 64% while performance parameters as Precision and F1-Score show better results. Despite the sub-optimal results, the false-negative detection rate is low and the computational cost of this simplified approach is dramatically lower than the cross-correlation approach, thus allowing possible development in real-time supervising devices.
Date of Conference: 03-07 July 2022
Date Added to IEEE Xplore: 29 August 2022
ISBN Information:
Conference Location: Palermo, Italy

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