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A fundamental goal of unsupervised feature selection is denoising, which aims to identify and reduce noisy features that are not discriminative. Due to the lack of information about real classes, denoising is a challenging task. The noisy features can disturb the reasonable distance metric and result in unreasonable feature spaces, i.e., the feature spaces in which common clustering algorithms cannot...
Aiming at the harsh electromagnetic environment, this paper presents a new method to extract partial discharge signal, which using fast Fourier transform suppress discrete spectrum interference, and then use discrete wavelet transform delete the residual noise all together. Further more, there is also bring out how to modulate the fast Fourier transform denoising threshold to achieve the best denoising...
The extraction of partial discharge (PD) signals from excessively noisy environment is crucial to on-line PD measurement. Recent research shows that the wavelet transform (WT) has achieved good effect in noise rejection in PD on-line detection. This paper presents some vital issues of WT implementation in extracting PD signals, including optimal mother wavelet selection, decomposition level selection,...
The suppression of noise is crucial prior to any partial discharge (PD) data analysis in on-line PD measurement. Recent research shows that the Wavelet transform (WT) has achieved good effect in noise rejection in on-line PD detection. This paper presents the vital issues of WT implementation in PD signals extraction, including optimal mother wavelet selection, decomposition level selection, threshold...
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