Minia University

About: Minia University is a education organization based out in Minya, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 4967 authors who have published 8986 publications receiving 108384 citations.

Functionalization of Electrospun Titanium Oxide Nanofibers with Silver Nanoparticles: Strongly Effective Photocatalyst

Abstract: In the present study, we are introducing silver-doped titanium oxide nanofibers produced by electrospinning technique. Calcination of dry nanofiber mats consisting of silver nitrate–titanium isopropoxide/PVAc in air at 600°C for 1 h leads to produce Ag-doped titania nanofibers. Two dyes have been invoked to check the photocatalytic ability of the produced nanofibers; methylene blue dihydrate and methyl red. The obtained results indicated that the silver-doped titanium oxide nanofibers can eliminate >92% of the methylene blue dye within 10 min only. In a case of methyl red, almost the dye was decayed (93%) within 3 h.

A Novel Adaptive EEMD Method for Switchgear Partial Discharge Signal Denoising

Abstract: The elimination of a variety of noises such as the narrow-band interference in the detection of partial discharge (PD) signals in switchgear is an intractable issue. Furthermore, the self-adaptation in the denoising process is weak. A partial discharge-based novel adaptive ensemble empirical mode decomposition (Novel Adaptive EEMD, NAEEMD) method is proposed in this paper for noise reduction. First, the signal is decomposed using the EEMD, only the first-order natural mode is decomposed until the signal margin reaches the EEMD decomposed termination condition. After removing the first-order mode, noise is added to the residual signal, and the remaining signal components are decomposed in the next stage. At last, the intrinsic mode function (IMF) of the noise reduction reconstruction is adaptively selected. The latter is accomplished by combining the energy density and the average period of the IMF correlation coefficient method. Meanwhile, the proposed method provides a new strategy for pre-processing the PD signal of the switchgear. The outcomes of the proposed NAEEMD de-noising method have been compared with the conventional wavelet denoising algorithm (WDA) and EMD-based threshold denoising for validation. The simulation results showed a good denoising effect and effectiveness of the proposed method compared to the WDA and EMD-based threshold denoising. Furthermore, an experimental simulation utilizing actual switchgear PD signal has been performed to verify the noise reduction effectiveness of the proposed method.