An improved MAMA-EMD for the automatic removal of EOG artifacts

Author(s):  
Mingai Li ◽  
Yuanyuan Zhang
Keyword(s):  
2015 ◽  
Vol 135 (8) ◽  
pp. 954-962
Author(s):  
Hirohisa Tsubakida ◽  
Yumie Ono ◽  
Atsushi Ishiyama

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1257
Author(s):  
Alexey Dorokhov ◽  
Alexander Aksenov ◽  
Alexey Sibirev ◽  
Nikolay Sazonov ◽  
Maxim Mosyakov ◽  
...  

The roller and sieve machines most commonly used in Russia for the post-harvest processing of root and tuber crops and onions have a number of disadvantages, the main one being a decrease in the quality of sorting due to the contamination of working bodies, which increases the quantity of losses during sorting and storage. To obtain high-quality competitive production, it is necessary to combine a number of technological operations during the sorting process, such as dividing the material into classes and fractions by quality and size, as well as identifying and removing damaged products. In order to improve the quality of sorting of root tubers and onions by size, it is necessary to ensure the development of an automatic control system for operating and technological parameters, the use of which will eliminate manual sorting on bulkhead tables in post-harvest processing. To fulfill these conditions, the developed automatic control system must have the ability to identify the material on the sorting surface, taking into account external damage and ensuring the automatic removal of impurities. In this study, the highest sorting accuracy of tubers (of more than 91%) was achieved with a forward speed of 1.2 m/s for the conveyor of the sorting table, with damage to 2.2% of the tubers, which meets the agrotechnical requirements for post-harvest processing. This feature distinguishes the developed device from similar ones.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3267
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fernando S. Brasil ◽  
Fabrício J. B. Barros

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.


1997 ◽  
Vol 144 (4) ◽  
pp. 281 ◽  
Author(s):  
M. Boloorian ◽  
J.P. McGeehan
Keyword(s):  

Author(s):  
Chenbei Zhang ◽  
Nabil Sabor ◽  
Junwen Luo ◽  
Yu Pu ◽  
Guoxing Wang ◽  
...  

2021 ◽  
pp. 1-10
Author(s):  
Rakesh Ranjan ◽  
A. Prabhakara Rao ◽  
Anish Kumar Vishwakarma

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