scholarly journals Adaptive algorithms: a bibliographic review

Athenea ◽  
2021 ◽  
Vol 2 (5) ◽  
pp. 35-40
Author(s):  
Luis Gonzalez

The analysis of a research work developed in the company C.V.G CARBONORCA of Venezuela is presented, which has two gas purification plants for the cooking area, designed to purify the gas that comes from the cooking ovens. Each plant is made up of solenoid valves, pneumatic valves, transmitters, process mimic panel and a supervisory system. All these elements are governed by a SIEMENS S5-115U PLC which is in a state of obsolescence, which is why the replacement of these automata by ALLEN BRADLEY ContolLogix automata was designed, in order to guarantee continuity in operations in plant. The research was done with a descriptive design of the field experimental type. A code for each gas treatment plant was obtained in RSLOGIX 5000 v17.00.00 and the update of the database of the supervisory system. The operation of the program was also verified through a simulation of the plant in a supervisory system, the deployment of which was designed for this purpose. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel References [1]M. Simao, N. Mendes, O. Gibaru y P. Neto, «A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction,» IEEE Access, vol. 7, pp. 39564 - 39582, 2019. [2]Instituto de Estadística de la Organización de las Naciones Unidas para la Educación, la Ciencia y la Tecnología, «Clasificación Internacional Normalizada de la Educación CINE,» UNESCO Institute for Statistics, Montréal, 2011. [3]Y. Zheng y H. Xiaogang, «Interference Removal From Electromyography Based on Independent Component Analysis,» IEEE Trans Neural Syst Rehabil Eng, vol. 27, nº 5, pp. 887-894, Mayo 2019. [4]B. Afsharipour, F. Petracca, M. Gasparini y R. Merletti, «Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing,» Journal Electromyography Kinesiology, vol. 31, pp. 144 - 153, 2016. [5]M. Niegowski, M. Zivanovic, M. Gómez y P. Lecumberri, «Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander,» de 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italia, 2015. [6]S. D. Soedirdjo, K. Ullah y R. Merletti, «Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis,» de Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2015. [7]A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard y Y. Laurillau, «Feature extraction of the first difference of EMG time series for EMG pattern recognition,» Computer Methods and Programs in Biomedicine, vol. 117, nº 2, pp. 247-256, Noviembre 2014. [8]M. Malboubi, F. Razzazi, M. Aliyari y A. DAvari, «Power line noise elimination from EMG signals using adaptive Laguerre filter with fuzzy step size,» de 17th Iranian Conference of Biomedical Engineering (ICBME), Isfahan, Iran, 2010. [9]C. Luca, L. Gilmore, M. Kuznetsov y S. Roy, «Filtering the surface EMG signal: Movement artifact and baseline noise contamination,» J. Biomech, pp. 1573-1582, 28 Mayo 2010. [10]R. Mello, L. Oliveira y J. Nadal, «Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram,» Comput Methods Programs Biomed, vol. 1, nº 87, pp. 28-35, 2007. [11]A. Botter y T. Vieira, «Filtered virtual reference: A new method for the reduction of power line interference with minimal distortion of monopolar surface EMG,» IEEE Transactions on Biomedical Engineering, vol. 62, nº 11, pp. 2638 - 2647, 2015. [12]J. R. Potvin y S. H. Brown, «Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates,» J. Electromyogr. Kinesiol., vol. 14, nº 3, pp. 389-399, 2004. [13]D. T. Mewett, K. J. Reynolds y H. Nazeran, «Reducing power line interference in digitised electromyogram recordings by spectrum interpolation,» Med. Biol. Eng. Comput., vol. 4, nº 42, pp. 524-531, 2004. [14]D. T. Mewett, H. Nazeran y K. J. Reynolds, «Removing power line noise from recorded EMG,» de 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001.

Author(s):  
Martina Ladrova ◽  
Radek Martinek ◽  
Jan Nedoma ◽  
Marcel Fajkus

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.


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