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All Journal Bulletin of Electrical Engineering and Informatics
Cries Avian
Universitas Jember
Electrical & Electronics Engineering

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Multilayer extreme learning machine for hand movement prediction based on electroencephalography Khairul Anam; Cries Avian; Muhammad Nuh
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2626

Abstract

Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
Capacitor bank controller using artificial neural network with closed-loop system Widjonarko Widjonarko; Cries Avian; Andi Setiawan; Moch. Rusli; Eka Iskandar
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.054 KB) | DOI: 10.11591/eei.v9i4.2411

Abstract

The problem of power factor in the industry is critical. This is due to the issue of low power factor that can make the vulnerability of industrial equipment damaged. This problem has been resolved in various ways, one of which is the Automatic Power Factor Correction, with the most popular device called capacitor bank. There are also many methods used, but several methods require certain calculations so the system can adapt to the new plant. In this study, researchers proposed a capacitor bank control system that can adapt to plants with different capacitor values without using any calculations by using an Artificial Neural Network with a closed-loop controller. The system is simulated using Simulink Matlab to know the performance with two testing scenarios. The first is changing the value of the power factor on the system and changing the value of the capacitor power at each bank, the second comparing it with the conventional methods. The results show that the system has been able to adapt to different capacitor power values and has a better performance than the conventional method in power factor oscillation due to the extreme power factor interference
Co-Authors Anam, Khairul Andi Setiawan Eka Iskandar Moch. Rusli Muhammad Nuh Widjonarko