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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Noha Abed-Al-Bary Al-jawady
Northern Technical University
Control & Systems Engineering Electrical & Electronics Engineering

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Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier Firas Saaduldeen Ahmed; Noha Abed-Al-Bary Al-jawady
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1180-1187

Abstract

Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.
An intelligent overcurrent relay to protect transmission lines based on artificial neural network Noha Abed-Al-Bary Al-Jawady; Mohammed Ahmed Ibrahim; Laith A. Khalaf; Mahmood Natiq Abed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i2.pp1290-1299

Abstract

Power systems are susceptible to faults due to system failures or natural calamities. This could be caused by damage to power system components, resulting in an interruption of power delivery to clients. Overcurrent relays are important relays that protect distribution feeders, transmission lines, transformers, and other components. The intelligent relay can perform both primary and secondary functions. Line-to-ground (L-G) faults are the most common occurrence in long transmission lines, posing a serious threat to electrical equipment. This article presents improved fault classification for transmission line overcurrent protection and highlights the use of artificial neural network (ANN) techniques to protect transmission lines of 100 km (terco type). An ANN is used to classify the faults. A back propagation neural network (BPNN) is used in this case. The neural network has been trained to classify faults in transmission lines for overcurrent protection. Various fault conditions are considered. In the event of a fault condition, the output of a neural network will be a tripping signal. The MATLAB neural network tool and the Simulink package are used to model the suggested method.
Co-Authors Firas Saaduldeen Ahmed Laith A. Khalaf Mahmood Natiq Abed Mohammed Ahmed Ibrahim