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All Journal Bulletin of Electrical Engineering and Informatics Emerging Science Journal
Kadir, Kushsairy
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Electrical & Electronics Engineering Environmental Science

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Design and implementation of pulse width modulation gate control signals for two-level three-phase inverters Aboadla, Ezzidin Hassan; Kadir, Kushsairy; Khan, Sheroz
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.4249

Abstract

The switching control circuit in a DC to AC inverter is the critical part that is applied to control the power transistors insulated-gate bipolar transistor (IGBTs) and metal-oxide semiconductor field-effect transistor (MOSFETs). This paper proposes a high-performance and low-cost pulse width modulation (PWM) control signal with a 120º phase shift circuit for a two-level three-phase inverter. Typically, a PWM signal with a 120º phase shift for three-phase inverters is generated with the help of analogue components with more complicated designs and power losses or by using a microcontroller with necessary programming or coding. The proposed solution is to design a 120° three-phase shift circuit based on D flip-flops and the 555-timer to generate the clock signal for the flip-flop input in addition to the dead-time control circuit. The proposed circuit is controlled by one square wave signal as an input signal to generate six output PWM control signals at 50 Hz to operate six MOSFETs in the three-phase inverter. Simulation results in power simulation software PSIM and PROTEUS simulation tools are used to verify the proposed circuit. Hardware implementation of the proposed circuit and three-phase inverter is carried out to validate the performance of the proposed design.
Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique Khel, Muhammad Haris Kaka; Kadir, Kushsairy; Albattah, Waleed; Khan, Sheroz; Noor, MNMM; Nasir, Haidawati; Habib, Shabana; Islam, Muhammad; Khan, Akbar
Emerging Science Journal Vol. 5 (2021): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-SPER-14

Abstract

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF
Co-Authors Aboadla, Ezzidin Hassan Albattah, Waleed Habib, Shabana Khan, Akbar Khel, Muhammad Haris Kaka Muhammad Islam, Muhammad Nasir, Haidawati Noor, MNMM Sheroz Khan