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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Applied Power Engineering (IJAPE)
Dhanasekar Ravikumar
Sri Sairam Engineering College
Automotive Engineering Electrical & Electronics Engineering

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Development of 15/33 level constant and variable DC source inverter for different loading conditions Vijayaraja Loganathan; Dhanasekar Ravikumar; Ganesh Kumar Srinivasan; Deepak Balachandran Kasthuri
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1051-1063

Abstract

In this paper, a design of symmetric and asymmetric multilevel inverter (MLI) with few quantities of switch is presented. The structure can be able to operate with both symmetric and asymmetric sources. The presented model is capable of producing output levels of 15 with symmetric structure and 33 with asymmetric structure. The tendered circuit is constructed with 14 switches and 7 sources. The presented MLI can be placed in moderate-voltage applications such as: electrical machine drives. The circuit's switching sequences are framed by a detailed discussion from its operation. In MATLAB/Simulink, the inverter is simulated for resistive, resistive-inductive, and induction motor loads, and the results are portrayed. Also, the working of the inverter is monitored in terms of harmonics presence in the load signals. Additionally, the presented MLI is developed in real time to evaluate its performances. The results obtained from the real time inverter are found satisfactory.
Design of a portable IoT robot with azure machine learning for monitoring mine workers’ health Shanthi Natarajan; Vijayaraja Loganathan; Dhanasekar Ravikumar; Diwakar Venkat Nalini; Harish Elangovan; Balaji Arikrishnan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp577-588

Abstract

The mining environment exposes workers to physical, environmental, and health risks. The lack of effective real-time health monitoring systems leads to delayed medical responses. Hence, this paper discusses developing a portable Internet of Things (IoT) robot with advanced machine learning and cloud computing to monitor mine workers’ health and send out alerts. The system is equipped with IoT sensors that monitor parameters continuously, such as heart rate, body temperature, blood pressure, and environmental factors (gas concentrations, air quality). Data collected in real-time is transmitted to a cloud-based platform for analysis using advanced machine learning algorithms. MQ-135 detects harmful gases, and DHT11 measures humidity and transmits data to the Arduino UNO. The HC-SR04 sensor measures object distances by emitting ultrasonic waves and detecting their echoes, aiding in obstacle detection. The NEO-6M GPS with GSM SIM900 modules transmit location data and emergency alerts via the GSM network, enabling responses to potential dangers. Simulation via Proteus validates the robot’s transceiver connectivity, mobility, and sensing functions. To enhance monitoring precision, the system adopts XGBoost, which classifies mine conditions, and the training model achieves 96.77% accuracy with high precision and recall. The system with Azure Machine Learning improves detection accuracy, raising temperature, CO, NH₄, and NO₂ precision by 7.25%, 15%, 17%, and 18%, respectively. Thus, the system features an intelligent alert mechanism to notify users of emergencies, enhancing worker safety and minimizing health-related risks in mining operations.
Co-Authors Balaji Arikrishnan Deepak Balachandran Kasthuri Diwakar Venkat Nalini Ganesh Kumar Srinivasan Harish Elangovan Shanthi Natarajan Vijayaraja Loganathan