Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Ravindran Ramkumar
Dhanalakshmi Srinivasan University

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PI controller for photovoltaic-fed novel multilevel inverter topologies Dhandapani Karthikeyan; Simran Rajiv Khiani; Albert Alice Hepzibah; Uthayakumar Jothilingam; Ravindran Ramkumar; Arumbu Venkadasamy Prathaban; Krishnasamy Vijayakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1268-1280

Abstract

This research describes the analysis and design of a unique 7-level and 9-level multi-level inverter topology with fewer DC power supplies and power switches, and a comparison using pulse width modulation (PWM). Comparisons of 7-level and 9-level multi-level inverters that analyze characteristics using optimal nearest level modulation (ONLM) technology are made using various PWM approaches such as nearest level modulation (NLM) technique, phase disposition (PD), phase opposition disposition (POD), and alternate phase opposition disposition (APOD). The output is evaluated based on a high Vrms, low total harmonic distortion (THD) harmonic profile. The controller is based on artificial intelligence (AI) and machine learning (ML) techniques. ONLM techniques are used to measure the voltage harmonics of the full load, several DC offset values are used. The control technology provided is validated using MATLAB/Simulink simulation tools and laboratory-based experimental testing.
Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator Dhandapani Karthikeyan; Deeba Kannan; Brindha Gunasekaran; Hemalatha Selvaraj; Saurabh Gupta; Ravindran Ramkumar; Krishnasamy Vijayakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1348-1358

Abstract

The paper presents an idea on pre-emptively ascertaining the soil pH value on an agriculture field amalgaming drone for aerial photo and subsequent smart irrigation model with the help of internet of things (IoT). The drone used for the aerial footage (multispectral imaging) consists of specialized cameras with filters that would help in ascertaining vegetation and health of the crops in the agriculture land. The IoT device used in smart irrigation model consists of sensors which accumulate data and execute the commands given in a recurring fashion of delay. Moreover, the use of triboelectric nanogenerator (TENG) would help in feasible energy harvesting for agricultural land use.
Early fire detection technique for human being using deep learning algorithm Kannan Deeba; Sattianadan Dasarathan; Srinivasa Rao Kandula; Krishnasamy Selva Sheela; Ravindran Ramkumar; Nagarajan Ashokkumar; Dhandapani Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1648-1655

Abstract

Fire and smoke detection in today’s world is a must, especially in clustered areas where a quick response can prevent significant damages and save lives. Early detection plays a significant role in preventing the fire from spreading by alerting the emergency response personnel. It may not be possible to install traditional fire and smoke detectors everywhere. As a result, incorporating fire and smoke detection into existing closed circuit television (CCTV) systems in various places can provide a warning to the appropriate authorities, allowing for quick action to prevent the fire from spreading. This work aims in developing an early fire and smoke prediction model with CCTV footage images and video frames. The images and videos are collected from multiple datasets available online. A convolutional neural network (CNN) model is developed for early detection and prevention of the spreading of fire and compares it with transfer learning models ResNet50 and VGG19. The model obtain an accuracy of around 94% using CNN model, 95% using VGG19 and 98% using ResNet 50. A model with high accuracy can replace traditional fire detection systems which can be both cost-effective and easy to implement to existing surveillance cameras.
Virtual analysis of machine learning models for diseases prediction in muskmelon Deeba Kannan; Balakrishnan Amutha; Sattianadan Dasarathan; Daniel Rosy Salomi Victoria; Vikas Maheshkar; Ravindran Ramkumar; Dhandapani Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1748-1759

Abstract

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.
Co-Authors Albert Alice Hepzibah Arumbu Venkadasamy Prathaban Balakrishnan Amutha Brindha Gunasekaran Daniel Rosy Salomi Victoria Deeba Kannan Deeba Kannan Dhandapani Karthikeyan Dhandapani Karthikeyan Hemalatha Selvaraj Kannan Deeba Krishnasamy Selva Sheela Krishnasamy Vijayakumar Nagarajan Ashokkumar Sattianadan Dasarathan Sattianadan Dasarathan Saurabh Gupta Simran Rajiv Khiani Srinivasa Rao Kandula Uthayakumar Jothilingam Vikas Maheshkar