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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mahajan, Rashima
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Comparative analysis of different types of pulse width modulation techniques for multilevel inverters R, Palanisamy; Devi, M. Nivethitha; T. R, Manikandan; Devi, K. Mekala; Mahajan, Rashima; D, Selvabharathi; K, Selvakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp680-688

Abstract

Multilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation.
Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks Kannan, Deeba; Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan; Venkatarajan, Shanmugasundaram; Mahajan, Rashima; Gunasekaran, Brindha; Murugamani, Pandi Maharajan; Dhandapani, Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2090-2100

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Co-Authors D, Selvabharathi Devi, K. Mekala Devi, M. Nivethitha Dhandapani, Karthikeyan Gunasekaran, Brindha K, Selvakumar Kannan, Deeba Murugamani, Pandi Maharajan R, Palanisamy Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan T. R, Manikandan Venkatarajan, Shanmugasundaram