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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ramkumar, Ravindran
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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A comprehensive review on different types of fuel cell and its applications Ramasamy, Palanisamy; Muruganantham, Balakrishnan; Rajasekaran, Stanislaus; Durai Babu, Babu; Ramkumar, Ravindran; Aparna Marthanda, Ayyalasomayajula Venugopala; Mohan, Sadees
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.v13i2.6348

Abstract

This review article provides an overview of various types of fuel cells that are currently being researched and developed. Fuel cells are electrochemical devices that convert chemical energy directly into electrical energy, making them a promising technology for clean and efficient energy production. The review covers the principles of operation and key characteristics of proton exchange membrane fuel cells (PEMFCs), solid oxide fuel cells (SOFCs), alkaline fuel cells (AFCs), direct methanol fuel cells (DMFCs), and microbial fuel cells (MFCs). The article also discusses the advantages and limitations of each type of fuel cell, as well as the current research and development efforts aimed at improving their performance and reducing their costs. Overall, this review provides a comprehensive understanding of the various types of fuel cells and their potential applications in the field of energy production.
Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon Kannan, Deeba; Balakrishnan, Amutha; Devi, K. Mekala; Singh, Nagendra; Kiruba, P. Angelin; Ramkumar, Ravindran; Karthikeyan, Dhandapani
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5432

Abstract

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.
Co-Authors Aparna Marthanda, Ayyalasomayajula Venugopala Balakrishnan, Amutha Devi, K. Mekala Durai Babu, Babu Hemalatha, Selvaraj Hepzibah, Albert Alice Kannan, Deeba Karthikeyan, Dhandapani Kiruba, P. Angelin Mohan, Sadees Muruganantham, Balakrishnan Palanisamy, Ramasamy Rajasekaran, Stanislaus Ramasamy, Palanisamy Santhakumari, Valarmathi Thangamani Singh, Nagendra Sugavanam, Vidyasagar Venkatarajan, Shanmugasundaram