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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
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Articles 3 Documents
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Search results for , issue "Vol 40, No 1: October 2025" : 3 Documents clear
Analysis of solid oxide fuel cell systems for off-grid energy production Mehimmedetsi, Boudjemaa; Draidi, Abdellah; Smaani, Billel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp18-33

Abstract

This work presents a simulation study of a 50-kW solid oxide fuel cell (SOFC) power supply system that provides electricity to residential users. Indeed, many decentralized applications rely on renewable energy sources not connected to the primary power grid. Moreover, fuel cell modelling and simulation are critical for promoting renewable energy as they eliminate the need for physical prototypes, saving time and money. We have also developed a reliable model for simulating self-contained SOFC fuel cells. The elaborated model includes the kinetics of electrochemical processes and accounts for voltage losses in SOFCs. Our fuel cells produce the necessary electrical current to charge the device. Also, our system has fuel cells, a DC/DC converter, and an inverter with LCL filters. These components connect the fuel cell system to other power electronics and the electrical load. Furthermore, a mathematical model of a dual inverter system describes its control method, including the proportional and integral parameters in the voltage and current loops has been derived. The proposed model and system could be helpful for a standalone load.
Interrogative insights into depression detection via social networks and machine learning techniques Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp388-396

Abstract

As users on social networks (SNs) interact with one another by exchanging information, giving feedback, finding new content, and participating in discussions; thus, generating large volumes of data each day. This data includes images, texts, videos and can be used to help the user find out how they have been doing, when they were depressed, how not to be depressed, and other similar insights. Depression is one of the most common chronic illnesses and it has emerged as a global mental health problem. But the lack of these data is incomplete, sparse and sometimes inaccurate, and so the task of diagnosing depression using automated systems is still proving a challenge. Various techniques have been used to detect depression through the years however, machine learning (ML) and deep learning (DL) techniques offer better ways. In the context of that, this study reviews state-of-the-art ML and DL approaches for the detection of depression using systematic literature review (SLR) method as well as highlight fundamental challenges in literature, which future works can focus on. We hope that this survey will provide a better understanding of these strategies for the readers and researchers in the ML and DL fields, when it comes to diagnosis of depression.
A multi-path routing protocol for IoT-based sensor networks Rajkumar Dhamodharan, Udaya Suriya; Karani, Krishna Prasad; Pichandi, Saranya; Palani, Kavitha; Rajendran, Sathiyaraj
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 1: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i1.pp225-235

Abstract

Internet of things (IoT) based sensors are to link a big number of low-cost and power-integrated devices in a reliable manner. Numerous military and adventurous applications are regulated by communication among IoT sensors. The multi-path routing protocol (MRP) approach presented in this research to enhance secure routing in IoT sensors is significant. This technique makes use of data transfer routing and the relationships between network components. It finds the most efficient route between the nodes that minimizes communication overhead and is both reliable and economical in terms of shortest duration. The particle swarm optimization (PSO) technique is used to find the shortest path that is most cost-effective. To reach the target node, end-to-end data transmission must transit via intermediary nodes, which are provided by the routing path node history. The optimal path is chosen by MRP from PSO, and it traces the path to identify the intermediate nodes. In the unlikely event of a crisis, MRP offers the most affordable backup route for data transfer. When compared to earlier techniques, the outcomes of these current approaches enhance network efficiency, balance energy consumption among nodes, and routing attacks.

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