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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
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Articles 75 Documents
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Search results for , issue "Vol 13, No 6: December 2024" : 75 Documents clear
Enhancing performance of slotted ALOHA protocol for IoT covered by constellation low-earth orbit satellites Chabou, Zakaria; Addaim, Adnane; Ait Madi, Abdessalam
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8158

Abstract

Recently, constellation of satellites has drawn a lot of interest from academia and industry as potential solution for extensive coverage of wide range of internet of things (IoT). In this work, IoT devices was assumed to be covered by constellation of low-earth orbit (LEO) satellites where the medium access control (MAC) technique called slotted ALOHA is employed. In this article, we use a constellation of satellites to reduce the collision domain and enhancing performance in order to obtain maximum results. We have carried out some modeling and simulations to optimize the number of satellites with different erasure probabilities with respect to IoT devices in order to enhance throughput and stability of slotted ALOHA protocol using the network simulator 2 (NS2). The numerical results have shown an improvement in terms of throughput and stability. And the simulation of the same system using NS2 is conducted and shows a good correlation with the theoretical study. Where the throughput reached 0.82% instead of 0.52%. Our findings offer proof that this method helps to use large number of IoT, and reduce collisions compared to conventional slotted ALOHA.
Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection Shiddiq, Minarni; Saktioto, Saktioto; Salambue, Roni; Wardana, Fiqra; Dasta, Vicky Vernando; Harmailil, Ihsan Okta; Rabin, Mohammed Fisal; Arpyanti, Nisa; Wahyudi, Dilham
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8120

Abstract

Oil palm fresh fruit bunches (FFBs) are the raw material of crude palm oil (CPO) on which ripeness levels of FFBs are essential to obtain good quality CPO. Most palm oil mills use experienced graders to evaluate FFB ripeness levels. Researchers have developed rapid and non-destructive methods for ripeness detection using computer vision (CV) and deep learning. However, most of the experiments used color cameras, such as a webcam or a smartphone, limited to visible wavelengths, and used still FFBs on–trees or on the ground. This study developed a light-emitting diode (LED)-based multispectral imaging system with deep learning for rapid and real-time ripeness detection of oil palm FFBs on a moving conveyor. The ripeness levels used were unripe and ripe. We also evaluated the spectrum of reflectance intensities for the ripeness levels. The ripeness detection system employed a two-class you only look once version 4 (YOLOv4) detection model using a dataset of 2000 annotated unripe and ripe FFB multispectral images and a video of 30 moving FFBs for real-time testing. The results show a promising method to detect oil palm FFB ripeness with an average accuracy of 99.66% and a speed range of 3.32-3.62 frame per second (FPS).
State of charge control based improved hybrid energy storage system for DC microgrid Gajjar, Rital R.; Giri, Nimay Chandra; Patel, Unnati; Gajjar, Rakeshkumar C.; Dave, Dhavalkumar; Aly, Abouelmaaty M.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7759

Abstract

This paper proposes a non-communication power management plan for a renewable solar-photovoltaic (PV) hybrid direct current (DC) microgrid consisting of batteries and supercapacitors (SCs). An effective control strategy for bidirectional converters has been proposed for power supply and load generation at different operating modes and state of charge (SOC) limits of the hybrid energy storage system (HESS). The battery and SC combination provides power to the load during normal or peak operating hours. The proposed hybrid power management system was tested under uneven load and generation using MATLAB. The proposed control enhanced the operation of microgrids by utilizing HESS with a novel control strategy based on the SOC. Seamless mode switches among different operating modes are also presented in this paper. This approach ensures stable current control, minimizes charging-discharging mode changes, mitigates the risk of overcharging and over-discharging batteries, extends battery service life, and balances the SOC across different energy storage units. Consequently, this strategy enhances the operational stability and economic efficiency of the DC microgrid. The proposed methodology provided better power allocation and improved the life of the battery.
Power loss estimation utilizing the flexibility of peak power loss regression equations based on 11 kV base case feeder Masdzarif, Nur Diana Izzani; Ibrahim, Khairul Anwar; Gan, Chin Kim; Au, Mau Teng
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7808

Abstract

Distribution network feeder characteristics can typically be divided into groups based on factors including length, load distribution along the feeder, peak demand, installed capacity, and load profile. By comparing the parameters to those of similar feeders with known losses, it is usually possible to predict the power losses and technical losses (TL) of the respective feeders pretty accurately. However, it is exceedingly difficult and time-consuming to estimate the losses with various variables and characteristics over such a large area. This paper proposed that through base case feeder modeling and simulation utilizing typical network and load data, feeders’ peak power loss (PPL) functions can be established as a simple and effective power loss estimation method. Hence, the least time-consuming way of using a PPL regression equation based on a base case feeder is established in this paper to estimate the losses. The flexibility of PPL is proven through the case study. In the end, the results obtained between PPL and peak power demand (PPD) are demonstrated to be precisely proportional and the method is proven as a simple power loss estimation method due to the flexibility of the PPL regression equation.
Securing IoT edge device communication with efficient ECC middleware for resource-constrained systems Mohamed Yusoff, Zainatul Yushaniza; Ishak, Mohamad Khairi; AB Rahim, Lukman
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7602

Abstract

The internet of things (IoT) rapidly grows into various parts of life. However, it has significant obstacles during setup and deployment, particularly in terms of network segmentation, administration, and security at all tiers, from physical to application. While IoT provides several advanced features and benefits, it is also vulnerable to security threats and flaws that must be thoroughly investigated to avoid misuse. Cryptographic approaches are routinely used to address these security concerns. Message queuing telemetry transport (MQTT), an application layer protocol, is vulnerable to various known and undisclosed security flaws. Integrating encryption techniques within the MQTT protocol to provide secure data flow is a potential strategy for increasing security. This study provides a middleware broker that improves authentication processes, securing connections between cloud servers and resource-constrained devices. Using a Java Servlet and the elliptic curve cryptography (ECC) technique, the study creates a system for creating encrypted identification keys within a web-based transaction framework. This system intends to provide asymmetric authentication that is energy and resource-efficient, with a focus on cost minimization. It also includes a security feature to protect users from common internet threats. The system's efficacy, including its low energy usage of only 4 mJ per device, is thoroughly tested, proving it meets the original protocol criteria.
Evaluating drones as bird deterrents in industrial environments: multirotor vs fixed-wing efficacy Hornain, Imran Mohd; Rosely, Nik Fadzly N
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7359

Abstract

Unmanned aerial vehicles (UAVs) or drones have been proposed as deterrent tools to mitigate pest birds’ problems. Many studies have been conducted to evaluate the efficacy of drones, mainly to protect crops, fishponds and airports. Little information can be acquired on using drones in industrial areas. In this study, two types of drones, categorized as multirotor drones and fixed-wing drones, were used to evaluate their efficacy in reducing pest birds, Asian glossy starling (Aplonis panayensis) flocks in one of the semiconductor factories in Kulim Hi-tech Park, Kedah, Malaysia during dusk. Each drone was evaluated during its five minutes of operation time and five minutes after landing. Control data were also taken to compare drone treatment days with no drone treatment days. Our result shows a significant difference between multirotor drone treatment and control treatment but not between fixed-wing drone treatment and control treatment due to different altitudes applied, ambient light intensity and size of flight path covered. We suggest implementing biomimetic design into drones and applying other conventional ground deterrents to prolong the residual effect of post-treatment.
Application of smart hydrogels scaffolds for bone tissue engineering Owida, Hamza Abu; Alnaimat, Feras; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad; Turab, Nidal M.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7608

Abstract

Recent attention in the biomedical and orthopedic sectors has been drawn towards bone defects, emerging as a prominent focus within orthopedic clinics. Hydrogels, due to their biocompatibility, elevated water content, softness, and flexibility, are increasingly acknowledged in tissue regeneration research. Advanced biomaterials offer numerous advantages over traditional materials, notably the capacity to respond to diverse physical, chemical, and biological stimuli. Their responsiveness to environmental cues, such as three-dimensional (3D) morphology and phase conditions, holds promise for enhancing the efficacy of localized bone lesion repairs. This paper aims to revolutionize the treatment of severe bone abnormalities by providing a comprehensive examination of hydrogels capable of morphological adaptation to environmental changes. It delineates their classification, manufacturing principles, and current research status within the field of bone defect regeneration.
An optimization based deep learning approach for human activity recognition in healthcare monitoring Kalyanasundaram, Aparna; Panathula, Ganesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8000

Abstract

Medical images are comprised of sensor measurements which help detect the characteristics of diseases. Computer-based analysis results in the early detection of diseases and suitable medications. Human activity recognition (HAR) is highly useful in applications related to medical care, fitness tracking, and patient data archiving. There are two kinds of data fed into the HAR system which are, image data and time series data of physical movements through accelerometers and gyroscopes present in smart devices. This study introduced crayfish optimization algorithm with long short term memory (COA-LSTM). The raw data is obtained from three datasets namely, WISDM, UCI-HAR, and PAMAP2 datasets; then, pre-processing helps in removal of unwanted information. The features from pre-processed data are reduced using principal component analysis and linear discriminant analysis (PCA-LDA). Finally, classification is performed using COA-LSTM where, the hyperparameters are fine-tuned with the help of COA. The suggested method achieves a classification accuracy of 98.23% for UCI-HAR dataset, whereas the existing techniques like convolutional neural network (CNN), multi-branch CNN-bidirectional LSTM, CNN with gated recurrent unit (GRU), ST-deep HAR, and Ensem-HAR obtain a classification accuracy of 91.98%, 96.37%, 96.20%, 97.7%, and 95.05%, respectively.
Handwritten digit recognition using a column scheme-based local directional number pattern Aouine, Mohammed; Gattal, Abdeljalil; Djeddi, Chawki; Abbas, Faycel
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7906

Abstract

One of the most well-known challenges in computer vision and machine learning is the recognition of handwritten digits. This study presents an advanced approach to improving isolated-digit recognition through the use of advanced feature extraction techniques. For example, digit recognition is commonly used to read numbers on forms and checks in banks. This paper introduces a novel method of extending the local directional number pattern (LDNP) to a column scheme using two different masks and their resolutions. A new descriptor of the LDNP column scheme is being proposed that combines derivative Gaussian and Kirsch masks in order to enhance textural analysis and capture more detailed local textual information. This approach is highly efficient and robust, able to handle variations in size, shape, and slant. Additionally, the support vector machine (SVM) is employed as a classifier, which has been shown to make better decisions. The empirical investigation is carried out using the CVL dataset, resulting in recognition rates that are comparable with the latest advancements in the field. The overall precision of 96.64% is achieved, outperforming existing similar works.
Power system stability and control: a comprehensive review focusing on the rotor angle case Mohamad Murad, Nor Syaza Farhana; Kamarudin, Muhammad Nizam; Md Rozali, Sahazati; Zakaria, Muhammad Iqbal
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8054

Abstract

This paper provides a review of power system stability, focusing on the rotor angle case. To gain a preliminary understanding of the stability studies, the discussion begins with an overview of generators in power system generation. The distinguishing parameters of synchronous generators as compared to their counterparts such as induction generators, inductor alternators, and direct current generators are also emphasized. The discussion that is not bounded within their stability issues and control strategies is briefly assessed. The shortcomings and advantages of various modeling approaches are also discussed therein. To extend the thoughts, this review includes a thorough discussion and classification of power system stability, which includes rotor angle stability, frequency stability, and voltage stability. The stability of the rotor angle is important as it ensures frequency stability and voltage stability. This paper also presents the power system modeling approach that is able to facilitate the rotor angle stability studies. This paper also aims to review the established rotor angle stabilizers and algorithms developed by previous researchers.

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