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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.
Arjuna Subject : -
Articles 2,901 Documents
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Analysis of the power sector in Bangladesh: current trends, challenges, and future perspectives Sarker, Md Tanjil; Farid, Fahmid Al; Alam, Mohammed Jaber; Ramasamy, Gobbi; Karim, Hezerul Abdul; Mansor, Sarina; Sadeque, Md. Golam
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.v13i6.7503

Abstract

Bangladesh’s economic development is largely dependent on the power sector, which promotes sustainability and growth. The country’s future energy security, however, is seriously threatened by the natural gas reserves running out by 2028. As a result, the current energy mix has to be modified right away to ensure Bangladesh’s sustained economic growth. This research paper offers a thorough analysis of Bangladesh’s power sector’s current state. With a focus on important metrics like installed capacity, electricity generation, and distribution infrastructure, the study seeks to provide insights into the sector’s opportunities, challenges, and strengths. The research highlighting the importance of energy security and forecasting the projected energy demand in Bangladesh. The study also looks at current projects and advancements that have shaped Bangladesh’s power industry. This research also provides an ideal energy option that supports Bangladesh's sustainable growth. This analysis offers significant insights into the dynamics of the power industry in Bangladesh, elucidating it is present trajectory, the challenges it encounters, and the potential avenues for achieving a more sustainable and resilient energy future.
The effect of thickness of a conductive nanocomposite ink printed on textile co-planar waveguide antenna Mohd Radi, Nor Hadzfizah; Ismail, Mohd Muzafar; Zakaria, Zahriladha; Razak, Jeefferie Abd
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4775

Abstract

In the area of wearable technology an enhancement of basic microstrip antenna is evolution of wearable textile antenna. A new development of wearable antenna is the incorporated of conductive plane using nanocomposite ink that embedded onto the fabric. In this paper, the performance of variety thickness of conductive Graphene-Ag-Cu ink on a drill fabric is presented. The performances include its resistivity and conductivity measurement. By performing a measurement using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and four-point probe, it can obtain and measure the composition and thickness of nanocomposite layered on a fabric and resistivity respectively. Hence, it can provide detailed information about the surface morphology, roughness, and thickness of the nanocomposite coating on the fabric as well as the electrical conductivity. Finally, the electrical conductivity increased to the fifth layered from 0.1473×104 S/cm up to 0.5393×104 S/cm.
Smart measurement and monitoring system for aquaculture fisheries with IoT-based telemetry system Megantoro, Prisma; Anugrah, Antik Widi; Abdillah, Muhammad Hudzaifah; Kustanto, Bambang Joko; Fadhilah, Marwan; Vigneshwaran, Pandi
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.v13i3.6900

Abstract

The instrumentation design of an online monitoring device for aquaculture media is discussed in this article. The main processor in this internet of things (IoT) real-time telemetry system is an ESP32 board. Temperature, acidity level, conductivity level, dissolved oxygen (DO) level, and degree of oxygen reduction in the water were the aquaculture parameters measured. The ESP32 collects data from each sensor, groups it into a dataset, displays it on the LCD, saves it to the SD card, and then uploads it to the real-time database. In addition, an Android application is being developed for users. This device has been tested to ensure that each measured parameter is accurate and precise. The accuracy test, one of the major results of laboratory scale tests, demonstrates that each parameter has a different measurement error that represents with average error absolute. Six tested sensors/instruments were subjected to the test. Average absolute error for temperature sensor is +0.76%, pH sensor is +1.52%, electrical conductivity (EC) sensor is +10.8%, oxidation reduction potential (ORP) sensor is +14.6%, DO sensor is +9.3%, and total dissolve solids (TDS) sensor is +13.2%. This device is very dependable and convenient for monitoring the condition of aquaculture media in real-time and accurately.
Controlling mobile robot in flat environment taking into account nonlinear factors applying artificial intelligence Huong, Tran Thi; Ha, Pham Thi Thu
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7818

Abstract

The article shows how to build and identify intelligent automatic control problems for mobile robots in a flat surface environment at the workplace, with known and unknown obstacles. Research and develop programming and control methods as an operating system for mobile robots robot operating system (ROS). Update map data information, in the operating environment, robot position control process, obstacle overcoming process simultaneous positioning and mapping (SLAM). From there, we aim to calculate and determine the robot's motion trajectory to get a smart path. The positioning trajectory calculation system robots. The authors use actor-critic (AC) algorithm to research and develop control. Research results in simulations, in Gazebo environment and test runs on real mobile robots have shown high-quality practical performance of automatic navigation and control while using this algorithm.
Performance analysis of different methods for optimal sliding mode control of DC/DC buck converter Chlaihawi, Amer A.; Al-Modaffer, Ameen M.; Alhadrawi, Zaid
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5459

Abstract

Performance is always need to be considered in designing DC/DC buck converter. Despite the drastic use of DC/DC buck converter in industry, limitations due to unregulated voltage and current still persist. The dynamic performance of three methods of sliding mode control (SMC) were investigated. The comparative assessment of integral sliding mode control (ISMC) method, showed that the ISMC has an outstanding performance over the other tested methods of two variables with conventional SMC. The excellent performance of ISMC, under diverse operating conditions that include varying input voltage and load resistance, is achievable and it can provide a considerable edge over other control techniques in various field of industries, include electrical vehicle. The ISMC is highly preferable to overcome the problem of varying switching frequency, as well as optimizing power on transient response. The performance characteristic of ISMC shows fast dynamic response of various applications. Detailed simulations of the three SMC methods were carried out to validate the control algorithms using MATLAB/Simulink software.
A systematic review of radar technologies for surveillance of foreign object debris detection on airport runway Nugraha, Eka Setia; Apriono, Catur; Zulkifli, Fitri Yuli
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.v13i6.8040

Abstract

Flights are projected to reach eight billion globally by 2037, demanding airport operators manage operations effectively, including safety on the runway due to the high number of aircraft movements. One crucial issue is any foreign object, commonly known as foreign object debris (FOD), that must be detected and cleaned immediately to ensure aircraft safety when taking off, landing, and taxing. The International Civil Aircraft Organization (ICAO) reported that FOD causes 10.08% of aviation accidents. Most airports manually monitor and detect FOD, which could be more effective and dangerous. Therefore, it is important to provide FOD detection systems with proper technologies. Radar technologies are potential FOD detection techniques that offer robustness to weather fluctuation. However, some factors must be considered properly to provide an effective FOD system. This paper reviews radar technologies for FOD detection on airport runways by considering factors, including types of debris, detection coverage, mode of radars, frequencies, and attenuation. It was found that all critical factors considered contribute to the quality of detection. This paper will provide guidelines for developing FOD detection based on radar technologies regarding airport necessities and its specific environment.
A novel energy-efficient dynamic programming routing protocol in wireless multimedia sensor networks Putra, Emansa Hasri; Satria, Muhammad Haikal; Azwar, Hamid; Rianda, Rendy; Saputra, Muhammad; Darwis, Rizadi Sasmita
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.v13i2.5855

Abstract

Wireless multimedia sensor networks (WMSNs) have characteristics that may influence the routing decisions, such as limited energy resources, storage and computing capacity. Therefore, a routing optimization needs to be done to match the characteristics of the WMSNs. Existing routing protocols only consider energy efficiency regardless of energy threshold, maximum energy, and link cost collectively as the primary basis of routing. In this work, the energy-efficient dynamic programming (EEDP) protocol is proposed to optimize routing decisions that take into account the energy threshold, the maximum energy, and the link cost. Then, the protocol is compared with the dynamic programming (DP), and the ant colony optimization (ACO) protocol. The simulation results show that the EEDP protocol can improve energy efficiency of nodes and network lifetime of the WMSNs. Then, the EEDP protocol is also implemented into a network topology of 10 NodeMCU ESP32 devices. As a result, the EEDP protocol can work very well by selecting routes based on nodes that have the remaining energy above 50 and has the shortest distance. The average delay in sending data for the entire route for the 10 iterations of sending data is 3.99 seconds.
Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification Rahouma, Kamel Hussein; Mabrouk, Shahenda Mahmoud; Aouf, Mohamed
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.v13i3.6828

Abstract

Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
Automatic prediction of learning styles: a comprehensive analysis of classification models Lestari, Uning; Salam, Sazilah; Choo, Yun-Huoy; Alomoush, Ashraf; Al Qallab, Kholoud
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7456

Abstract

Learning styles are a topic of interest in educational research about how individuals acquire and process information in offline or online learning. Identification of learning styles in the online learning environment is challenging. The existing approaches for the identification of learning styles are limited. This study aims to review the many learning styles characterized by various classification approaches toward the automatic prediction of learning styles from learning management system (LMS) datasets. A systematic literature review (SLR) was conducted to select and analyze the most pertinent and significant papers for automatically predicting learning styles. Fifty-two research papers were published between 2015-2023. This research divides analysis into five categories: the classification of learning style models, the collection of the collected dataset, learning styles based on the curriculum, research objectives related to learning styles, and the comprehensive analysis of learning styles. This study found that learning style research encompasses diverse theories, models, and algorithms to understand individual learning preferences. Statistical analysis, explicit data collection, and the Felder-Silverman model are prevalent in research, highlighting the significance of algorithm improvement for optimizing learning processes, particularly in computer science. The categorization and understanding of various methods offer valuable insights for enhancing learning experiences in the future.
Reliability analysis in distribution system by deep belief neural network Ramalingappa, Likhitha; Ekanthaiah, Prathibha; Ali, MD Irfan; Manjunatha, Aswathnarayana
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.v13i2.6324

Abstract

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

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