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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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The fog computing for internet of things: review, characteristics and challenges, and open issues Al-Shareeda, Mahmood A.; Alsadhan, Abeer Abdullah; H. Qasim, Hamzah; Manickam, Selvakumar
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.v13i2.5555

Abstract

The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.
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.
Improved bidirectional long short term memory-based QRS complex detection using autoencoder Insani, Asep; Wibowo, Suryo Adhi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7033

Abstract

In this paper, we propose a new technique to improve QRS complex detection. This technique consists of incorporating an autoencoder and bidirectional long short term memory (BiLSTM). The autoencoder used is a stacked autoencoder and functions as signal filtering. Meanwhile, BiLSTM is used as a detector. Exploration of the effect of hyperparameter in the autoencoder was also carried out to determine the effect on QRS complex detection. Furthermore, the dataset used in this study is the MIT-BIH arrhythmia database. Based on the experimental results, the hyperparameter in the autoencoder that gives a better effect on QRS complex detection is 16-8. Finally, the proposed method out-of-perform state of the art algorithm with accuracy 99.94%.
Gaussian filter and CNN based framework for accurate detection of brain tumor by analyzing MRI images Sivakumar, S; Chaudhari, Poonam; Thatavarti, Satish; Sucharitha, G.; Mahesh, Basuthkar; Raghuvanshi, Abhishek
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.v13i6.6778

Abstract

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively.
Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach Doan, Hoa Van; Thi-Thuy Vu, Nga
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.v13i3.7054

Abstract

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.
Wideband coupled-line BPF with high-selectivity based on parallel transmission line signal interference technique for cellular base stations applications Firmli, Maroua; Zatni, Abdelkarim
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.v13i6.7782

Abstract

This paper presents a novel approach involving a modified wideband parallel coupled line bandpass filter (BPF). The proposed modification aims to achieve both enhanced skirt selectivity and simplified configuration, while ensuring the prevention from discontinuities between adjacent segments. The improvements in the performance of the filter’s structure are achieved through the integration of a signal-interference filtering model using parallel transmission line with distinct impedance values and electrical lengths at the input/output of the filter. This integration gives rise to the generation of multiple transmission zeros (TZs), thereby bolstering attenuation within both in-band and out-of-band frequency ranges. For the purpose of concept validation, a 3?? wideband band-pass filter with ?0=2.45 Ghz, accompanied by a fractional bandwidth of 50% was designed and simulated using microstrip RO6010 substrate. The outcomes of the simulation exhibit good performance, characterized by minimal insertion loss, wide bandwidth and the presence of seven TZs within the passband, resulting in high selectivity and sharp stopband rejection level.
The hybrid solar energized back-to-back high voltage direct current modular converter for distributed networks Thadkapally, Karunakar; Josh, Francisxavier Thomas; Joseph, Jeyaraj Jency; Jayakumar, Jayaraj
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.v13i1.5771

Abstract

High voltage direct current (HVDC) transmission is flexible towards the power control (produced by solar or wind) and can be transported over thousands of kilo meters with minimal losses over the high voltage alternative current (HVAC). It allows solar power to be integrated into the current power grid on a large scale. The author view in this article aims at providing an overview of methods used to integrate HVDC and solar systems. MATLAB/Simulink is used to simulate the solar power integration with HVDC transmission link. This article emphaises solar energy and grid integration, which results in quality and controlled electricity to the grid. Further the simulation studies are compared with real time data between the stations Pugalur AC grid (high solar energy region) and Thrissur AC grid (low solar energy region). Obtained results from the simulation, voltage and currents and power quality stresses the superiority towards the solar integration. The comparison studies enumerate the need to go situation for HVDC technology during the penetration of solar voltaic penetration into the utility network.
A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions Rahaman, Md Obaidur; Kashem, Mohammod Abul
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.v13i3.6866

Abstract

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.
Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell Pangaribowo, Triyanto; Mulyo Utomo, Wahyu; Budiman, Abdul Hamid; Abu Bakar, Afarulrazi; Khaerudini, Deni Shidqi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7045

Abstract

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.
Comparison of 5G performance post-merger between two network operators using field tests in urban areas Chatchalermpun, Surachai; Daengsi, Therdpong; Sriamorntrakul, Pakkasit; Phanrattanachai, Kritphon
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.v13i6.8307

Abstract

In late Q1/2023, DTAC and TRUE officially completed their merger. Consequently, this study was initiated to ascertain whether their respective fifth-generation (5G) networks had been seamlessly integrated several months following the merger. The investigation involved conducting drive tests along two predefined routes within the urban areas of Bangkok, employing the G-NetTrack pro tool for testing and data collection. Additionally, stationary tests were conducted in two crowded places using an application called Speedtest. Subsequently, an array of quality of service (QoS) metrics, including reference signal received power (RSRP), reference signal received quality (RSRQ), signal to noise ratio (SNR), download (DL), upload (UL) speeds and latency, were meticulously analyzed and presented. The findings of this study unveiled that, despite the successful completion of the DTAC and TRUE merger from a business standpoint, the technical integration of their respective 5G networks had not been finalized, although there were no significant differences between DTAC and TRUE for DL (p-value=0.542) and UL (p-value=0.090). Notably, significant differences were found between DTAC and TRUE for four metrics, including RSRP, RSRQ, SNR and latency (p-values0.05). Remarkably, roaming functionalities were still operational between the two networks.

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