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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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Bandwidth enhancement of millimeter-wave microstrip patch antenna array for 5G mobile communication networks Umar Musa; Suleiman Babani; Suleiman Aliyu Babale; Abubakar Sani Ali; Zainab Yunusa; Sani Halliru Lawan
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4680

Abstract

This paper proposed enhancing the bandwidth of a millimeter wave microstrip patch antenna (MPA) and its array for a 5G mobile communication network. The proposed antenna is designed and fabricated on a Rogers RT Duroid 5,880 substrate with a standard thickness of 0.5 mm, a relative dielectric constant of 2.2, and a tangent loss of 0.0009. With a center frequency of 28 GHz, a measured return loss of -21.37 dB, a bandwidth of 1.14 GHz, and a gain of 6.27 dBi, the proposed single element operates in the local multipoint distribution service band. The proposed antenna is designed and manufactured as an array of 1×2 and 1×4 elements. The 2-element MPA array has a measured bandwidth of 1.207 GHz and a gain of 7.76 dBi, higher than that of a single element. The 4-element MPA array achieved a measured bandwidth of 2.685 GHz and a gain of 9.87 dBi, which is higher than the 2-element and single-element arrays at 28 GHz. This demonstrates that the array of antennas improves gain and bandwidth significantly. Hence, the proposed antenna and array are suitable for 5G mobile communication networks due to their small size.
Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid Linus Alwal Aloo; Peter Kamita Kihato; Stanley Irungu Kamau; Roy Sam Orenge
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4906

Abstract

The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.
New efficient fractal models for MapReduce in OpenMP parallel environment Muslim Mohsin Khudhair; Furkan Rabee; Adil AL_Rammahi
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4977

Abstract

Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.
Machine learning techniques for accurate classification and detection of intrusions in computer network Mutyalaiah Paricherla; Mahyudin Ritonga; Sandip R. Shinde; Smita M. Chaudhari; Rahmat Linur; Abhishek Raghuvanshi
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4708

Abstract

An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent the defences that are in place. Training and examination are the two basic components that make up the intrusion detection system (IDS) and each one may be analysed separately. During training, a number of distinct models are built, each of which is able to distinguish between normal and abnormal behaviours that are included within the dataset. This article proposes a combination of ant colony optimization (ACO) and the firefly approach for feature selection. The final outcome of giving careful thought to the selection of features will eventually result in greater accuracy of categorisation. When classifying various sorts of features, we make use of a wide variety of machine learning (ML) algorithms, including AdaBoost, gradient boost, and Bayesian network (BN), amongst others. The tests and assessments made use of data obtained from three distinct datasets, namely NSL-KDD, UNSW-NB15, and CICIDS 2017. The degree of performance of an individual may be broken down into its component parts, which include the F1 score, accuracy, precision, and recall. Gradient boost performs far better when it comes to recognising and classifying incursions.
A 2.45 GHz microstrip patch antenna design, simulation, and anlaysis for wireless applications Md. Sohel Rana; Bijoy Kumer Sen; Md. Tanjil-Al Mamun; Md. Shahriar Mahmud; Md. Mostafizur Rahman
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4770

Abstract

This paper designs, simulates, and analyzes the S-band microstrip patch antenna (MPA) for wireless applications. FR-4 (lossy) and Rogger RT/duroid, whose dielectric permittivity is 4.3 and 2.2, respectively, have been used as substrate materials. Simulation is done by computer simulation technology (CST) suite studio 2019 software. Simulations with FR-4 material showed that the return loss was -20.405 dB, the gain was 2.592 dB, the directivity was 7.47 dBi, the voltage standing wave ratio (VSWR) was 1.221, the bandwidth (BW) was 0.0746 GHz, and the efficiency was 34.69%. Also, Rogers RT/duroid material gives results of a return loss of -12.542 dB, a bandwidth (BW) of 0.0349 GHz, a gain of 8.092 dB, a directivity of 8.587 dBi, and an efficiency of 94.24%. The main goal of this antenna is to have a low return loss while getting as close as possible to a VSWR of 1. This will improve the antenna's gain, directivity, and efficiency compared to other antennas. Copper was used to make the patch and the ground, which were 0.35 mm and 0.0077 mm thick, respectively. The results obtained from the proposed antenna were better than those previously published in various in modern scientific journal and conference papers.
Classification of 27 heart abnormalities using 12-lead ECG signals with combined deep learning techniques Atiaf A. Rawi; Murtada Khalafallah Elbashir; Awadallah M. Ahmed
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4668

Abstract

An electrocardiogram (ECG) machine with a standard 12-lead configuration is the primary clinical technique for diagnosing abnormalities in heart function. Automated 12-lead ECG machines have the capacity to screen the general population and provide second opinions for physicians. However, expertise and time are required for manual ECG interpretation. Therefore, computer-aided diagnoses are of interest to the medical community. Hence, this study aims to build a deep learning (DL) model with an end-to-end structure that can categorize 12-lead ECG results into 27 different disorders. We use multivariate time-series data to construct a novel end-to-end DL model (based on combined convolutional neural networks (CNNs), long short-term memory, gated recurrent units, and a deep residual network structure) for feature representations and determining spatial relations among deep features. In addition, a dataset of 43,101 classified standard ECG recordings was collected from six different sources to guarantee the model’s ability to generalize and alleviate data divergence. As a result, the residual network-based model obtained promising outcomes and an accuracy of 0.97. According to the experimental data, it outperforms other methods.
Advanced optimal GA-PID controller for BLDC motor Hashmia S. Dakheel; Zainab B. Abdullah; Salam Waley Shneen
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4649

Abstract

The brushless direct current (BLDC) motor is characterized by high torque, which made it widely used in many industrial applications. To improve the working performance of the BLDC motor, the addition of controllers such as proportional integral derivative (PID) is adopted. To obtain high-performance controllers, the design process is adopted to develop a suitable algorithm. The genetic algorithm (GA) was chosen to tune the PID controller and get suitable parameters for each of kp, ki and kd with self-tuning. The design process is based on BLDC motor control using the GA to tune the traditional PID controller. Simulations were carried out for three cases including the absence of controllers secondly, by using the traditional control unit and finally with the GA. The integral time absolute error (ITAE) type error control standard for BLDC motor control system was selected. After conducting the simulation, the results demonstrated the superiority of the GA over the traditional ones in terms of response speed, (stability, rise, and settling) time and percentage overshootas details will be mentioned the model in the subsequent paragraphs of the research, finally the simulation results indicate the development and improvement of BLDC motor operation and performance during real time.
Wind power forecasting model based on linguistic fuzzy rules Mohammed Moujabbir; Khalid Bahani; Mohammed Ramdani; Hamza Ali-Ou-Salah
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4733

Abstract

The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.
Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset Md. Murad Hossin; F. M. Javed Mehedi Shamrat; Md Rifat Bhuiyan; Rabea Akter Hira; Tamim Khan; Shourav Molla
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4448

Abstract

According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is being utilized to validate the findings of this study. The comparison was made using the following performance metrics: accuracy, sensitivity, false omission rate, specificity, false discovery rate and area under curve. The LR method achieved a maximum accuracy of 99.12% among all eight algorithms and was compared to other comparable studies in the literature. The five features chosen are used to calculate the model's fidelity-to-interpretability ratio (FIR), which indicates how much interpretability was sacrificed for performance. The uniqueness of this work is the explainability approach taken in the model's performance, which aims to make the model's outputs more understandable and interpretable to healthcare experts.
Secure two-factor mutual authentication scheme using shared image in medical healthcare environment Husam A. Abdulmalik; Ali A. Yassin
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4459

Abstract

The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.

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