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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 70 Documents
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Search results for , issue "Vol 12, No 5: October 2023" : 70 Documents clear
IoT-based monitoring and shading faults detection for a PV water pumping system using deep learning approach Marwah Qasim Obaidi; Nabil Derbel
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4496

Abstract

One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods.
A novel data offloading scheme for QoS optimization in 5G based internet of medical things Saadya Fahad Jabbar; Nuha Sami Mohsin; Jamal Fadhil Tawfeq; Poh Soon JosephNg; Ahmed Lateef Khalaf
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5069

Abstract

The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of data transmission at the original targeted location. Initially, healthcare data was collected. Preprocessing is achieved by the normalization method. An EEEF data offloading scheme is proposed. A fruit fly optimization (FFO) technique is utilized. The performance metrics such as energy consumption, delay, resource utilization, scalability, and packet loss are analyzed and compared with existing techniques. The future scope will make use of a revolutionary optimization approach for IoMT.
Classification of gene expression dataset for type 1 diabetes using machine learning methods Noor AlRefaai; Sura Zaki AlRashid
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4322

Abstract

Type 1 diabetes (T1D) disease is considered one of the most prevalent chronic diseases in the world, it causes a high level of glucose in the human blood. Despite the seriousness of this disease, T1D may affect people and their condition develops to an advanced stage without feeling it, which makes it difficult to control the disease. Early prediction of the presence of this disease may significantly reduce its risks. There are many attempts to overcome this disease, some of them are heading towards biological solutions and others towards bioinformatic solutions. Several studies have used a single feature selection method with a machine learning (ML) model to predict or classify T1D. In this paper, ML techniques were used for classification, such as Naive Bayes (NB), support vector machine (SVM), and random forest (RF) using a T1D gene expression dataset that has a multiclass to classify the genes associated with this disease. The proposed model can identify the genes related to T1D with high efficiency, which helps a lot in predicting a person carrying the disease before symptoms appear. The highest accuracy of 89.1% was obtained when applying SVM with chi2 as the feature selection method.
ECG biometric in real-life settings: analysing different physiological conditions with wearable smart textiles shirts Muhammad Muizz Mohd Nawawi; Khairul Azami Sidek; Amelia Wong Azman
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5133

Abstract

The adoption of biomedical signals such as the electrocardiogram (ECG) for biometric is rising in tandem with the increased attention to wearable devices. However, despite its potential benefits, ECG is rarely implemented as a biometric mechanism in real-life wearable applications. Therefore, this research aims to analyse the ECG signals extracted from wearable Hexoskin Proshirt for biometric authentication in different physiological conditions. A total of 11 subjects participated in this study, where the ECG signals were recorded while standing, sitting, walking, and uncontrolled activity. The raw ECG signal is first pre-processed using noise-removal butterworth filters in the time domain, followed by an efficient QRS segmented feature extraction approach. Finally, around 854 datasets were generated for training and validation, while the remaining 300 were used to test the proposed recognition method with a quadratic support vector machine (QSVM). The results show that the proposed method achieved a reliable accuracy above 98% with false acceptance rate (FAR) of 0.93%, false rejection rate (FRR) of 3.64%, and true positive rate (TPR) above 96% on the in-house datasets. This researchs findings confirm the possibility of using ECG biometrics for authentication purposes in various real-life settings with varying physiological parameters using a smart textile shirt.
A clustering approach to improve VANETs performance Hayder Ayad Khudhair; Alaa Taima Albu-Salih; Mustafa Qahtan Alsudani; Hassan Falah Fakhruldeen
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5086

Abstract

Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV.
Optimal fractional SSSC auxiliary controller for power system low frequency oscillations damping improvement Khadidja Benayad; Tarik Zabaiou; Amar Bouafassa
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4952

Abstract

This work presents a new controller design to improve inter-area low frequency electromechanical oscillations (LFEOs) damping and enhance the overall stability of the electrical network. The control strategy used the static synchronous series compensator (SSSC) based flexible AC transmission systems (FACTS) series-type device mainly performed for power flow and voltage regulation. Then, a fractional order proportional integral derivative (FOPID) is introduced as an auxiliary controller for the SSSC using the difference of rotor speed deviations of generators as input signal to improve the oscillations damping. Moreover, genetic algorithm (GA) is applied to seek for optimum controller gains. The proposed control approach is examined on two-area four-machine (2A4M) test system. The FOPID performance is compared with the integer order proportional integral derivative (PID). Obtained results show that the proposed SSSC-based FOPID controller achieves high performance for enhancement of inter-area low frequency oscillations damping.
Design of an efficient convolutional buck-boost converter for hybrid bioinspired parameter tuning Chandini Mutta; Agam Das Goswami
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5463

Abstract

Power-electronic systems with voltage boosts use buck-boost converters. These converters suppress current and invert voltage to improve voltage swing. Power-electronic systems with voltage boosts use buck-boost converters that suppress current and invert voltage to improve voltage swings. Researchers propose many converter models, but their total harmonic distortion (THD) limits their scalability. Harmonics from additional current components increase THD. The model filters excessive currents using inductor-based storage, capacitive filters, and resistive circuits. However, these models are unstable, reducing their performance in large converter circuits. This text proposes a novel convolutional neural network (CNN) with a hybrid bioinspired model based on genetic algorithm (GA) and particle swarm optimization (PSO) to overcome this limitation. Estimating internal buck and boost parameters efficiently reduces reverse currents. These parameters include inductor current ripple, recommended inductance, internal switch current limit, and switching frequency. The model finds low-power, high-efficiency buck-boost configurations based on these values. Incremental learning operations tuned the GA model, which was applied to many buck-boost configurations. The proposed model had a 5.9% lower delay, 16.2% lower harmonics, and 4.6% better power efficiency than state-of-the-art buck-boost models.
Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller Mohammed Qasim Sulttan; Salam Waley Shneen; Jafaar Mohammed Daif Alkhasraji
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4870

Abstract

The multi-input multi-output (MIMO) technique is becoming grown and integrated into wireless wideband communication. MIMO techniques suffer from a large-scale linear dynamic problem, it will be easy to adjust the proportional-integral-derivative (PID) of a continuous system, unlike the nonlinear model. This work displays the tuning of the PID controller for MIMO systems utilizing a statistical grey wolf optimization (GWO) and evaluated by objective function as integral time absolute error (ITAE). The instantaneous adjusting characteristic GWO approach is the criterion that distinguishes such a combination-proposed strategy from that existing in the traditional PID approach. The GWO algorithm searching-based methodology is used to determine the adequate gain factors of the PID controller. The suggested approach guarantees stability as the initial scheme for a steady state condition. A combination of ITAE combined with the GWO reduction method is adopted to reduce the steady-state transient time responses between the higher-order initial scheme and the unit amplitude response. Simulation outcomes are illustrated using MATLAB software to show the capability of adopting the GWO scheme for PID controlling.
Data augmentation and enhancement for multimodal speech emotion recognition Jonathan Christian Setyono; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5031

Abstract

Humans’ fundamental need is interaction with each other such as using conversation or speech. Therefore, it is crucial to analyze speech using computer technology to determine emotions. The speech emotion recognition (SER) method detects emotions in speech by examining various aspects. SER is a supervised method to decide the emotion class in speech. This research proposed a multimodal SER model using one of the deep learning based enhancement techniques, which is the attention mechanism. Additionally, this research addresses the imbalanced dataset problem in the SER field using generative adversarial networks (GAN) as a data augmentation technique. The proposed model achieved an excellent evaluation performance of 0.96 or 96% for the proposed GAN configuration. This work showed that the GAN method in the multimodal SER model could enhance performance and create a balanced dataset.
Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer Hung Duc Nguyen; Ly Huu Pham
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5245

Abstract

In this paper, an economic load dispatch problem (ELD) is solved for reaching optimal power output of hybrid systems in addition to cost minimization. The systems consider the forbidden working zones (FWZs), dynamic load demand, wind farms, and solar photovoltaic fields (SPs). The cost minimization solutions for the ELD problem are found by applying the Dandelion optimizer (DO), the salp swarm algorithm (SSA), and the particle swarm optimization (PSO). In the study case, the power system consists of six thermal power plants (TPs), two wind farms, and two SPs. In addition, the variation of load demand over 24 hours of one day is applied. DO and SSA can achieve the best cost of $15443.0753 for the first system, but PSO cannot. However, DO is the most stable method reaching the standard deviation of 0.0184 for fifty runs but that of SSA and PSO is about 1.0439 and 8.9664. For the second system, DO can reach smaller cost than PSO by $11.17, $137.74 and $323.09 for the best, mean and worst solutions among fifty found solutions. As a result, DO is strongly recommended for solving the ELD problem.

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