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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 66 Documents
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Search results for , issue "Vol 11, No 6: December 2022" : 66 Documents clear
Design and implementation reversible multiplexer using quantum-dot cellular automata approach Noora H. Sherif; Mohammed Hussien Ali; Najim Abdallah Jazea
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4307

Abstract

Rapid progress in the field of nanotechnology includes using quantum dot-cellular automata (QCA) as a replacement for conventional transistor-based complementary metal oxide semiconductor (CMOS) circuits in the construction of nano-circuits. Due to ultra low thermal dissipation, rapid clocking, and extremely high density, the QCA is a rapidly growing field in the nanotechnological field to inhibit the field effect transistor (FET)-based circuit. This paper discusses and evaluates two multiplexer (MUX) architectures: an innovative and effective 4×1 MUX structure and an 8×1 MUX structures using QCA technology. The suggested architectural designs are constructed using the Fredkin and controlled-NOT (CNOT) gates. These constructions were designed to simulate using tool QCA designer 2.0.3. The 591 and 1,615 cells would be used by the 4×1 and 8×1 QCA MUX architectures, respectively. The simulation results demonstrate that, when compared to the previous QCA MUX structures, the suggested QCA MUX designs have the best clock latency performance and use of different gate types.
Metamaterial and metasurface based emitters for solar thermal photovoltaic applications: analytical review Lydia Anggraini; Hendra Jaya Tarigan; Joni Welman Simatupang
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3588

Abstract

The motivation behind this paper stemmed from the fact that the world consumes the fast-depleting fossil fuel energy. Before the fossil fuel runs out, new technologies to harvest energy from alternative sources are needed. Sunlight is clean, free and abundant. The market for solar thermal and photovoltaic electricity generation is expanding rapidly. Therefore, an analytical review on the types of emitter for solar thermal photovoltaic (STPV) applications utilizing metamaterials and metasurfaces is presented in this research study. STPV is still important in the development of an emitter technology. STPV classifications based on the types of materials, compositions, dimensions, geometries, and long-term temperature stability are considered. The ability to engineer STPV by controlling one or more of the foregoing physical parameters are useful for researchers. Different types of design and simulation tools are considered. The near future plans are to optimize the efficiency of the emitter and investigate how various layers and different combinations of metamaterials affect such an efficiency by employing a simulation tool such as finite-difference time-domain (FDTD, Lumerical).
Load frequency control of multi area system under deregulated environment using artificial gorilla troops optimization Sambugari Anil Kumar; M. Siva Sathya Narayana Varma; K. Jithendra Gowd
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4188

Abstract

The artificial gorilla troops method is utilized in the tilt integral derivative (TID) controller that is discussed in this paper in order to adorn the load frequency control (LFC) in the restructured thermal-hydal system. The controller is implemented in simulink. In this study, a mathematical definition of the social life of gorillas and innovative methods for exploring and exploiting gorilla habitats are presented. By applying step load perturbations and using integral square error as the evaluation method, the dynamic properties of the system can be determined. It is clear that the newly developed method of optimizing artificial gorilla troops performs better than the grey-wolf optimization technique (GTO). In this paper, the TID controller and the proportional integral derivative (PID) controller are contrasted with regard to a variety of optimization strategies inorder to compare different power transcations.
Unmanned aerial vehicles and machine learning for detecting objects in real time Mustafa Fahem Al Baghdadi; Mehdi Ebady Manaa
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4185

Abstract

An unmanned aerial vehicle (UAV) image recognition system in real-time is proposed in this study. To begin, the you only look once (YOLO) detector has been retrained to better recognize objects in UAV photographs. The trained YOLO detector makes a trade-off between speed and precision in object recognition and localization to account for four typical moving entities caught by UAVs (cars, buses, trucks, and people). An additional 1500 UAV photographs captured by the embedded UAV camera are fed into the YOLO, which uses those probabilities to estimate the bounding box for the entire image. When it comes to object detection, the YOLO competes with other deep-learning frameworks such as the faster region convolutional neural network. The proposed system is tested on a wild test set of 1500 UAV photographs with graphics processing unit GPU acceleration, proving that it can distinguish objects in UAV images effectively and consistently in real-time at a detection speed of 60 frames per second.
Common human diseases prediction using machine learning based on survey data Jabir Al Nahian; Abu Kaisar Mohammad Masum; Sheikh Abujar; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3405

Abstract

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.
Smarter dam based on cyber-physical system utilizing Raspberry Pi4 and NodeMCU ESP8266 Mustafa Yassin Deab; Muayad Sadik Croock
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4360

Abstract

The majority of dams still use traditional technologies to manage water gates. These strategies have a number of limitations, including those related to human error. This study describes an automated method for controlling and managing dams based on the structure of a cyber-physical system (CPS) in order to reduce work hazards and human efforts. The suggested system design consists of four ESP8266 nodes, each connected to a water level sensor and distributed around the main dam and its feeder sections. These nodes are linked to a central server (Raspberry Pi4). The data is gathered and retransmitted through the message queuing telemetry transfer (MQTT) protocol to the (Raspberry Pi4) central server via wireless sensor nodes (WSNs) distributed on the dam's various sides to regulate the water levels in the dam's main reservoir and the areas it feeds. Furthermore, the Raspberry Pi4 transmits data to the cloud server using internet media. A cloud-based dashboard with numerous tabs for each node has been constructed. The results of the experiments reveal that the proposed technique is superior to the ones currently in use for dam management.
Classifying healthy and infected Covid-19 cases by employing CT scan images Marwa Mawfaq Mohamedsheet Al-Hatab; Raid Rafi Omar Al-Nima; Maysaloon Abed Qasim
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4344

Abstract

A broad family of viruses called coronaviruses may infect people. The infection's symptoms are often relatively minor and resemble a normal cold. Since the coronavirus disease of 2019 (Covid-19) has never been observed in humans, anyone can contract it, and no one has an innate immunity to it. The detection of Covid-19 is now a critical task for medical practitioners. computed tomography (CT) scans can be considered as the best way to diagnose Covid-19. For patients with severe symptoms, imaging might help to assess the seriousness of the disease. Also, the CT scan can be helpful for determining a plan of care for a patient. This work focuses on classifying Covid-19 cases for healthy and infected by presenting a powerful scheme of recognizing CT scan images. In this study will be provided by proposing a model based on applying deep feature extractions with support vector machine (SVM). Big dataset of CT scan images is employed, it is available in the repository of GitHub and Kaggle. Remarkable result of 100% have been benchmarked as the highest evaluation after investigations. The proposed model can automatically detect between healthy and infected individuals.
A crypto-steganography scheme for IoT applications based on bit interchange and crypto-system Suray Alsamaraee; Ali Salem Ali
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4194

Abstract

Image steganography and cryptography have been used widely due to the dramatic evolution of the internet of things (IoT) and the simplicity of capturing and transferring digital images. Pressing challenges in the context of a steganography system include security, imperceptibility, and capacity issues. In the existing schemes, fixing one issue has been indicated to affect the other and vice versa. Based on the above challenges, a new scheme has been proposed for the Crypt-steganography scheme. The proposed scheme consists of three main contributions. The first contribution is hybrid additive cryptography (HAC), which is related to encrypting secret messages before the embedding process to ensure security. The HAC depends on ElGamal elliptic curve cryptosystem (ECC) with cubic Bézier curve to achieve text confidentiality. The second contribution is a bit interchange method (BIGM), which is related to the embedding process and solves the image's imperceptibility. The third contribution is a new image partitioning method (IPM). The IPM contribution increases the randomization of selecting the embedding pixels. The IPM proposes a random pixel selection based on three iterations of the Hénon Map function used with IPM. Different parameters are used to evaluate the proposed scheme. Based on the findings, the proposed scheme gives evidence to overcome existing challenges.
Smart grid application in the Iraqi power system: current and future challenges Saraa Ismaeel Khalel; Nagham Hikmat Aziz; Maha Abdulrhman Al-Flaiyeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4099

Abstract

A smart grid could generate and distribute electricity effectively economically, securely and sustainably. It offers customers more information and choice, including the export of energy to the grid, demand-side participation and energy efficiency. However, to implement a smart grid into the Iraqi power system, various challenges should be faced, especially concerns related to understanding the contents and features of this network compared with the traditional Iraqi network. As well as the challenges and risks of implementing the smart grid itself in the modern work environment, especially with the tremendous progress in communication technologies, which has brought serious problems to the operation of the network such as cyberattacks. Also, the traditional Iraqi network suffers from various problems, including the large deficit in the generated power-to-load demand ratio, which reaches about more than a third, and the great destruction that the network has been subjected to because of the wars that the country has been exposed to during the past three decades. In this study, a clear vision was presented to researchers and engineers who are interested in applying the smart grid in Iraq on this vital topic, which will greatly help in applying this essential matter to develop the work of the Iraqi power system and improve the efficiency and services it provides.
Transfer learning for detecting COVID-19 on x-ray using deep residual network Helmi Imaduddin; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4334

Abstract

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.

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