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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 28, No 3: December 2022" : 65 Documents clear
A new logic circuits optimization algorithm using bipartite graph Oday Ahmed Al-Ghanimi; Hussein K. Khafaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1621-1632

Abstract

Designing a logic circuit from the scratch requires its description in logical expression, (e.g. sum of products), and then the expression should be optimized to diminish the cost and complexity of the circuit by reducing the number of literals, the number of logical terms, and/or logical operations. Karnaugh map, K-Map, is the most popular method in the optimization process, but it suffers from many drawbacks such as its inefficiency or the inability to be used in minimizing logical expression containing more than four literals, in addition to the complexity of implementing it as a program. In this paper, we propose a new algorithm to optimize the logic circuits depending on the bipartite graph and some of the suggested mathematical operations. The proposed algorithm is simple for programming implementation, literal-unlimited number, and is easy to be visualized and understandable. Many of the logic circuits of 3, 4, 5, and 6 literals were optimized and the results were correctly matched with the results of the Karnaugh map. Also, tens of logic circuits of more than 6 literals are optimized and the results were correctly checked with their truth tables and Logic-Friday tool.
Reconfigurable data encoding schemes for on-chip interconnect power reduction in deep submicron technology Vennapusapalli Shavali; Sreeramareddy Gorlagummanahally Maripareddy; Patil Ramana Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1330-1344

Abstract

With technology scaling, size of both transistor and interconnects are reduced. Power dissipation due to dynamic switching is high in the interconnects. Suitable encoding schemes that reduces transition between data bits are used to minimize interconnect power dissipation. In this paper transition between data bits is minimized based on three novel data encoding schemes identifying the novel methods estimates bit transitions in a pair of data bits and performs half inversion or full inversion on one byte of data thus reducing the switching activity by 50%. The encoder and decoder for the three encoding schemes are modelled in verilog hardware description language (HDL) and implemented using application specefic integrates circuit (ASIC) flow targeting 32 nm. Technology over all power dissipation of encoding scheme is 1.04 μW in addition over head area of 210 cells with encoding delay of 340 ps. Encoder decoder register transfer logic (RTL) code is implemented and the total area required is 34980 units. The data encoding and decoding schemes are suitable for low power applications.
Analysis of the current state of deepfake techniques-creation and detection methods Ashraf A. Abu-Ein; Obaida M. Al-Hazaimeh; Alaa M. Dawood; Andraws I. Swidan
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1659-1667

Abstract

Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algorithms and datasets used to build deepfakes, as well as the approaches presented to detect deepfakes to date. By reviewing the background of deepfakes methods, this paper provides a complete overview of deepfake approaches and promotes the creation of new and more robust strategies to deal with the increasingly complex deepfakes.
New methods for proportional-integral controller design for time-delay systems Aye Taiwo Ajiboye; Jayeola Femi Opadiji; Abdulrahman Olalekan Yusuf; Olusogo Joshua Popoola; Esther Toyin Olawole; Olalekan Femi Adebayo
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1437-1450

Abstract

The development of structured methods for proportional-integral (PI) controller design for systems with time delay are proposed in this article. Several PI controller design methods for time-delay systems have been reported. However, combining two or more methods to form new ones have not been given serious attention. The system stability region in the controller parameters space was determined by plotting the stability boundaries. In this study, the controller gains were first obtained using genetic algorithm (GA), weighted geometric center (WGC), and centroid of convex stability region (CCSR). Thereafter, these gains were combined by finding the centroids of lines joining any of the two gain locations, and triangle whose vertices are the location of the three gains in the convex stability region, thus yielding four additional methods, M1, M2, M3, and M4. Compared to a particular existing method, some of the proposed methods yield faster response speed at the expense of reference input tracking, while the reverse is the case for others. Any of the proposed methods (M1, M2, M3, and M4) can be selected depending on the system performance specifications.
Smart technologies of the risk-management and decision-making systems in a fuzzy data environment Yesmagambetova Marzhan; Keribayeva Talshyn; Koshekov Kairat; Belginova Saule; Alibekkyzy Karlygash; Ospanov Yerbol
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1463-1474

Abstract

The purpose of this article is to provide a methodology for calculating and predicting the quality of solution implementation in complicated multi-parametric organizational and technological challenges with control agent uncertainty. The article's study findings are centered on the practical application of formal methods in predicting the outcomes of control and decision-making risks under the uncertainty of model agents. The proposed mathematics and simulation applications use a multi-agent strategy to handle the general problem of assessing quality control based on "producer risk (project customer)" and "user risk." Computer experiments with simultaneous graphical visualization of the results improve the accuracy of mathematical modeling, increasing the study's effectiveness. Under the uncertainty of system agents, a simulation model has been designed to analyze and anticipate the dependability of control and the hazards of decision-making. The suggested model is unique in that it takes into account the statistical nature of normative values as well as the rules of equal probability. To handle a frequent problem, the proposed system technique employs a dual approach. It accomplishes this by assessing the quality of the control process based on the magnitude of the risks in the decision-making system.
A new approach for road extraction using data augmentation and semantic segmentation Kawther Ould Babaali; Ehlem Zigh; Mohamed Djebbouri; Oussama Chergui
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1493-1501

Abstract

Accurate road extraction from remote sensing images is a challenging task. Several methods of extraction have been developed but the precision of extraction is still limited for the unpaved and small-width roads. This paper proposes an accurate road extraction approach called DAA-SSEG since it uses data augmentation architecture (DAA) and semantic segmentation model (SSEG). The proposed approach DAA-SSEG is based on a modified full convolutional neural network that overcomes the vanishing gradient and the training saturation issues. It recognizes roads at the pixel level. Furthermore, The DAA-SSEG approach uses a new plan of data augmentation based on geometric transformation and images refinement techniques. It allows getting a richer dataset thus better training and an accurate extraction. The experiment denotes that the proposed approach DAA-SSEG, that combine data augmentation architecture and semantic segmentation method, outperforms some state-of-the-art methods in terms of F-measures. The results demonstrate that it ensures accurate extraction of unpaved and small-width roads, in urban and rural areas. Moreover, the proposed approach distinguishes between roads and trails and can extract some roads not labeled beforehand.
Reactive power control and performance analysis of doubly fed induction generatorin micro grid Syed Sarfaraz Nawaz; Sandipam Tara Kalyani
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1214-1226

Abstract

For both financial and environmental considerations, the power system includes a large number of solar and wind generating plants. In reality, wind energy has always been used using a doubly fed induction generator (DFIG) based variable speed wind turbine. This study examines the effectiveness of indirect control of a doubly fed induction generator for closed loop reactive power adjustment. A wind energy conversion system with continuous grid power's design, analysis, and MATLAB simulation are also covered. For DFIG to work reliably and be controlled to ensure stability for the power system, a seamless transition mode change is required. The horizontal axis wind turbine technology provides the necessary reactive power into the grid under all unexpected circumstances. The concept of DFIG mathematical modelling is covered. Various simulated outputs at loading circumstances are shown, along with separate control of active and reactive powers and variations in prime mover speed and excitation. This study examines the performance enhancement of DFIG using its grid-based proportional integral (PI), proportional integral derivative (PID), and fractional order proportional integral derivative (FOPID) controllers. Based on the thorough simulation findings, the type of control system that gives the efficient performance of DFIG in grid is ultimately decided. These simulation results demonstrate how the suggested controllers outperform the current controllers in terms of improving system performance.
Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency Ezzitouni Jarmouni; Ahmed Mouhsen; Mohamed Lamhamedi; Hicham Ouldzira; Ilias En-naoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1276-1285

Abstract

Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.
Comparison of the efficiency of machine learning algorithms for phishing detection from uniform resource locator Ahana Nandi Tultul; Romana Afroz; Md Alomgir Hossain
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1640-1648

Abstract

We are using cyberspace for completing our daily life activities because of the growth of Internet. Attackers use some approachs, such as phishing, with the use of false websites to collect personal information of users. Although, software companies launch products to prevent phishing attacks, identifying a webpage as legitimate or phishing, is a very defficult and these products cannot protect from attacks. In this paper, an anti-phishing system has been introduced that can extract feature from website’s URL as instant basis and use four classification algorithms named as K-Nearest neighbor, decision tree, support vector machine, random forest on these features. According to the comparison of the experimental results from these algorithms, random forest algorithm with the selected features gives the highest performance with the 95.67% accuracy rate. Then we have used one deep learning algorithm as enhanced of our experiment named as deep neural decision forests which have given performance with the 92.67% accuracy rate. Then we have created a system which can extract the features from raw URL and pass the features to our deep neural decision forest trained model and can classify the URL as Phishing or legitimate.
Islamic events reminder system via short message service notifications alert Maha Ibrahim Khaleel; Anwar Hamza Bresam
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1649-1658

Abstract

For persons working in the field of modern technology, the lack of awareness about Islamic events remains a significant obstacle. One of the numerous reasons why the event gets forgotten on its designated date. This prompted the creation of an automatic reminder system with mobile technology integration. This paper has the purpose of assisting people in remembering their daily Islamic events, as well as serving as a model for informing people of Islamic occasions via short message service (SMS) notifications. As a result, the main goal is to develop an Islamic model that uses SMS to inform people. To develop a free system based on the concept of recalling the most significant events in Muslim history in order to keep people informed. (Microsoft Visual Studio.net and Microsoft SQL Server Management Studio Express) as our main database. The text message reminder system is made up of two parts: an SMS application for automatic text messaging and a web-based application for customer registration and automatic reminder scheduling. The automated method delivered 100% of the SMS messages to the participants throughout the pilot testing. Finally, the system displayed a notice indicating that the text messages were successfully despatched, and the application was confirmed to be functional.

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