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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 72 Documents
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Search results for , issue "Vol 12, No 6: December 2023" : 72 Documents clear
The novel strategy of electrical arc furnace design and control approach for voltage flicker investigation Thakre, Mohan P.; C. Tapre, Pawan; P. Kadam, Deepak; Sharma, Mousam; Somnath Kadlag, Sunil; Vilas Mahadik, Yogesh
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4880

Abstract

Voltage flickers and harmonics are power quality (PQ) problems in the electric system during a variation and arc furnace (AF) adaptability. AF creations must determine a harmonic and flicker. This article evaluates complex AF systems. This article presents a newly established time domain (TD) static become to on an AF's V-I attributes (VIA). Static arc configurations are useful for harmonic analyses, but dynamic methods are needed for PQ studies, especially voltage flicker analysis. The MATLAB-based dynamic AF configuration is simulated for four different configurations. A response with configurations 1 and 4 varies from the real AF outcomes. The  simulation results and numerical finding shows that configurations 2 and 3 are much more appropriate and produce better results for minimum 3rd harmonics for arc current, arc voltage, and point of common coupling (PCC) voltage. The novelty of this configuration is that the energy transferred to the load by the AF during the cycle of operation has been identified, making the developed scheme more reliable and dependent on the load's operational conditions. After that, effective applications of these configurations and other configurations' accuracy should be clarified.
Clustering of students admission data using k-means, hierarchical, and DBSCAN algorithms Cahapin, Erwin Lanceta; Malabag, Beverly Ambagan; Santiago Jr., Cereneo Sailog; Reyes, Jocelyn L.; Legaspi, Gemma S.; Adrales, Karl Louise
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4849

Abstract

Admissions in the university undergo procedures and requirements before a student can be officially enrolled. The senior high school grades remain the most significant in college admission decisions. This paper presents the use of data mining to cluster students based on admission datasets. The admission dataset for 2019-2020 was obtained from the office of student affairs and services. This dataset contains 2,114 observations with 11 attributes. Data preparation and data standardization were performed to ensure that the dataset is ready for processing and implemented in R programming language. The optimal number of clusters (k) was identified using the silhouette method. This method gave an optimal number of k=2 which was used in the actual clustering using the k-means and hierarchical clustering algorithms. Both algorithms were able to cluster students into two: cluster 1-social sciences or board courses and cluster 2-management or non-board courses. Further, density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was also used on the same dataset and it yielded a single cluster. This study can be replicated by using at least a 5-year dataset of students’ admission data employing other algorithms that would suggest students’ retention and turn over to board examinations.
Investigating the effectiveness of deep learning approaches for deep fake detection Berrahal, Mohammed; Boukabous, Mohammed; Yandouzi, Mimoun; Grari, Mounir; Idrissi, Idriss
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6221

Abstract

As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%.
Develop a new handling method for selfish nodes in mobile ad-hoc networks Al-Shakarchi, Sanaa Jafaar Hassan; Alubady, Raaid
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4895

Abstract

Mobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods.
Survey on cryptocurrency security attacks and detection mechanisms Almamoori, Amenah Abdulabbas; Bhaya, Wesam S.
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4434

Abstract

Cryptocurrencies have become extremely popular as a form of payment in recent years. They are supported by blockchain, a cutting-edge advanced technology that makes extensive use of cryptographic mechanisms and other sophisticated distributed computing techniques. On these grounds, cryptocurrencies have been a target of several attacks. Cyber-attacks, for example, are exogenous events that can robustly affect cryptocurrencies by influencing their stabilization of price and market valuation. This study describes an overview of cybercriminals’ activities on cryptocurrencies. It provides a detailed discussion on the most popular types of attacks on the cryptocurrency ecosystem. Moreover, it provides possible countermeasures to these attacks. Finally, it produces insights into the most impactful attacks on cryptocurrencies and the best methods that have been proposed for detecting cryptocurrency attacks. The main goal of this survey is to obtain a thorough understanding of cryptocurrency attacks, which have been the subject of major studies concerning financial risks on cryptocurrency. A large number of existing publications have reviewed and assessed various forms of attacks to achieve this goal. However, these works have considerably flawed. To the best of our knowledge, the present survey sheds light on future research directions.
Tel-MPLS: a new method for maximizing the utilization of IP telephony over MPLS networks M. Abualhaj, Mosleh; M. Al-Zyoud, Mahran; Abu-Shareha, Ahmad Adel; O. Hiari, Mohammad; Y. Shambour, Qusai
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6117

Abstract

Currently, the multiprotocol label switching (MPLS) standard is extremely prevalent. By exploiting the features provided by MPLS technology, a range of services, including IP telephony, have enhanced their overall performance. However, due to the size of the packet header, the IP telephony service consumes a significant portion of the MPLS network's available bandwidth. For instance, in IP telephony over MPLS networks, the packet header might account for as much as 80% of lost time and bandwidth. Designers working on IP telephony are making substantial efforts to address this issue. This study contributes to current efforts by proposing a novel approach called Tel-MPLS, which involves IP telephony over MPLS. TelMPLS approach uses the superfluous fields in the IP telephony packet's header to retain the packet data, therefore lowering or zeroing the IP telephony packet's payload. Tel-MPLS is an approach that significantly reduces the bandwidth of IP telephony MPLS networks. According to the findings, the Tel-MPLS approach is capable of reducing the amount of bandwidth that is lost by 12% when using the G.729 codec.
Long range technology for internet of things: review, challenges, and future directions A. Al-Shareeda, Mahmood; Abdullah Alsadhan, Abeer; H. Qasim, Hamzah; Manickam, Selvakumar
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5214

Abstract

New networking issues are presented by the increasing need for a wide variety of applications, which has spurred the creation of a new internet of things (IoT) paradigm, such as long range (LoRa). The LoRa protocol uses a patented kind of spread spectrum modulation to provide low-power, long-range communication. In this paper, we provide a comprehensive review of LoRa-IoT in terms of IoT applications, LoRa class, security and privacy requirements, and the evolution of LoRa technology. This review analysis and compares long range wide area network (LoRaWAN) to wireless technology (e.g., Bluetooth, LoRa, 5G, Sigfox, long term evolution-M (LTE-M), Wi-Fi, Z-wave, Zigbee) and provides a list of environment simulators (e.g., OMNeT++, MATLAB, ns-3, SimPy) to carry out experiment for LoRa-IoT. Finally, this review does not only review literature recently studied for LoRa-IoT but also discusses challenges and future directions.
Autonomous and smart cleaning mobile robot system to improve the maintenance efficiency of solar photovoltaic array Megantoro, Prisma; Abror, Abdul; Syahbani, Muhammad Akbar; Anugrah, Antik Widi; Perkasa, Sigit Dani; Setiadi, Herlambang; Awalin, Lilik Jamilatul; Vigneshwaran, Pandi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5950

Abstract

A solar photovoltaic (PV) array is part of a PV power plant as a generation unit. PV array that are usually placed on top of buildings or the ground will be very susceptible to dirt and dust. Thus, this dirt and dust will be able to reduce the performance and work efficiency of the generation unit. Cleaning PV arrays by manpower requires high effort, cost, and risk, especially in higher location. This study presents the design of a mobile robot that is used to replace human labor to clean PV arrays. That way, the PV array maintenance steps can reduce operational costs and risks. This intelligent controlled mobile robot can maneuver safely and efficiently over PV arrays. gyroscope and proximity sensors are used to detect and follow the sweep path over the entire PV array area. Proportional integral derivative (PID) control test makes the robot can stabilize in about 5.72 seconds to keep on the track. The smart PV cleaning robot has average operation time about 13 minutes in autonomous mode and 20-24 minutes in manual mode. The operation of the robot is effective to give more efficiency on the use of energy, time, and maintenance costs of PV array system.
Comparative study of moisture treatment techniques for mineral insulating oil Sutan Chairul, Imran; Ab Ghani, Sharin; Abu Bakar, Norazhar; Shahril Ahmad Khiar, Mohd; Hidayah Rahim, Nor; Nazri Mohamad Din, Mohamad
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5927

Abstract

The presence of moisture is one of the factors that promote degradation of transformer insulating oils and deterioration of cellulose insulation materials in oil-immersed power transformers, which affect the lifespan of the transformers. Realizing the importance of moisture in transformer insulating oils, this study compares the effectiveness of three moisture treatment techniques nitrogen bubbling technique (NBT), molecular sieve technique (MST), and vacuum oven technique (VOT)) for mineral oil (MO). The moisture content and AC breakdown voltage of the MO samples before and after moisture treatment were measured using Karl Fischer coulometric titrator and portable oil tester, respectively, in accordance with the American Society for Testing and Materials (ASTM) D1533 and ASTM D1816 standards. The results showed that NBT is the best moisture treatment technique for the MO, where the NBT reduced 80.79% of moisture present in the oil, followed by MST and VOT, which reduced 72.87 and 42.28% of moisture, respectively. The results also showed that the AC breakdown voltage of the MO samples after moisture treatment was improved owing to the reduction in moisture content.
Enhancement of medical images diagnosis using fuzzy convolutional neural network Mahdi, Huda Ali; Shujaa, Mohamed Ibrahim; Zghair, Entidhar Mhawes
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5729

Abstract

Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.

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