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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 63 Documents
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Search results for , issue "Vol 11, No 2: April 2022" : 63 Documents clear
Learning algorithm of artificial neural network factor forecasting power consumption of users Tavarov Saijon Shiralievich; Sidorov Alexander Ivanovic; Shonazarova Shakhnoza Mamanazarovna; Sultonov Olamafruz Olimovich; Parviz Yunusov
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3172

Abstract

Seasonal fluctuations in electricity consumption, an uneven load of supply lines reduce not only the indicator of energy efficiency of networks but also contribute to a decrease in the service life of elements of power supply systems. Revealing the patterns of such fluctuations makes it possible to build models of power consumption, predict its dynamics, which in general will contribute to ensuring the energy efficiency of urban electrical networks and increasing the reliability of power supply systems. A computational, computer and neural network model is proposed that allows to increase the accuracy of the forecast of electricity consumption by household consumers. Based on the developed mathematical model, taking into account the obtained factor coefficients - ti, h, c, s, k for 2020 for 9 cities of the Republic of Tajikistan, monthly coefficients characterizing the terrain conditions (αi)  were calculated. The results obtained using the proposed method was compared with the results of a computer and neural network model.
Exploiting user grouping and energy harvesting in downlink cellular system Minh-Sang Van Nguyen; Phuc Huu Dang; Nhan Duc Nguyen
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2982

Abstract

A mobile communication system combining energy harvesting with a cooperative nonorthogonal multiple access (NOMA) system is presented in this research. In the proposed scheme, the relay is assumed to have a limited power source, and it will harvest radio energy from the base station (BS) to serve the near and far users. In this scenario, we consider two possible situations during information transmission in the NOMA application system: perfect successive interference cancellation (SIC) and imperfect successive interference cancellation. The system performance is assessed primarily based on closed-form outage probability expressions. Numerical simulations are conducted to examine the outage probability of the proposed scheme and to verify the derived formulas. The study results have proved that the system performance is still good under the imperfect SIC condition, and several optimal parameters to improve the system performance have been found. Moreover, our research results have shown the superior performance of the proposed model compared with current orthogonal multiple access (OMA) networks.
A new ranking approach for E-commerce websites based on fuzzy TOPSIS algorithm Houcine Belouaar; Okba Kazar; Meftah Zouai; Abdelhak Merizig
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3563

Abstract

With the gigantic growth of the E-commerce market, E-commerce websites are becoming more and more numerous. Customers of E-commerce websites are spoiled for choice and have encountered several problems in choosing not only the right products but also the E-commerce website from which they want to purchase the desired products. E-commerce websites ranking is recognized as a complex multi-criteria decision-making (MCDM) problem. In practice, clients of E-commerce websites generally have difficulty expressing their judgments in precise numbers because the criteria are some- times imprecise and sometimes uncertain and ambiguous. In this context, we propose to use fuzzy logic to allow clients to express their ratings in natural language and propose an approach based fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) for E-commerce websites ranking. A numerical experimentation was conducted for validate the effectiveness of the proposed approach.
A spectrum sensing approaches in cognitive radio network by using cloud computing environment Sabbar Insaif Jasim; Mustafa Mahmood Akawee; Raed Abdulkareem Hasan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3162

Abstract

A spectrum agreement has failed to meet the demands of new applications due to the fixed spectrum allocation (FSA) concept. But current efforts are targeted towards the utilization of cognitive radio as a way of addressing the issue of resources deficiency. The number of radio spectrum users keeps increasing daily owing to the advancement in technology in all aspects of life; even the licensed band users are currently demanding for extension of their radio spectrum and to balance the congestion in radio spectrum, some users may have to be placed on other bands. This article focused on voids detection (via spectrum sensing) in radio spectrum and secondary user assignment in cloud computing. Spectrum sensing was approached in two was in this study-underlay and interweave spectrum allocation. Both approaches are evaluated using certain performance metrics, such as throughput enhancement and queuing time minimization.
A mitigation technique for torque ripple in a brushless DC motor by controlled switching of small DC link capacitor Bogimi Sirisha; Laxminarayana Yalakanti
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3408

Abstract

High performance applications are now days utilizing the brushless DC motor (BLDC) drives due to its ruggedness, compactness, high torque to weight ratio, high dynamic response etc. the feature of square-wave current excitation waveforms in BLDC motor drives allows some major system simplifications for trapezoidal BLDC motors. The developed torque is constant in ideal conditions in this motor when its back emf waveform is of trapezoidal type. Despite this, due to the physical construction of the motor and its settings, torque ripple exists in the output torque and is an undesired phenomenon in the BLDC motor drives and are also linked to the motor's control and driver sides. This paper provides a new way for reducing torque ripple and is simple, compact and cost effective. To prove the correctness of the compensation technique, circuit is modelled and simulations are carried to examine the theoretical performance of the BLDC motor drive. Experiments on a prototype drive were conducted to further validate the theoretical analysis as well as the utility of the proposed technique.
Comparison of transfer learning method for COVID-19 detection using convolution neural network Helmi Imaduddin; Fiddin Yusfida Ala; Azizah Fatmawati; Brian Aditya Hermansyah
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3525

Abstract

Currently, one of the most dangerous diseases is Coronavirus disease 2019 (COVID-19). COVID-19 is a threat to the whole world, and almost all countries are experiencing the COVID-19 pandemic, including Indonesia. Various ways to detect COVID-19 sufferers have been carried out, such as swab tests, rapid tests, and antigens. One way that can be done to detect COVID-19 infection is to look at X-ray images of the patient's lungs because someone infected with COVID-19 has a different lung shape from normal people. Many studies have been carried out to detect COVID-19, using either machine learning (ML) or deep learning (DL). In this study, we propose to use transfer learning as an extraction feature in the classification of the covid dataset. The study was conducted four times using four different methods, namely ResNet 50, MobileNet V2, Inception V3, and DensNet-201. After experimenting, we compared the results to find out which method has the best results in detecting COVID-19. From this research, it was found that the ResNet 50 model has the best results with 92.3% accuracy, 93% precision, 93% F1-Score, 99% sensitivity, and 90.7% specificity.
Forecasting epidemic diseases with Arabic Twitter data and WHO reports using machine learning techniques Qanita Bani Baker; Farah Shatnawi; Saif Rawashdeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3447

Abstract

Twitter is one of the essential social media tools used by many people because they express their views, daily problems, and what they suffer from the health aspects. On Twitter, we can detect and track the spread of the most serious diseases like flu; by analyzing people's tweets and collecting reports from health organizations. In this paper, the data from Twitter was collected in the Arabic language related to the spread of influenza using many Arabic keywords. Then, we applied several machine learning algorithms, which are random forest, multinomial naïve bayes, decision tree, and voting classifier. We also found the correlation between the collected tweets and the reports collected from the World Health Organization (WHO) website according to three experiments. These experiments are: i) between the tweets and reports based on the 13 countries regardless of the time, ii) between the tweets and reports based on the Arab regions that depend on these countries' dialects irrespective of the time, iii) between all tweets and all reports based on the week number. The results from these experiments show that there is a strong correlation between the tweets and the reports, which means that the tweets and the WHO reports can together detect the flu outbreaks in the Arab world.
Important factors to remember when constructing a cross-site scripting prevention mechanism Md. Maruf Hassan; Badlishah R. Ahmad; Ashrafia Esha; Rafika Risha; Mohammad S. Hasan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3557

Abstract

Web application has become an essential part of daily activities to provide easy accessibility that ensures better performance. It is a platform where sensitive information such as username, password, credit card details, operating system and software version. is stored that attracts intruders to generate most of their attacks. Intruders can steal valuable data by compromising web application security flaws; cross site scripting (XSS) vulnerability is one of these. Several studies have been conducted in order to prevent the XSS vulnerability. In this research, we searched Scopus Indexed articles published in the last 11 years (between 2008 and 2020) using two keywords (“XSS attack prevention” and “XSS prevention”). The purpose of this study was to conduct a literature review on XSS prevention techniques e.g., strengths and weaknesses, including structural issues and real-time deployment location in order to extract valuable information. This review identified 14 articles among the 25 selected articles that provided various suitable prevention techniques for XSS attacks. Seven articles are based on tools that have been implemented and take into account design, coding, testing, and integrating validation processes, six articles are about server site solutions, and one is about automatic mitigation solutions. As a result, this research will be invaluable in guiding the advancement of XSS prevention techniques.
Ergodic capacity of internet of things’ devices in presence of channel state information imperfection Dinh-Thuan Do; Anh-Tu Le
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3138

Abstract

Non-orthogonal multiple access (NOMA) is deployed to improve spectral efficiency for applications in fifth generation networks. NOMA system splits power domain to many parts to further serve massive users by relaxing the orthogonal use of radioresources. In this paper, a relay is required to help the source communicate with destinations with a fixed power allocation scheme. We derive expressions to highlight ergodic performance of two users the deployment of NOMA is suitable to different rate requirements from destinations (e.g., a cellular users have different requirements compared with internet of things devices). By conducting Monte-Carlo simulations, we find main system parameters which have crucial impacts on ergodic capacity. This paper is different other recent studies since we emphasize on imperfect channel state information (CSI) and Rician fading model for our analytical results.
Scalable epidemic message passing interface fault tolerance Soma Sekhar Kolisetty; Battula Srinivasa Rao
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3374

Abstract

Resilience and fault tolerance are challenging tasks in the field of high performance computing (HPC) and extreme scale systems. Components fail more often in such systems, results in application abort. Adopting fault–tolerance techniques can be consistently detect failures and continue application’s execution even if the failures exist. A prominent parallel programming specification, message passing interface (MPI), as it would be used to implement failure detection and consensus algorithm in this paper. Although the MPI does not facilitate fault tolerant behavior, this work presents a fault tolerant, matrix based failure detection and consensus algorithm. The proposed algorithm uses Gossiping. To detect failures, randomised pinging will be applied during the execution of the algorithm by using piggybacked gossip messages. In order to achieve consensus on the failures in the system, failed processes’ information will be sent using the same piggybacked gossip messages to all the alive processes. The algorithm was implemented in MPI framework and is completely fault tolerant. The results exhibit all the MPI process failures were detected using randomised pinging and global consensus has achieved on failed MPI process in the system.

Page 4 of 7 | Total Record : 63

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