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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 2,901 Documents
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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.
Risk assessment in fleet management system using OCTAVE allegro Salman Alfarisi; Nico Surantha
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3241

Abstract

The purpose of this study is to use the OCTAVE allegro methodology to identify risks in fleet management system (FMS), determine prioritized risks to be mitigated, provide mitigation recommendations for these prioritized risks, and shows how effective the recommendation is. The result of this study is expected to become an input for FMS service provider of possible risks in FMS services, and risk mitigation approaches that can be used to handle those risks. This risk assessment has successfully identified 6 critical information assets, 10 risks in total, and 4 risks that need to be mitigated, followed by proposed mitigation approaches for those risks. Some of the recommendation has been applied by the company and contribute to SLA achievement of the system. The result also showed that application and simulation software provide most prominent risks in FMS service, thus securing these two will eliminate most risk in FMS service.
An improved deep bagging convolutional neural network classifier for efficient intrusion detection system Mathiyalagan Ramasamy; Pamela Vinitha Eric
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3252

Abstract

In the current trend, the network-based system has substantial jobs, and they have become the targets of attackers. When an intrusion occurs, the security of a computer system is compromised. As a result, we must seek out the best methods for ensuring frameworks. A crucial component of the security management architecture is the intrusion detection system (IDS). To maintain effective network security, the design and implementation of IDS remain an important assessment topic. For intrusion detection, the previous system created an enhanced relevance vector machine (ERVM) classifier. However, intrusion detection is not robust for large-scale intrusion datasets, resulting in a high attack rate. The suggested work developed an improved deep bagging based convolutional neural network (DBCNN) for intrusion detection to address this issue. Preprocessing, feature selection, and classification are three processes included in the proposed framework. The KDD dataset is preprocessed in this stage using the kalman filter method. The feature selection is then carried out using the inertia weight based dragonfly method (IWDA). Finally, the DBCNN classifier successfully identifies interruption assaults. The KDD dataset is used to test the new model. The test results show that the proposed work accomplishes better execution contrasted and the current framework as far as accuracy, precision, recall and f-measure.
Classification improvement of gene expression for bipolar disorder using weighted sparse logistic regression Abdulnasir Younus Ahmed; Mohammed Abdulrazaq Kahya; Suhaib Abduljabbar Altamir
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.v11i2.3594

Abstract

The computer-aided diagnosis system plays an important role in the classification of diseases and genes such as psychological or other diseases. Bipolar disorder (BD) is a commond psychological disease nowdys. Genes that describe this type of disease may include irrelative values to bipolar disorder disease. These values may adversely impact the classification performance. Logistic regression (LR) and recently sparse logistic regression (SLR) were used as a common technique to solve such binary classification problems. Gene selection has been applied to be a successful technique to get better classification output by excluding the irrelative values of genes. In this work we go further in improving the classification accuracy by restoring to incorporating the weight of these genes utilizing integrating the standardization of T-test with the sparse logistic regression, aiming to accomplish high classification accuracy. A bipolar dataset of gene expressions measured for 22283 genes using Affymetrix technology was used. Two performance indicators; classification accuracy, and geometric-mean of specificity and sensitivity are considered in evaluating the proposed method. Experimental results show an improvement over the two competitor methods; SLR-smoothly clipped absolute deviation (SCAD) and SLR-lasso in three indicators: classification accuracy, geo-means, and area under the curve. Therefore, our technique is beneficial to predict and classify BD psychopaths.
Design and simulation of dual-band rectangular microstrip patch array antenna for millimeter-wave Shahad Dhari Sateaa; Maysam Sameer Hussein; Zainab Ghazi Faisal; Amany Mohammad Abood; Huda Dhari Satea
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3336

Abstract

Microstrip array antennas are essentially for radar and communications systems. They are used to get a needed pattern that cannot be realized with a single element. This paper aims to design and simulate of rectangular microstrip patch array antenna 1 patch (1×1), 2 patches (1×2), and 4 patches (1×4) and improve the performance results. The proposed antenna is simulated by using electromagnetic simulation, computer software technology Microwave studio (CST) printed on Rogers RT5880 (lossy) substrate with dielectric constant 2.2, 0.0009 loss tangent, and thickness 0.1 mm. The simulation results show that the small patch antenna size (1.57 mm × 2 mm) for three designs works at dual bandwidth. The major target of this work is to accomplish an unusual directivity with improved gain for three antenna array designs.
Comparative characterization of microstrip patch antenna array with defected ground structure for biomedical application Suganthi Santhanam; Thiruvalar Selvan Palavesam
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3459

Abstract

In this paper, microstrip dual band antenna array with defected ground structure has been proposed for low frequency wearable on-body applications. The array has been simulated with Cotton, Polyimide, Polyester and Teflon (PTFE) flexible materials which are mostly used for biomedical applications. The performance of 2x2 trapezoidal patch array with partial ground, thin slot defection at center has been studied in terms of return loss, VSWR, power handling capability, radiation pattern and surface current distribution. All substrates exhibits acceptable fractional bandwidth below -10 dB and Cotton proves its superiority than other substrates with -20 dB and -48 dB return loss with 16.12% and 7.73% bandwidth at 1.24 GHz and 1.94 GHz respectively. The proper matching of array has been proved with VSWR value below 1.3 for all substrates. The observation of input port impedance shows that, Cotton array has good impedance matching nearly to 50 Ω and PTFE array has 100 Ω poor impedance matching. The proposed four flexible array exhibit omnidirectional pattern in H plane with 1.43 dBi gain and bidirectional pattern in E plane with 1.4 dBi gain which are support for ideal fitness of proposed flexible material array for medical applications.
An intelligent differential protection of power transformer based on artificial neural network Ali Nathim Hamoodi; Mohammed Ahmed Ibrahim; Bashar Muhammed Salih
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3547

Abstract

This paper describes the application of artificial neural network (ANN) techniques for protecting small power transformer 2 kVA (Terco type). ANN network trained according to the primary and secondary currents data under NN tool, this network performs as a function of differential protection relay. Symmetrical and unsymmetrical faults are analyzed using Matlab environment. ANN network senses the difference in the internal current of both transformer sides and sending a trip to the circuit breaker (CB) at moment of fault occurrence. All the voltages and the currents waveforms affected with the fault and the response time increased according to this technique. Finally, the trip signal and the quick disconnect time were improved according to ANN technique.
Implementation of FOC algorithm using FPGA for GaN-based three phase induction motor drive Tung Duong Do; Nam Duong Le; Vu Hoang Phuong; Nguyen Tung Lam
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.v11i2.3569

Abstract

Induction motor is widely used in industrial applications due to its low cost, simple design, and reliability. In this paper, the induction motor control structure FOC will be implemented on the FPGA platform for the drive system using GaN devices. By using GaN technology, the switching frequency can be up to 100 kHz instead of 2 to 20 kHz when using IGBT transistors. It leads to a significant reduction in switching loss as well as increasing the power density of the power electronic converter. The control structure will be programmed in VHDL language on the system-on-chip environment of Xilinx Zybo z7010 FPGA development board. The validity of the research is verified by some results when operating with HIL device.

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