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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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Controlling matrix converter in flywheel energy storage system using AFPM by processor-in-the-loop method Nguyen Hung Do; Sy Manh Ho; Quoc Tuan Le; Tung Hoang; Trong Minh Tran; Phuong Vu
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3813

Abstract

Flywheel energy storage systems are considered as the grid integration of renewable energy sources due to their inherent advantages such as fast response, long cycle life and flexibility in providing auxiliary services to the grid, such as frequency regulation, and voltage support. The article focuses on the design of the controller, then conducts processor-in-the-loop (PIL) simulation of the dynamics part using the axial flux permanent magnet (AFPM) motor with the matrix converter on MATLAB/Simulink combined with the controller on the TMS32F28377S microcontroller card of Texas Instruments to evaluate the efficiency of the energy storage system.
Performance analysis of demand forecasting in energy consumption based on ensemble model Dhanalakshmi Jaganathan; Ayyanathan Natarajan
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3649

Abstract

Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%.
Assessing factors influencing internet banking adoption by using rasch model measurement Khairi Azhar Aziz; Marzanah A. Jabar; Salfarina Abdullah; Rozi Nor Haizan Nor
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3761

Abstract

The purpose of this paper is to use the Rasch model software in analyzing the identified influencing factors of internet banking towards improving adoption usage and to find the validity and reliability of the instrument. An initial conceptual model was developed based on the previous of Literature Review. About 17 factors have been identified from the previous models and frameworks and they have been improved as influencing factors. Those factors are the website design (WD), the ease of use (EOU), the security, the quality system, the social influence, the trust, the electronic word of mouth (eWOM), the rewards, the perceived usefulness (PU), the perceived ease of use (PEOU), the intention to use (IU), the sustainability, the commitment, the user experience/generation, the knowledge, the profession, and the income. The questionnaire survey was successfully conducted with 51 respondents and pilot data verification of 30 respondents. The results showed that person reliability was 0.93 spread of person respondent was 54.06 and person separation was 3.66 However, item reliability result was 0.88, spread of item was 45.98 and item separation equal to 2.70 was fair. This paper was proven to be a significant contribution to the validity and reliability of using the Rasch model.
Empowering secure transmission for downlink of multiple access system relying non-orthogonal signal multiplexing Dinh-Thuan Do; Minh-Sang Van Nguyen
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3774

Abstract

The growth of internet-of-things (IoT) inspired use cases in different run of the mill environments such as cities, industries, healthcare, agriculture, and transportation, has led to a greater desire for safer IoT data gathering and storage. However, securing IoT is challenging due to form-factor, complexity, energy, and connectivity limitations. Conventional coding-based security techniques are unsuitable for ultra-reliable low-latency and energy-efficient communication in IoT. Numerous research studies on physical layer security (PLS) techniques for fifth generation (5G) have emerged recently, but not all of the solutions can be used in IoT networks due to complexity limitations. Non-orthogonal multiple access (NOMA) is billed as a possible technology to solve connectivity and latency requirements in IoT. In this study, we exploit the power allocation characteristics of NOMA to enhance security in a downlink deviceto-device (D2D) decode and forward (DF) IoT network infiltrated by an eavesdropper. Our performance metric of choice is the secrecy outage probability (SOP). We formulate exact SOP results for different users. Simulation results demonstrate the positive impact of NOMA on SOP in a D2D IoT-NOMA network.
Chaotic based multimedia encryption: a survey for network and internet security Obaida M. Al-Hazaimeh; Ashraf A. Abu-Ein; Malek M. Al-Nawashi; Nasr Y. Gharaibeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3520

Abstract

Nowadays the security of multimedia data storage and transfer is becoming a major concern. The traditional encryption methods such as DES, AES, 3-DES, and RSA cannot be utilized for multimedia data encryption since multimedia data include an enormous quantity of redundant data, a very large size, and a high correlation of data elements. Chaos-based approaches have the necessary characteristics for dynamic multimedia data encryption. In the context of dynamical systems, chaos is extremely dependent on the initial conditions, non-convergence, non-periodicity, and exhibits a semblance of randomness. Randomness created from completely deterministic systems is a particularly appealing quality in the field of cryptography and information security. Since its inception in the early '90s, chaotic cryptography has seen a number of noteworthy changes. Throughout these years, several scientific breakthroughs have been made. This paper will give an overview of chaos-based cryptography and its most recent advances.
Distributed denial of service attacks detection for software defined networks based on evolutionary decision tree model Hasan Kamel; Mahmood Zaki Abdullah
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3835

Abstract

The software defined networks (SDN) system has modern techniques in networking, it separates the forwarding plane from the control plane and works to collect control functions in a central unit (controller), and this separation process leads to many advantages, such as cost reduction and programming ability. Concurrently, because of its centralized architecture, it is prone to a variety of attacks. Distributed denial of service (DDoS) attack has a significant impact on SDN, it is characterized by its ability to consume network resources as well as its ability to turn off the entire network. The work in this study aims to improve and increase the security and robustness of SDN systems against the attack or intrusion, by using a machine learning model to detect attack traffic and classify traffic of SDN as (attack or normal), and optimization algorithm (genetic algorithm) for improving the accuracy of the classification. After preparing and preprocessing the dataset, we used the genetic algorithm (GA) to optimize the hyperparameters of the decision tree (DT) model, and the proposed evolutionary decision tree (EDT) model was used to classify traffic into normal and attack traffic. The results indicate that the suggested model achieved a high classification accuracy of 99.46.
Even-odd crossover: a new crossover operator for improving the accuracy of students’ performance prediction Somia A. Shams; Asmaa Hekal Omar; Abeer S. Desuky; Mohammad T. Abou-Kreisha; Gaber A. Elsharawy
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3841

Abstract

Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to state-of-the-art resampling techniques.
Characterization of a compact low cost 6.5kV Cockcroft voltage multiplier Madhu Palati; Prashanth Narayanappa Ananda
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3809

Abstract

Generation of high voltages is often necessary in Industrial, Medical, civilian and defense applications. One of the popular methods of generation of high voltage DC is using a Cockcroft voltage multiplier generator. Knowledge on characterization of the voltage multiplier circuit helps the designer to study the effect of various input parameters on output, saves lot of time and money. In this paper various methods of generation of high voltages, advantages and disadvantages of each method are discussed. Design of five stage voltage multiplier circuit, fabrication and characterization of the 6.5kV voltage multiplier generator are presented. Simulation was carried using PSPICE software under different load conditions. Effect on the output voltage, ripple voltage with different values of load, frequency was studied. Experiments were carried out on the proposed prototype model and validated by comparing the values obtained from experimentation with the simulation and theoretical values
Fast and accurate classifying model for denial-of-service attacks by using machine learning Mohammed Ibrahim Kareem; Mahdi Nsaif Jasim
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3688

Abstract

A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.
Design and realization of low-cost solenoid valve remotely controlled, application in irrigation network Abdelhamid Benbatouche; Boufeldja Kadri
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.4123

Abstract

The remote and automated irrigation system of farmlands can avoid and minimize the waste of water and energy resources. This can be done with the remote-control of the solenoid-valves. A new solenoid-valve was designed and built from a simple valve with a motor and switches. The remote and automated irrigation system can monitor and receive requests via short message service (SMS) or web interface for controlling pump or solenoid-valves connected to the system. After each operation performed by the system, users receive notifications via SMS messages that contain the real-time status of the solenoid-valves controlled or temperature and humidity value. This system was created using Raspberry-Pi as the system control center. It has been connected to several sensors, and raspicam is used to take photo or video capture in real-time after the users’ request, and the global system mobile (GSM) module is a communication interface used to receive requests for controlling the irrigation system or to send notifications to users. A website is also developed for consultation and control of all that it contains in the system remotely. The result of this research aims to build a secure remote and automated irrigation system including low-cost solenoid-valve with Raspberry-Pi based on control and notification via SMS and web-page.

Page 146 of 291 | Total Record : 2901

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