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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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Internet of things based agricultural drought detection system: case study Southern Somalia Dahir, Abdukadir; Omar, Mohamed; Abukar, Yahye
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4117

Abstract

Drought is defined as a protracted lack of precipitation that lasts at least a season, resulting in a water deficit that affects plants, animals, and humans. It is a widespread and repeating feature of climate change in practically all temperate zones, ranging from excessively wet to burned. In Somalia, the government and farmers lack the technological skills to identify and monitor recurrent environmental issues. Our research created a technique for detecting droughts early on to reduce their impact. Using internet of things (IoT) devices, we created a system that measures temperature, humidity, and soil moisture. We then examined the data and used a line chart to show it in a web application (PHP and MySQL). The device reads these environmental parameters using an Arduino Uno, a DHT11 sensor, and a soil moisture sensor. The system deployed provides a real-time, cost-effective method for monitoring and controlling drought in modern agriculture.
Nutrition information estimation from food photos using machine learning based on multiple datasets Mustafa Al-Saffar; Wadhah R. Baiee
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4007

Abstract

Bodyweight, blood pressure, and cholesterol are all risk variables that can aid people in making educated decisions regarding their health promotion activities. Food choices are among the most effective methods for preventing chronic illnesses, including heart disease, diabetes, stroke, and some malignancies. Because various meals give varying amounts of energy and minerals, good eating necessitates keeping track of the nutrients we ingest. Furthermore, there is a paucity of information on whether understanding food constituents might aid in more accurate nutrition calculations. Therefore, this research suggests processing food images on social media to anticipate the contents of each food and extracting nutrition information for each food image to serve as healthy implicit feedback to take advantage of the rapid accumulation of rich photos on social media. The proposed methodology is a framework based on a machine-learning model for predicting food ingredients. We also compute critical health metrics for each ingredient and combine them to obtain nutrition data for the food. The result revealed a promising way of extracting food components and nutrition information. Compared with other researchs, our proposed prediction and attribute extraction strategy achieves a remarkable accuracy of 85%.
Performance analysis of inductive power transfer using JMAG-designer Mahadi, Ismail Ahmat; Yahaya, Jabbar Al-Fattah
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3570

Abstract

Due to its advantage of sending electrical power from the transmitter source to the receiver load with no physical contact, wireless power transfer (WPT) has rapidly gained popularity in recent years. They can be used in a variety of applications, including induction cooking, mobile phone charging, radio frequency identification (RFID), and electric vehicles (EVs). Using JMAG-designer, a simulation of series-parallel inductive power transmission has been investigated in this research. This study aims to determine how the output power and efficiency change depending on how many coils turn in the transmitter and receiver. The number of coils turn in the transmitter is fixed which is 20 turns, the number of coils turn in the receiver is variable and ranges between 15 and 30, and the air gap or distance between the coupling coils is set at 10 cm. The selected frequency to be used in this simulation is between 10 and 50 kHz. According to the absorption result, the output power and efficiency rise when the receiver has more coil turns than the transmitter, and the output power and current rise along with an increase in resonance frequency.
Integrating security and privacy in mmWave communications Ghadah M. Faisal; Hasanain Abdalridha Abed Alshadoodee; Haider Hadi Abbas; Hassan Muwafaq Gheni; Israa Al-Barazanchi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4314

Abstract

The aim of this paper is to integrate security and privacy in mmWave communications. MmWave communication mechanism access three major key components of secure communication (SC) operations. proposed design for mmWave communication facilitates the detection of the primary signal in physical (PHY) layer to find the spectrum throughput for primary user (PU) and secondary user (SU). The throughput of SC for PU with maximum throughput being recorded at 0.7934 while maximum throughput for SU is recorded at 0.7679. So, we will design a mmWave communication mechanism for solving this problem. The probability for sensing where the probability of detection (PD) is predicted at a defined range of 690 km with an estimated accuracy of 83.56% while the probability of false alarm (PFA) is predicted at a defined range of 230 km with an estimated accuracy of 81.39%. This conflicting but interrelated issue is investigated over three stages for the purpose of solving with a cross-layer model with MAC and PHY layers for a secure communication network (SCN) while reducing the collision effect concurrently with a 92.76% for both cross-layers. MATLAB 2019b would be forwarded in use as the increasing demand for augmenting the bandwidth in secure communications has actuated the evolutionary technology.
Quantitative feedback theory based robust speed control of vector controlled induction motor Krishnankutty, Jisha Lakshmi; Thomas, Arekkadan Antony Powly; Srivastava, Suresh
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4927

Abstract

In this research paper a method for the robust speed control of Indirect Field orient controlled induction motor (IM) is proposed. The quantitative feedback theory (QFT) in implemented to design the controller in order to achieve the desired performance for the closed loop system in the presence of uncertainties and parameter variations. In this research work the QFT based controller is designed for the simplified model of IM. The worst case of uncertainties and all possible parameter variations are taken into consideration. The IM with the controller developed is simulated using the MATLAB/Simulink and the results are analyzed and compared with the conventional proportional integral derivative (PID) controller. The time domain and frequency domain analysis of both controllers were conducted and compared. A study on the nature of electromagnetic torque and control signal is also included to justify the effectiveness of proposed controller. The simulation results verify the superior performance of the proposed robust control method compared to PID controller.
K-Means clustering-based semi-supervised for DDoS attacks classification Mahdi Nsaif Jasim; Methaq Talib Gaata
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4353

Abstract

Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.
A comparison statement on DCPWM based conducted EMI noise mitigation process in DC-DC converters for EV Srinivasan Kalaiarasu; Sudhakar Natarajan
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4315

Abstract

Fast switching techniques at high frequencies are employed for quick charging and energy conversion in electric vehicle (EV) power converters. Electromagnetic interference (EMI) noise is produced due to the fast-switching process, which may result in malfunctioning and degraded EV performance. In this work, a digital chaotic pulse width modulation (DCPWM) technique-based EMI noise mitigation process has been applied to elementary positive output super lift Luo (EPOSLL), two-stage cascaded boost (TSCB), and ultra-lift Luo (ULL) converters, and a comparison study has been conducted with EMI reduction levels as per electromagnetic compatibility (EMC) standards. The duty cycle is varied from 0.5 to 0.67 to get the desired output voltage as an input of 10V to achieve the power ratings of 40 W to 80 W for various load conditions. A total of 4 dBV (3 V) to 15 dBV (10 V) of conducted EMI noise has been mitigated for the above-said converters. Simulation results based on power spectrum density and hardware results based on fast fourier transform (FFT) of output voltages are analyzed. According to the findings, the ULL converter is more acceptable for electromagnetic compatibility in EV applications than EPOSLL and TSCB DC-DC converters.
A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems Mounir Bensaid; Abdellfattah Ba-Razzouk; Mustapha Elharoussi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4141

Abstract

Several strategies have been developed for identifying power quality issues, monitoring them, and compensating for relevant disturbances. In this field, online estimate of amplitudes and phase angles of network voltages and currents is commonly used. The adaptive linear neuron (ADALINE)-based voltage sag detection algorithm with least mean square (LMS) adaptation allows for rapid convergence of estimate techniques based on artificial neural networks (ANN). This approach has the advantage of being straightforward to implement on hardware and based on simple calculations (essentially multiply and accumulate "MAC"). This paper gives a comparison of the performance of two ADALINE approaches ("with" and "without" error supervision) for detecting and estimating voltage dips. The described techniques and models of a two-coupled motor system were implemented in MATLAB/Simulink/SimPowerSystems to run simulations under various fault scenarios in order to create the three-phase voltage sag alarm signal. The simulation outcomes are presented and debated.
Data mining and analysis for predicting electrical energy consumption Inteasar Yaseen Khudhair; Sanaa Hammad Dhahi; Ohood Fadil Alwan; Zahraa A. Jaaz
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4593

Abstract

In this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies.
Modeling recurrence of COVID-19 and its variants using recurrent neural network Bolarinwa, Jesufunbi Damilola; Vincent, Olufunke Rebecca; Aborisade, Dada Olaniyi; Adenusi, Cecilia Ajowho; Ugwunna, Charles Okechukwu
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3620

Abstract

Coronavirus disease 19 (COVID-19), a disease caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), began as the flu and gradually developed into a highly infectious global pandemic leading to the death of over 6 million people in about 200 countries of the world. Its pathogenic nature has qualified it as a deadly disease, causing moderate and severe respiratory difficulty in infected individuals with the ability to mutate into different variants of the first version. As a result, different government agencies and health institutions have sought solutions within and outside the clinical space. This paper models COVID-19 possible recurrence as variants and predicts that the subsequent waves will be more severe than the first wave. Long short-term memory network (LSTM) was used to predict the future occurrence of COVID-19 and forecast the virus's pattern. Machine evaluation was performed using precision, recall, F1-score, an area under the curve (AUC), and accuracy evaluation metrics. Datasets obtained were used to test the data. The collected characteristics were passed on to the system classification network, demonstrating the function's value based on the system's accuracy. The results showed that the COVID-19 variants have a higher disastrous effect within three months after the first wave.

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