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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 66 Documents
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Search results for , issue "Vol 11, No 6: December 2022" : 66 Documents clear
Energy management strategy with smart building control system to reduction electrical load using ANN Bareq Musaab Jalal; Rashid Hamid Al-Rubayi
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.v11i6.4087

Abstract

Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.
Toothed log periodic graphene-based antenna design for THz applications Farah Mustafa Rasheed; Hussein A. Abdulnabi
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.v11i6.4256

Abstract

This paper proposes a graphene-based toothed log-periodic antenna for the THz frequency region (0.1–10) THz applications. By adjusting the applied DC voltage on the graphene, the antenna's properties, such as bandwidth, radiation pattern operational frequency ranges have been shifted. The chemical potential, surface conductivity, and surface impedance of the graphene are affected by changing applied DC voltage and hence a reconfigurable antenna has been resulting. The suggested antenna's radiating element is from a graphene material and has log-periodic shape, with 50 ohm feed line placed on the grounded silicon dioxide substrate, 1 µm-thick layers of silicon crystalline and alumina on top of the substrate. The antenna is simulated by the computer simulation technology (CST) 2020 software program. The resultant bandwidth (7-10) TH has a return loss of less than -10 when the chemical potential of graphene is 1eV.
Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning Muhammad Munsarif; Muhammad Sam’an; Safuan Safuan
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.v11i6.3927

Abstract

Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
Distributed brain tumor diagnosis using a federated learning environment Dhurgham Hassan Mahlool; Mohamed Hamzah Abed
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.v11i6.4131

Abstract

In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accurate clinical findings.
Analysis of instrumentation system for photovoltaic pyranometer used to measure solar irradiation level Prisma Megantoro; Muhammad Akbar Syahbani; Sigit Dani Perkasa; Ahmad Rahmad Muzadi; Yusrizal Afif; Agus Mukhlisin; Pandi Vigneshwaran
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.v11i6.4390

Abstract

A pyranometer is a device used to measure the level of solar irradiation. This device has a sensor that measures the density of the electromagnetic flux of solar radiation on a flat plane. The electromagnetic flux density parameter is converted into an electrical parameter in watts per square meter. Pyranometers are used in weather station devices to analyze and predict weather conditions. Solar power generation systems are usually also installed with this device. It is intended to monitor solar irradiation's condition to examine the generating system's performance. This article discusses the photovoltaic-based pyranometer characterization method. The characterization method is carried out to determine the measurement parameters such as accuracy, precision, and hysteresis. Knowing these parameters will make it possible to see the performance of measuring solar irradiation levels by a measuring instrument for solar irradiation levels, like a pyranometer. The characterization method is to compare the measurement results with standard instruments. The solar irradiance level monitoring is also optimal, accurate, and precise with a reliable measurement method.
Reliability analysis of single phase quazi Z source inverter for standalone photovoltaic system Bharathi Rao; M. Satyendra Kumar
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.v11i6.4101

Abstract

Quazi Z source inverter has an advantage that it can boost or buck voltage to be given to an inverter. Operation of a QZSI is similar to a Z source inverter (ZSI). A conventional photovoltaic application uses a two-stage topology of boost converter and an inverter. A QZSI can be replaced for a two-stage topology to serve the purpose. A 3 KW single phase QZSI is designed and simulated for a standalone photovoltaic system in this paper. The results for total harmonic distortion (THD) of output voltage and mean time between failure (MTBF) of the overall system is compared with a conventional ZSI and two-stage topology for the same rating. MTBF of the overall system is computed using reliability block diagram method. Reliability curve is also plotted for all the three considered topologies.
Multi-feature stacking order impact on speech emotion recognition performance Yoga Tanoko; Amalia Zahra
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.v11i6.4287

Abstract

One of the biggest challenges in implementing SER is to produce a model that performs well and is lightweight. One of the ways is using one-dimensional convolutional neural network (1D CNN) and combining some handcrafted features. 1D CNN is mostly used for time series data. In time series data, the order of information plays an important role. In this case, the order of stacked features also plays an important role. In this work, the impact of changing the order is analyzed. This work proposes to brute force all possible combinations of feature orders from five features: Mel-frequency cepstral coefficient (MFCC), Mel-spectrogram, chromagram, spectral contrast, and tonnetz, then uses 1D CNN as the model architecture and benchmarking the model's performance on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that changing the order of features can impact overall classification accuracy, specific emotion accuracy, and model size. The best model has an accuracy of 79.17% for classifying 8 emotion classes with the following order: spectral contrast, tonnetz, chromagram, Mel-spectrogram, and MFCC. Finding a suitable order can increase the accuracy up to 16.05% and reduce the model size up to 96%.
Grasshopper optimization algorithm based path planning for autonomous mobile robot Asmaa Shareef; Salah Al-Darraji
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.v11i6.4098

Abstract

Autonomous mobile robots have become very popular and essential in our life, especially in industry. One of the crucial activities of the robot is planning the path from a start point to a target point, avoiding obstacles in the environment. Recently, path planning received more attention, and many methodologies have been proposed. Path planning studies have shown the effectiveness of swarm intelligence in complex and known or unknown environments. This paper presents a global path planning method based on grasshopper optimization algorithm (GOA) in a known static environment. This algorithm is improved using the bias factor to increase the efficiency and improve the resulting path. The resulting path from this algorithm is further enhanced using an improved version multinomial logistic regression algorithm (MLR). The algorithms were evaluated using three different large environments of varying complexities. The GOA algorithm has been compared with the ant colony optimization algorithm (ACO) using the same environments. The experiments have shown the superiority of our algorithm in terms of time convergence and cost.
A review on techniques for improving power quality: research gaps and emerging trends Vasupalli Manoj; Prabodh Khampariya; Ramana Pilla
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.v11i6.4396

Abstract

Many issues have arisen for the electricity sector because of the widespread use of gadgets that rely on electrical power. India's energy demands are rising rapidly due to the country's rapid economic development. These days, almost every industry makes extensive use of electrical machinery and equipment. Electronic gadgets add a non-linear demand to the power grid, which can cause power quality (PQ) problems such as voltage fluctuations that can damage electrical equipment and even knock the grid down. As the use of DG units becomes more commonplace, complications related to power conversion interfaces, load switching, and other factors emerge as potential bottlenecks in service. Power filters were first created several decades ago to address problems with PQ and to dampen harmonics caused by nonlinear loads. This document summarises previous studies that have investigated the effectiveness of power filters and other techniques for enhancing power quality. There are also descriptions of research that is still going on and where it stands, as well as a look ahead to what its possible future benefits might be.
Hiding text using the least significant bit technique to improve cover image in the steganography system Estabraq Hussein Jasim Halboos; Abbas M. Albakry
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.v11i6.4337

Abstract

One of the highest priorities in the era of information technology is to achieve an accurate and effective system for hiding security data. One of the goals of steganography is imperceptability to intruder. So this paper work to increase the imperceptibility on image, which has weaknesses in previous studies, as well as to avoid statistical attacks such as chi-square. A method has been proposed that includes calculating the color contrasts in the homogeneous areas of the image and dividing them according to the color contrast and exploiting the data of pixels that have a high impact to embed on the two first and third bits of least significant bit (LSB) to increase the amount of embedded data, impact regions (IR) classify according to selected features extracted in advance by using the support vector machine (SVM) classifier. Work was done on standard images taken from a standard dataset (USC-SIPI) for two types of gray and color images. The results showed the worth of the proposed method through a high peak signal to noise ratio (PSNR) that reached 89.5 dB due to the distribution of data on pixels according to the proposed method

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