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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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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.
Normal operation and reverse action of on-load tap changing transformer with its effect on voltage stability Sinan Moayad A. Alkahdely; Ahmed Nasser B. Alsammak
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.v12i2.4556

Abstract

As electrical grids have expanded significantly, so too has the load on network buses. This, however, causes voltage drops to occur at the load side of the grid. A voltage drop causes a system to become unstable, increases its power loss, and reduces the amount of power that it transfers before finally leading to a collapse. An on-load tap changing (OLTC) transformer can be used to prevent the negative effects of an increased load by restoring the load voltage to its base value when sudden disturbances occur in the source. However, incorrect OLTC placement can cause the system to become unstable and cause collapse. This is referred to as the reverse action phenomenon of an OLTC. Therefore, this present study examined improving the ability of an OLTC to increase system stability and prevent collapse. A simple radial power distribution system was modelled in MATLAB. The results indicate that the proposed model can increase system stability and prevent collapse.
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.
A predictive analysis framework of heart disease using machine learning approaches Shourav Molla; F. M. Javed Mehedi Shamrat; Raisul Islam Rafi; Umme Umaima; Md. Ariful Islam Arif; Shahed Hossain; Imran Mahmud
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.v11i5.3942

Abstract

Heart diseaseis among the leading causes for death globally. Thus, early identification and treatment are indispensable to prevent the disease. In this work, we propose a framework based on machine learning algorithms to tackle such problems through the identification of risk variables associated to this disease. To ensure the success of our proposed model, influential data pre-processing and data transformation strategies are used to generate accurate data for the training model that utilizes the five most popular datasets (Hungarian, Stat log, Switzerland, Long Beach VA, and Cleveland) from UCI. The univariate feature selection technique is applied to identify essential features and during the training phase, classifiers, namely extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), gradient boosting (GB), and decision tree (DT), are deployed. Subsequently, various performance evaluations are measured to demonstrate accurate predictions using the introduced algorithms. The inclusion of Univariate results indicated that the DT classifier achieves a comparatively higher accuracy of around 97.75% than others. Thus, a machine learning approach is recognize, that can predict heart disease with high accuracy. Furthermore, the 10 attributes chosen are used to analyze the model's outcomes explainability, indicating which attributes are more significant in the model's outcome.
Use of scanning devices for object 3D reconstruction by photogrammetry and visualization in virtual reality Irena Drofova; Wei Guo; Haozhou Wang; Milan Adamek
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.v12i2.4584

Abstract

This article aims to compare two different scanning devices (360 camera and digital single lens reflex (DSLR) camera) and their properties in the three-dimensional (3D) reconstruction of the object by the photogrammetry method. The article first describes the various stages of the process of 3D modeling and reconstruction of the object. A point cloud generated to the 3D model of the object, including textures, is created in the following steps. The scanning devices are compared under the same conditions and time from capturing the image of a real object to its 3D reconstruction. The attributes of the scanned image of the recon-structed 3D model, which is a mandarin tree in a citrus greenhouse in a daylight environment, are also compared. Both created models are also compared visually. That visual comparison reveals the possibilities in the application of both scanning devices can be found in the process of 3D reconstruction of the object by photogrammetry. The results of this research can be applied in the field of 3D modeling of a real object using 3D models in virtual reality, 3D printing, 3D visualization, image analysis, and 3D online presentation.
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.
An insight into the intricacies of lingual paraphrasing pragmatic discourse on the purpose of synonyms Jabir Al Nahian; Abu Kaisar Mohammad Masum; Muntaser Mansur Syed; Sheikh Abujar
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.v11i5.3485

Abstract

The term "paraphrasing" refers to the process of presenting the sense of an input text in a new way while preserving fluency. Scientific research distribution is gaining traction, allowing both rookie and experienced scientists to participate in their respective fields. As a result, there is now a massive demand for paraphrase tools that may efficiently and effectively assist scientists in modifying statements in order to avoid plagiarism. natural language processing (NLP) is very much important in the realm of the process of document paraphrasing. We analyze and discuss existing studies on paraphrasing in the English language in this paper. Finally, we develop an algorithm to paraphrase any text document or paragraphs using WordNet and natural language tool kit (NLTK) and maintain "Using Synonyms" techniques to achieve our result. For 250 paragraphs, our algorithm achieved a paraphrase accuracy of 94.8%.
Security of private cloud using machine learning and cryptography Jabbar, Ali Abdulsattar; Bhaya, Wesam Sameer
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.v12i1.4383

Abstract

There are increased security challenges that target cloud systems. One of the most important requirements of users in cloud storage is protecting their cloud from attacks and keeping data secure. Modern technologies of machine learning are providing the ability to analyze and classify data perfectly. This paper proposes a model placed between users and the cloud, which is based on two phases. The first of which is protecting the cloud from different types of network attacks and detecting normal and abnormal flow. The second one is categorizing the users' data and then encrypting it based on its importance using different encryption algorithms. The accuracy results of random forest (RF) and decision tree (DT) are 100% of attack detection for each one. For the second phase of classifying data, the algorithms used are the logistic regression (LR) and stochastic gradient descent (SGD) learning which resulted in 98% accuracy for both. Besides, the encryption algorithms that have been adopted are rivest cipher (RC4), triple data encryption (3DES), and advanced encryption standard (AES) for encryption of the classified data according to the importance which will be then stored in the cloud in its secure form.
Characterization of metamaterial based patch antenna for worldwide interoperability for microwave access application Pandharinath R. Satarkar; Rajesh Basant Lohani
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.v11i5.4149

Abstract

Electromagnetic metamaterial is an artificial material that is made up of different types of structural designs on dielectric substrates. In this paper a broad and elite investigation is being carried out by designing and simulating a metamaterial cell comprising a square split ring resonator with a copper wire strip etched on the ground plane to discover its some unusual parameters such as double negativity of cell which are naturally not found in other materials of nature. A course of action of these unit cells in a grouping shapes metamaterial. These metamaterial cells show exceptionally great applications in the design of microstrip patch antennas by improving their characteristics such as bandwidth, return loss, and gain. The proposed microstrip line feed patch antenna is designed at a 3.5 GHz resonance frequency useful for various worldwide interoperability for microwave access (WiMAX) applications. The ground plane of a substrate of a patch antenna is loaded with a square split-ring resonator, the proposed antenna is fabricated to obtain experimental parameters. A conventional and proposed patch antenna is simulated, fabricated tested analysed, and reported for performance comparison of its parameters.
Synthesis of sliding mode control for flexible-joint manipulators based on serial invariant manifolds Thang, Le Tran; Son, Tran Van; Khoa, Truong Dang; Chiem, Nguyen Xuan
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.v12i1.4363

Abstract

This paper focuses on synthesizing sliding mode control (SMC) for flexible-joint manipulators (FJM) based on serial invariant manifolds in order to increase the control quality for the system. SMC based on the serial invariant manifolds is proposed. The control law is found based on synergetic control theory (SCT) and analytical design of aggregated regulators (ADAR) method. In order to improve the control quality due to the effect of the stiffness value between two links in the system, a mechanism for constructing manifolds is built. The time response of the outer loop manifolds close to the actuator will be larger in the next round. The control quality of the system can be pre-evaluated through the parameters of the designed manifolds. Global stability is demonstrated by using the Lyapunov function in the design process. Finally, the effectiveness of the proposed controller based on SCT is demonstrated by numerical simulation results and compared with the traditional SMC.

Page 170 of 291 | Total Record : 2901

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