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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 64 Documents
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Search results for , issue "Vol 11, No 1: February 2022" : 64 Documents clear
Two-port network model of wind turbine generator for three-phase unbalanced distribution system load flow analysis Rudy Gianto; Purwoharjono Purwoharjono
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.v11i1.3074

Abstract

An unbalanced electric power system can occur due to unequal lines and/or loads in each phase of the system. For a system with a high level of unbalance, the assumption that the system is balanced becomes incorrect. For a case like this, the system must be analyzed using three-phase approaches because single-phase analysis techniques can no longer be used to obtain accurate results. Therefore, the development of three-phase models of all components of the electric power system, including the wind turbine generating system (WTGS), is very important so that the system can properly be analyzed and its performance can correctly be evaluated. This paper proposes a method to incorporate WTGS in a three-phase load flow analysis of an unbalanced distribution system. The proposed method is based on the previously published single-phase two-port network model. In this paper, the single-phase two-port network model is modified and extended so that it can be applied to unbalanced systems. The model is then included in the three-phase distribution system load flow (DSLF) analysis. The proposed method is validated using 12-node and 19-node distribution systems.
Deployment of e-services based contextual smart agro system using internet of things Abdus Sattar; Yeasin Arafat Shampod; Md. Tanjid Ahmed; Nasrin Akter; Arif Mahmud
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.v11i1.3255

Abstract

Climate change's effects are becoming more apparent, and farmers are bearing the brunt of the consequences. As a result, by 2050, food production is expected to decline by 18%. Therefore, the study's goal is to develop effective and well-organized roadmap for context-based smart agricultural systems using a pre-determined ICT framework. Following that, this study offers a four-level conceptual framework for an e-services-based smart agro system utilizing internet of things (IoT). Here, each level optimizes the IoT infrastructure to accept e-services based on contextual information supplied by the e-services. Furthermore, the proposed ICTization process intends to broaden the role of ICT technology development. Besides, the system's views decrease misunderstandings about technology, growth, and connectivity while also enhancing raw data, administration, and service synchronization. Farmers, agricultural officers, and network operators, for example, are all included in the proposed roadmap, which includes omnipresent farm treatment services. Precision farming, on the other hand, need new knowledge and innovation in order to achieve an integrated and comprehensive approach to technology.
Intelligent mobile detection of cracks in concrete utilising an unmanned aerial vehicle Khattab M. Ali Alheeti; Muzhir Shaban Al-Ani; Abdulkareem Kareem Najem Al-Aloosy; Abdulkareem Alzahrani; Duaa Abdul Sattar Rukan
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.v11i1.2987

Abstract

Cracks in constructions may result in negative consequences in terms of expenditure and safety. This in turn highlights the importance of finding ways to detect these cracks easily and effectively. Hence, technological advances play significant role in enabling effective and innovative ideas, such as the use of autonomous drones and artificial intelligence solutions. In this research, we utilise a type of drone called an unmanned aerial vehicle, equipped with a high-speed camera that can capture images of cracks in buildings, and pass the information to the system. We utilised a dataset that has images collected from different Middle East Technical University (METU) campus buildings with various concrete surfaces (with and without cracks). The crack detection approach uses statistical measures and a support vector machine that prevents overfitting, attains a good rate of accuracy, tackles problems in real-time, and can train a model when a small dataset exists. The combination of an unmanned aerial vehicle, artificial intelligence, and digital image processing gives excellent results. Performance metrics reported for seven rounds of experiments showed rates of accuracy in detection ranging from 83.3–100% (with 100% achieved in two rounds). This demonstrates the effectiveness of our proposed detection system in detecting cracks in constructions.
Image compression using singular value decomposition by extracting red, green, and blue channel colors Shamsul Fakhar Abd Gani; Rostam Affendi Hamzah; Ramlan Latip; Saifullah Salam; Fatin Noraqillah; Adi Irwan Herman
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.v11i1.2602

Abstract

This paper presents an image compression using singular value decomposition (SVD) by extracting the red, green, and blue (RGB) channel colors. Image compression is needed in the development of various multimedia computer services and applications for example in the telecommunications and storage technologies. Now a days, video technology, digital broadcast codec and teleconferencing become popular and always requires high image compression process for display. Hence, efficient image compression is compulsory to reduce the number of storage sizes and maintain the image quality. Therefore, this article proposes image compression using SVD, which this method is efficiently reducing the image storage size and at the same time maintaining the image quality. The SVD removes redundant pixel values based on RGB colors to make the storage image size decreased. Based on the experimental analysis on two different type of image extensions (i.e., jpg and png), the SVD is capable to reduce the image size and at the same time preserving the image quality.
Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks Ahmed Subhi Abdalkafor; Salah A. Aliesawi
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.v11i1.3462

Abstract

Wireless sensor network (WSN) is one of the most promising technologies due to its size, cost-effective nature, and its ability to easily deploy in the target environment, as well as for its entry into many sensitive applications. However, making the most of the potential of this network is very difficult due to many issues, including the data received from the sensor nodes contains a huge amount of data redundant that negatively affects the overall network performance. Recent years have witnessed an increasing interest in data aggregation technology intending to eliminate redundant data from neighboring sensor nodes before transferring to the base station, thus improve performance efficiency and increasing the wireless sensor networks lifespan. This paper focused on applying three intelligent algorithms (SOM, HAC, and RBF) and describing the impact of data aggregation strategy on WSNs through the results obtained. As well as, an accurate description of the literature that applied these algorithms. A Competitive classification accuracy has been achieved when the proposed work is implemented and tested via the intel berkeley research lab dataset.
Performance of K-means algorithm based an ensemble learning Dhurgham Kadhim Hashim; Lamia Abed Noor Muhammed
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.v11i1.3550

Abstract

K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on the initial centroids of clusters that would be selected randomly. More suggestions have been given in order to overcome this problem. Ensemble learning is a method used in clustering; multiple runs are executed that produce different results for the same data set. Then the final results are driven. According to this hypothesis, more ensemble learning techniques have been suggested to deal with the clustering problem. One of these techniques is "Three ways method". However, in this paper, three ways method as an ensemble technique would be suggested to be merged with k-mean algorithm in order to improve its performance and reduce the impact of initial centroids on results. Then it was compared with traditional k-means results through practical work that was executed using popular data set. The evaluation of the hypothesis was done through computing related metrics.
Inclusive bidirectional conversion system between Chittagonian and standard Bangla Nahid Hossain; Hafizur Rahman Milon; Sheikh Nasir Uddin Sabbir; Azfar Inan
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.v11i1.3237

Abstract

In recent years, the Bangla language has come out as a very prominent figure in the world of natural language processing. Many researchers have produced exemplary works on the language, but there has not been any notable work on its highly enriched dialects due to the lack of resources and the diversity and complexity in its grammatical structure. This paper has suggested a bidirectional conversion system for one of the most widely used dialects of Bangla; the Chittagonian dialect. Our method employs a bidirectional lexicon that uses binary search, word-to-word mapping, and morphological transformations. The system has achieved an accuracy rate of 95.86% for Chittagonian to standard Bangla and 93.89% in the case of standard Bangla to Chittagonian. We have also provided an efficient word suggestion module, and it has yielded satisfactory results.
Extractive text summarization for scientific journal articles using long short-term memory and gated recurrent units Devi Fitrianah; Raihan Nugroho Jauhari
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.v11i1.3278

Abstract

Along with the increasing number of scientific publications, many scientific communities must read the entire text to get the essence of information from a journal article. This will be quite inconvenient if the scientific journal article is quite long and there are more than one journals. Motivated by this problem, encourages the need for a method of text summarization that can automatically, concisely, and accurately summarize a scientific article document. The purpose of this research is to create an extractive text summarization by doing feature engineering to extract the semantic information from the original text. Comparing the long short-term memory algorithm and gated recurrent units and were used to get the most relevant sentences to be served as a summary. The results showed that both algorithms yielded relatively similar accuracy results, with gated recurrent units at 98.40% and long short-term memory at 98.68%. The evaluation method with matrix recall-oriented understudy for gisting evaluation (ROUGE) is used to evaluate the summary results. The summary results produced by the LSTM model compared to the summary results using the latent semantic analysis (LSA) method were then obtained recall values at ROUGE-1, ROUGE-2, and ROUGE-L respectively were 76.25%, 59.49%, and 72.72%.
Improving intrusion detection in SCADA systems using stacking ensemble of tree-based models Duc-Duong Nguyen; Minh-Thuy Le; Thanh-Long Cung
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.v11i1.3334

Abstract

This paper introduces a stacking ensemble model, which combines three single models, to improve intrusion detection in supervisory control and data acquisition (SCADA) systems. The first layer of the proposed model is the combination of random forest, light boosting gradient machine, and eXtreme gradient boosting models. We use an multilayer perceptron (MLP) network as a meta-classifier of the model. The proposed model is optimized and tested on an international dataset (gas pipeline dataset). The tested results show an accuracy of 99.72% with the f1-score of 99.72% for binary classification tasks (attacked or non-attacked detection). For categorical tasks, the detection rates of almost all attack types are higher than 97.55% (except for denial of service (DoS)-95.17%), with an overall accuracy of 99.62%.
Breast cancer segmentation using K-means clustering and optimized region-growing technique Srwa Hasan Abdulla; Ali Makki Sagheer; Hadi Veisi
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.v11i1.3458

Abstract

Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.

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