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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 63 Documents
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Search results for , issue "Vol 10, No 2: April 2021" : 63 Documents clear
Live migration using checkpoint and restore in userspace (CRIU): Usage analysis of network, memory and CPU Adityas Widjajarto; Deden Witarsyah Jacob; Muharman Lubis
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2742

Abstract

Currently, cloud service providers have used a variety of operational mechanisms to support the company's business processes. Therefore, the services are stored on the company's server, which presents in the form of infrastructure, platform, software, and function. There are several vulnerabilities that have been faced in the implementation such as system failure, natural disasters, human errors, or attacks from unauthorized parties. In addition, the time of unavailability of services can be minimized by doing an LM, in which many servers have been used the containers to behave like a service provider. Actually, its existence replaces the virtual machine that requires more resources although the process only can be done through docker with checkpoint and restore in userspace (CRIU). In this research, LM processes are done to the docker container using CRIU by analyzing the quality of service (QoS), memory, and CPU usage. Thus, the simulation is carried out by establishing the LM using 2 (two) different platforms through scenarios with one and three containers respectively. The performance analysis results aim to examine several indicators in comparison with the achievable result to reduce problems that occurred in the cloud service.
A smart partial discharge classification SOM with optimized statistical transformation feature Z. H. Bohari; M. Isa; A. Z. Abdullah; P. J. Soh; M. F. Sulaima
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2751

Abstract

Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.
Secure outage probability of cognitive radio network relying non-orthogonal multiple access scheme Chi-Bao Le; Dinh-Thuan Do
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2116

Abstract

This paper studies the secondary network relying relay selection to transmit signal from the secondary source (base station) to two destinations. Especially, two destinations are required non-orthogonal multiple access (NOMA) scheme and it benefits to implementation of the Internet of Things (IoT) systems. However, eavesdropper over-hears signal related link from selected relay to destination. This paper measure secure performance via metric, namely secure outage probability (SOP). In particular, signal to noise ratio (SNR) criterion is used to evalute SOP to provide reliable transmission to the terminal node. Main results indicates that the considered scheme provides performance gap among two signals at destination. The exactness of derived expressions is confirmed via numerical simulation.
An in-depth exploration of Bangla blog post classification Tanvirul Islam; Ashik Iqbal Prince; Md. Mehedee Zaman Khan; Md. Ismail Jabiullah; Md. Tarek Habib
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2873

Abstract

Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are Support Vector Machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels Marion O. Adebiyi; Micheal O. Arowolo; Oludayo Olugbara
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2769

Abstract

Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.
Performance evaluation for vehicular ad-hoc networks based routing protocols Hussain Falih Mahdi; Mohammed Salah Abood; Mustafa Maad Hamdi
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2943

Abstract

VANET is a branch of MANETS, where each vehicle is a node, and a wireless router will run. The vehicles are similar to each other will interact with a wide range of nodes or vehicles and establish a network. VANETs provide us with the infrastructure to build new solutions for improving safety and comfort for drivers and passengers. There are several routing protocols proposed and evaluated for improving VANET's performance. The simulator is preferred over external experience because it is easy, simple, and inexpensive. In this paper, we choose AODV protocol, DSDV protocol, and DSR protocol with five different nodes density. For each protocol, as regards specific parameters like (throughput, packet delivery ratio, and end- to- end delay). On simulators that allow users to build real-time navigation models of simulations using VANET. Tools (SUMO, MOVE, and NS-2) were used for this paper, then graphs were plotted for evaluation using Trace-graph. The results showed the DSR is much higher than AODV and DSDV, In terms of throughput. While DSDV is the best choice because of the low average end to end delay. From the above, we conclude that each strategy has its own negative and positive aspects that make it ideally suited to a particular scenario than other scenarios.
Hardware-in-the-loop based comparative analysis of speed controllers for a two-mass system using an induction motor drive with ideal stator current performance Vo Thanh Ha; Tung Lam Nguyen; Vo Thu Ha
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2370

Abstract

A comparative study of speed control performance of an induction motor drive system connecting to a load via a non-rigid shaft. The nonrigidity of the coupling is represented by stiffness and damping coefficients deteriorating speed regulating operations of the system and can be regarded as a two-mass system. In the paper, the ability of flatness based and backstepping controls in control the two-mass system is verified through comprehensive hardware-in-the-loop experiments and with the assumption of ideal stator current loop performance. Step-by-step control design procedures are given, in addition, system responses with classical PID control are also provided for parallel comparisons. 
Microscopy images segmentation algorithm based on shearlet neural network Nemir Ahmed Al-Azzawi
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2743

Abstract

Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
A streamlined 17-level cascaded H-bridge multilevel converter with solar PV integration Muhammad Hamza Shahbaz; Kashif Amjad; Naqash Ahmad; Arslan Ahmed Amin; Sajid Iqbal; Muhammad Gufran Khan; Muhammad Adnan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2764

Abstract

The quest for a green electrical power system has increased the use of renewable energy resources and power electronic converters in the existing power system. These power electronic converters, however, are a major cause of harmonics and result in the degradation of power quality. In the last two decades, researchers have proposed various designs of multilevel converters to minimize these harmonic distortions, however, a comprehensive solution for stand-alone solar photovoltaic (PV) systems with low total harmonic distortion (THD) is still missing in the present body of knowledge. This paper proposes a single-phase 17-level cascaded H-bridge multilevel converter (CHMC) model for a stand-alone system using solar PV arrays. The proposed model employs eight different flexible PV arrays that can be replaced with DC voltage sources when required to meet the load demand. The proposed model does not include any capacitor and filter thus saving a lot of cost in the overall system. The model has been implemented in the Simulink environment using a model-based design approach. The simulation results show that the proposed model has reduced the THD to almost 7% as compared to the existing models. The cost comparison of the proposed converter also proved its economic benefit over other types.
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system Mohd Suhairi Md Suhaimin; Mohd Hanafi Ahmad Hijazi; Chung Seng Kheau; Chin Kim On
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2859

Abstract

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

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