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International Journal of Electrical and Computer Engineering

IJECE
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Published by Institute of Advanced Engineering and Science

ISSN : 20888708 EISSN : 27222578 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 115 Documents

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Search results for , issue "Vol 10, No 4: August 2020" : 115 Documents clear

Analysis of a framework implementation of the transceiver performances for integrating optical technologies and wireless LAN based on OFDM-RoF Adnan Hussein Ali; Alaa Desher Farhood; Maham Kamil Naji
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The greatest advantages of optical fibers are the possibility of extending data rate transmission and propagation distances. Being a multi-carrier technique, the orthogonal frequency division multiplexing (OFDM) can be applicable in hybrid optical-wireless systems design owing to its best spectral efficiency for the interferences of radio frequency (RF) and minor multi-path distortion. An optical OFDM-RoF-based wireless local area network (W-LAN) system has been studied and evaluated in this work. The outline for integrating an optical technology and wireless in a single system was provided with the existence of OFDM-RoF technology and the microstrip patch antenna; these were applied in the Optisystem communication tool. The design of the proposed OFDM-RoF system is aimed at supporting mm-wave services and multi-standard operations. The proposed system can operate on different RF bands using different modulation schemes like 4,16 and 64QAM, that may be associated to OFDM and multidata rates up to 5 Gbps. The results demonstrate the robustness of the integrated optical wireless link in propagating OFDM-RoF-based WLAN signals across optical fibers.

Adaptive management of technical condition of power transformers Vladimir M. Levin; Ammar A. Yahya
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment.

Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network Md. Kamrul Hossain; Md. Mokammel Haque
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.

Novel holistic architecture for analytical operation on sensory data relayed as cloud services Manujakshi B. C; K. B. Ramesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

Random forest application on cognitive level classification of E-learning content Benny Thomas; Chandra J.
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with Random Forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.

A comprehensive study on disease risk predictions in machine learning G. Saranya; A. Pravin
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted.

Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features Henry Castro; Juan David Garcia-Racines; Alvaro Bernal-Noreña
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.

A high security and noise immunity of speech based on double chaotic masking Ehab AbdulRazzaq Hussein; Murtadha K. Khashan; Ameer K. Jawad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

It is known that increasing the security of the information and reducing the noise effect through public channels are two of the main priorities in developing any communication system. In this article, an efficient, secure communication system with two levels of encryption has been applied to the speech signal. The suggested security approach was implemented by using two different stages of chaotic masking on the signal; one masking was conducted by using Lorenz system and the other masking was built by using Rӧssler chaotic flow system. The main goal of developing this two-chaotic masking approach is to increase the key space and the security of the information. Also, an immunity technique has been implemented in the suggested approach to reduce the noise effect. For practical application purposes, this system was tested with additive white gaussian noise (AWGN) channel. The simulation results show that the quality of reconstructed speech signal is changeable according to the used signal to noise ratio (SNR); therefore, a proposed technique based on digital processing method (DPM) was applied to the first masked signal by converting the sampled data from the analog to the binary format. The simulation results show that an 22 dB (SNR) is sufficient to recover the speech signal with minimum noise by using the suggested approach.

Dual output DC-DC quasi impedance source converter Muhammad Ado; Awang Jusoh; Tole Sutikno; Mohd Hanipah Muda; Zeeshan Ahmad Arfeen
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i4.pp3988-3998

A double output port DC-DC quasi impedance source converter (q-ZSC) is proposed. Each of the outputs has a different voltage gain. One of the outputs is capable of bidirectional (four-quadrant) operation by only varying the duty ratio. The second output has the gain of traditional two-switch buck-boost converter. Operation of the converter was verified by simulating its responses for different input voltages and duty ratios using MATLAB SIMULINK software. Its average steady-state output current and voltage values were determined and used to determine the ripples that existed. These ripples are less than 5% of the average steady-state values for all the input voltage and duty ratio ranges considered.

Maximum voltage sag compensation using direct converter by modulating the carrier signal S. Abdul Rahman; Gebrie Teshome
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The aim of this paper is to achieve maximum voltage sag compensation of 52% using direct converter based DVR. The DVR topology has only two switches. The DVR is designed to compensate the voltage sag in a phase by taking power from the same phase. A direct converter is connected between the series transformer and the line in which sag compensation is to be achieved. If the actual amplitude of the error signal is used and the amplitude of carrier signal is kept at 1 unit, it is possible to achieve only 22% of sag compensation. If the amplitude of the carrier signal is modulated according to the percentage of existing sag, 52% of the sag is compensated through ordinary PWM technique with the THD less than 5%. Matlab Simulation results are presented for the validating the analysis.

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Tole Sutikno

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ijece@iaesjournal.com

Editorial Address
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Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

Phone -

Journal Mail Official ijece@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

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Abstract

Contact Name
Tole Sutikno

Contact Email
ijece@iaesjournal.com

Phone
-

Journal Mail Official
ijece@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA