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International Journal of Electrical and Computer Engineering
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 111 Documents
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Search results for , issue "Vol 12, No 2: April 2022" : 111 Documents clear
On the applicability of numerical tools for simulating wave-ports close to the cutoff frequency Eman Mohamed Eldesouki; Khalid Mustafa Ibrahim; Ahmed Mohmed Attiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1724-1731

Abstract

This paper focuses on a common drawback in electromagnetic numerical computer aided design computer aided design (CAD) tools: high frequency structure simulator (HFSS), computer simulation technology (CST) and FEKO, where the excitation by using a wave-port below and close to the cutoff frequency has unreliable values for the reflection coefficient. An example for such problem is presented in the design of a dual horn antenna fed by two different waveguide sections. To overcome this numerical error in the results of these CAD tools, a tapered waveguide section is used in the simulation as an excitation mechanism to the feeding waveguide. The cross section of the input port at this tapered waveguide section is designed to have a cutoff frequency smaller than the lowest frequency under investigation for the original problem. Then, by extracting the effect of the tapered section from the obtained reflection coefficient, it would be possible to obtain the reflection coefficient of the original problem.
Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning Jovi D’Silva; Uzzal Sharma
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1990-2000

Abstract

Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.
Determining customer limits by data mining methods in credit allocation process Tuğçe Ayhan; Tamer Uçar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1910-1915

Abstract

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.
Discrete interferences optimum beamformer in correlated signal and interfering noise Satyanand Singh; Sajai Vir Singh; Dinesh Yadav; Sanjay Kumar Suman; Bhagyalakshmi Lakshminarayanan; Ghanshyam Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1732-1743

Abstract

This paper introduces a significant special situation where the noise is a collection of D-plane interference signals and the correlated noise of D+1 is less than the number of array components. An optimal beamforming processor based on the minimum variance distortionless response (MVDR) generates and combines appropriate statistics for the D+1 model. Instead of the original space of the N-dimensional problem, the interference signal subspace is reduced to D+1. Typical antenna arrays in many modern communication networks absorb waves generated from multiple point sources. An analytical formula was derived to improve the signal to interference and noise ratio (SINR) obtained from the steering errors of the two beamformers. The proposed MVDR processor-based beamforming does not enforce general constraints. Therefore, it can also be used in systems where the steering vector is compromised by gain. Simulation results show that the output of the proposed beamformer based on the MVDR processor is usually close to the ideal state within a wide range of signal-to-noise ratio and signal-to-interference ratio. The MVDR processor-based beamformer has been experimentally evaluated. The proposed processor-based MVDR system significantly improves performance for large interference white noise ratio (INR) in the sidelobe region and provide an appropriate beam pattern.
Bandwidth enhancement of dual-band bi-directional microstrip antenna using complementary split ring resonator with defected structure for 3/5 GHz applications Charernkiat Pochaiya; Srawouth Chandhaket; Prapan Leekul; Jhirat Mearnchu; Tanawut Tantisopharak; Thunyawat Limpiti
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1683-1694

Abstract

This paper presents a bandwidth enhancement of a dual-band bi-directional rectangular microstrip patch antenna. The novelty of this work lies in the modification of conventional rectangular microstip patch antenna by using the combination of two techniques: a complementary split ring resonator (CSRR) and a defected patch structure (DPS). The structure of antenna was studied and investigated via computer simulation technology (CST). The dimension and position of CSRR on the ground plane was optimized to achieve dual bandwidth and bi-directional radiation pattern characteristics. In addition, the bandwidths were enhanced by defecting suitable shape incorporated in the microstrip patch. A prototype with overall dimension of 70.45×63.73 mm2 has been fabricated on FR-4 substrate. To verify the proposed design, the impedance bandwidth, gain, and radiation patterns were carried out in measurements. The measured impedance bandwidths were respectively 560 MHz (3.08-3.64 GHz) and 950 GHz (4.64-5.59 GHz) while the measured gains of each bandwidth were respectively 4.28 dBi and 4.63 dBi. The measured radiation patterns were in good agreement with simulated ones. The proposed antenna achieves wide dual bandwidth and bi-directional radiation patterns performances. Consequently, it is a promising candidate for Wi-Fi or 5G communications in specific areas such as tunnel, corridor, or transit and rail.
An analysis between exact and approximate algorithms for the k-center problem in graphs Velin Kralev; Radoslava Kraleva; Viktor Ankov; Dimitar Chakalov
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2058-2065

Abstract

This research focuses on the k-center problem and its applications. Different methods for solving this problem are analyzed. The implementations of an exact algorithm and of an approximate algorithm are presented. The source code and the computation complexity of these algorithms are presented and analyzed. The multitasking mode of the operating system is taken into account considering the execution time of the algorithms. The results show that the approximate algorithm finds solutions that are not worse than two times optimal. In some case these solutions are very close to the optimal solutions, but this is true only for graphs with a smaller number of nodes. As the number of nodes in the graph increases (respectively the number of edges increases), the approximate solutions deviate from the optimal ones, but remain acceptable. These results give reason to conclude that for graphs with a small number of nodes the approximate algorithm finds comparable solutions with those founds by the exact algorithm.
A unified ontology-based data integration approach for the internet of things Ahmed Swar; Ghada Khoriba; Mohamed Belal
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2097-2107

Abstract

Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.
A binary particle swarm optimization approach for power system security enhancement Padmanabha Raju Chinda; Ragaleela Dalapati Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1929-1936

Abstract

Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. Generation re-dispatching is answer for the evacuation of line overloads. The issue is the minimization of different goals viz. minimization of fuel cost, minimization of line loadings and minimization of overall severity index. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. The positioning is assessed utilizing fuzzy logic. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives.
Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method Vladimir Mikhailovich Levin; Ammar Abdulazez Yahya; Diana A. Boyarova
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1139-1146

Abstract

Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises.
Robust cepstral feature for bird sound classification Murugaiya Ramashini; P. Emeroylariffion Abas; Kusuma Mohanchandra; Liyanage C. De Silva
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1477-1487

Abstract

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management.

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