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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 96 Documents
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Search results for , issue "Vol 11, No 5: October 2021" : 96 Documents clear
Switched capacitor based multi-level boost inverter for smart grid applications Mostafa Al gabalawy; Ramy M. Hossam; Shimaa A. Hussien; Nesreen S. Hosny
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3772-3781

Abstract

To link DC power sources to an AC grid, converters are needed. Inverters are the power electronic devices, which are used for this purpose. Conventional inverters employ harmonic filters and transformers that are lossy and expensive. Multilevel inverters (MLIs) are an alternative to conventional ones, proposing reduced total harmonic distortion (THD), increased range of control, and inductor-less design. They generate a stepped waveform, with close similarity to a sine wave. Many distributed sources may be employed in a smart grid. If those sources have minimal THD, the filtering process could be reduced at the point of common coupling. This paper presents two switched capacitor based MLIs, proposing boost capability and low THD. Inverters have inherent charge balancing capability, which eliminates the need for auxiliary circuits and voltage sensors. Inverters switches are modulated using phase opposition disposition pulse-width modulation (PODPWM) method that ease the balancing of the voltage and decrease the losses of switching. Designs were verified by simulation and the output waveforms were introduced.
Online multiclass EEG feature extraction and recognition using modified convolutional neural network method Haider Abdulkarim; Mohammed Z. Al-Faiz
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4016-4026

Abstract

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy
Evaluation of electrical load estimation in Diyala governorate (Baaquba city) based on fuzzy inference system Siraj Manhal Hameed; Hayder Khaleel AL-Qaysi; Ali Sachit Kaittan; Mohammed Hasan Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3757-3762

Abstract

The evaluation of electrical load estimation is requisitely of any electrical power system. This manner is needed for system obligation, economical distribution and maintenance time of electrical system. In this paper, we propose electrical load estimation method based on fuzzy inference system which gives accurate results for estimated loads in Iraq (Diyala governorateBaaquba city). And it can assist the electrical generation and distribution system that depends on important parameters (temperature, humidity and the speed of the wind). By considering the parameters temperature, humidity and the speed of the wind. These parameters are applied as inputs to the fuzzy logic control system to obtain the normalize estimated load as output by electing membership functions. It is exceptionally valuable to form a choice by taking into consideration these assessed readings that come to from the proposed FIS that displayed in this paper with precision of 0.969 from the real stack request.
Forecasting number of vulnerabilities using long short-term neural memory network Mohammad Shamsul Hoque; Norziana Jamil; Nowshad Amin; Azril Azam Abdul Rahim; Razali B. Jidin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4381-4391

Abstract

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
Learning games creation: IMIE model Ali Abarkan; Abderrahim Saaidi; Majid Ben Yakhlef
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4373-4380

Abstract

In this article, we propose a new model for the learning games design, which aims to enrich and solve the problems of existing models. From the work carried out around the models of educational games, we carefully identify a series of steps to follow, taking into account the simplicity, ease of adaptation and the integration of several factors interest such as the integration of a set sub-models treated pedagogically define the pedagogical side in the game, also the distribution of the intervening necessary for each of the stages to ensure better collaboration between them, for the purpose to helping the creators to trace their path from the start of the creation, in order to achieve the best example of games with an adequate oscillation pedagogical-fun, that will meet the requirements of existing classical teaching methods.
Efficiency of LSB steganography on medical information Oluwakemi Christiana Abikoye; Roseline Oluwaseun Ogundokun
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4157-4164

Abstract

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.
Predicting stock price movement using effective Thai financial probabilistic lexicon Surinthip Sakphoowadon; Nawaporn Wisitpongphan; Choochart Haruechaiyasak
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4313-4324

Abstract

Predicting stock price fluctuation during critical events remains a big challenge for many researchers because the stock market is extremely vulnerable and sensitive during such time. Most existing works rely on various numerical data of related factors which can impact the stock price direction. However, very few research papers analyzed the effect of information appearing in financial news articles. In this paper, a novel probabilistic lexicon based stock market prediction (PLSP) algorithm is proposed to predict the direction of stock price movement. Our approach used the proposed thai financial probabilistic lexicon (ThaiFinLex) derived from Thai financial news and stock market historical prices. The PLSP development consists of three steps. Firstly, we constructed ThaiFinLex by extracting event terms from news articles and calculating their associated probability of increasing/decreasing values of stock prices. Then, event terms with bad prediction performance were filtered out. Finally, the stock price directions were predicted using the PLSP and the remaining effective event terms. Our results indicated that the proposed model can be used for predicting stock price movement. The performance is as high as 83.33% when PLSP is used to predict stocks from the financial sector.
Fuzzy super twisting algorithm dual direct torque control of doubly fed induction machine Boumaraf farid; Boutabba Tarek; Belkacem Sebti
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3782-3790

Abstract

This paper proposes the fundamental aspects of hybrid nonlinear control which is composed of the super twisting algorithm (STA) based second order sliding mode control applying fuzzy logic method (FSOSMC), with pertinent simulation results for a doubly fed induction machine (DFIM) drive. To minimize chattering effect phenomenon due to Signum function employed in sliding mode algorithm, a new method is proposed. This technique consists in replacing the signum function by fuzzy switching function in the SOSMC to minimize flux and torque ripples. This FSOSMC is associated to the double direct torque control DDTC of the doubly fed induction machine (DFIM) by combining the advantages of fuzzy logic (FL) and the advantages of super-twisting sliding mode. The FSOSMC-DDTC strategy is compared with a PI-DDTC and SOSMC-DDTC. Simulation results demonstrate good efficiency and excellent robustness of the hybrid nonlinear controller.
Simulation model of PID for DC-DC converter by using MATLAB Salam Waley Shneen; Dina Harith Shaker; Fatin Nabeel Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3791-3797

Abstract

The change in loads in most applications whose source of nutrition is a renewable energy system. Renewable energy systems can change according to climatic conditions. To control and control these changes, the use of conventional control systems such as PIDs. The PID is one of the most common and used conventional control systems that have been chosen to output the type of power electronic devise (DC-DC converter) in different working conditions. The current study aims to improve the system performance through simulation. Simulation results demonstrate the effectiveness of the system with the controller based on setting parameters such as recording system states, embedded elevation time and transient response.
Advanced deep flux weakening operation control strategies for IPMSM Pham Quoc Khanh; Ho Pham Huy Anh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3798-3808

Abstract

This paper proposes an advanced flux-weakening control method to enlarge the speed range of interior permanent magnet synchronous motor (IPMSM). In the deep flux weakening (FW) region, the flux linkage decreases as the motor speed increases, increasing instability. Classic control methods will be unstable when operating in this area when changing load torque or reference speed is required. The paper proposes a hybrid control method to eliminate instability caused by voltage limit violation and improve the reference velocity-tracking efficiency when combining two classic control methods. Besides, the effective zone of IPMSM in the FW is analyzed and applied to enhance stability and efficiency following reference velocity. Simulation results demonstrate the strength and effectiveness of the proposed method.

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