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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 95 Documents
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Search results for , issue "Vol 11, No 2: April 2021" : 95 Documents clear
New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm Brahim Ait Benali; Soukaina Mihi; Ismail El Bazi; Nabil Laachfoubi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1485-1497

Abstract

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.
Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack Arnold Adimabua Ojugo; Rume Elizabeth Yoro
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1498-1509

Abstract

Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.
Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis Abdallah Zahidi; Said Amrane; Nawfel Azami; Naoual Nasser
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1697-1708

Abstract

As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.
Partially isolated four port converter with combined PWM and secondary phase shift control G. Ranipriya; R. Jegatheesan; K. Vijayakumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1086-1094

Abstract

A partially isolated four-port converter is proposed in this paper for interfacing two renewable sources and a storage device with an isolated load. This converter is capable of achieving high power density because of the effective sharing of devices among the input ports. Combined PWM and secondary phase shift control is employed to have a decoupled power flow management of input and output side ports. PWM control is used at the input side for maximum power tracking of renewable sources and battery power management. At the output side, secondary Phase shift control is used for controlling the output voltage. The adopted secondary phase shift control allows the primary switching legs to be operated with 1800 phase shift which results in reduced current ripple at input ports. The working principle of the converter, its output characteristics and control strategy are discussed. Working of the converter and its control strategy is verified through simulation for different input and output conditions. Further, to validate the simulation results, the experimental results of a 500W prototype are also provided.
Speech emotion recognition based on SVM and KNN classifications fusion Mohammed Jawad Al Dujaili; Abbas Ebrahimi-Moghadam; Ahmed Fatlawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1259-1264

Abstract

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.
Ultra-wide band energy harvesting for ultra-low power electronics applications Nasr Rashid; Mohamed Shehata
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1158-1165

Abstract

In this work, the feasibility of energy harvesting in the useful UWB band (i.e., 3.1-10.6 GHz) is analytically investigated. A typical UWB communications/EH chain in this band is modeled and analyzed, considering the spectral constraints imposed by the federal communications commission (FCC) to UWB signaling. Based on the developed model, accurate analytical expressions are derived for the average received powers of two common types of impulse radio UWB (IR-UWB) signaling waveforms. Numerical simulations on the system-level show excellent agreement with the obtained analytical expressions. Moreover, the DC power levels expected from spectrally constrained IR-UWB waveforms are extremely low (less than 0.3 microwatt) and, accordingly, provide useful guidelines for the design and development of ULP electronics applications in the sub-microwatt range.
Improvement the voltage stability margin of Iraqi power system using the optimal values of FACTS devices Ghassan Abdullah Salman; Hatim G. Abood; Mayyadah Sahib Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp984-992

Abstract

The detection of potential voltage collapse in power systems is essential to maintain the voltage stability in heavy load demand. This paper proposes a method to detect weak buses in power systems using two stability indices: the voltage stability margin factor (dS/dY) and the voltage collapse prediction index (VCPI). Hence, the paper aims to improve the voltage stability of Iraqi transmission grid by allocating FACTS devices in the optimal locations and optimal sizes. Two types of FACTS are used in this paper which are Thyristor controlled series compensator (TCSC) and static var compensator (SVC). The objective function of the problem is fitted using particle swarm optimization (PSO). The proposed method is verified using simulation test on Diyala-132 kV network which is a part of the Iraqi power system. The results observed that improvement the voltage stability margin, the voltage profile of Diyala-132 kV is increased and the power losses is decreased.
Hybrid energy storage system control analogous to power quality enhancement operation of interlinking converters Salman Hajiaghasi; Ahmad Salemnia; Mohsen Hamzeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp909-918

Abstract

Increasing nonlinear loads and power electronic converters lead to various power quality issues in microgrids (MGs). The interlinking converters (ILCs) can participate in these systems to harmonic control and power quality enhancement. However, ILC participation deteriorates the dc link voltage, system stability, and storage lifetime due to oscillatory current phenomena. To address these problems, a new control strategy for a hybrid energy storage system (HESS) is proposed to eliminate the adverse effects of the harmonic control operation of ILC. Specifically, battery and super-capacitor (SC) are used as HESSs that provide low and high power frequency load, respectively. The proposed strategy tries to compensate the current oscillation imposed by ILC with fuzzy control of HESS. In this method, a proportional-resonant (PR) controller integrated with harmonic compensator (HC) is employed to control the ILC for power quality enhancement and oscillatory current elimination. The main advantages of the proposed strategy are to reduce DGs power fluctuations, precise DC bus voltage regulation for generation and load disturbances, improved grid power quality under nonlinear load and transition conditions. The performance of the proposed method for isolated and grid-connected modes is verified using simulation studies in the MATLAB software environment.
Machine learning model for clinical named entity recognition Ravikumar J.; Ramakanth Kumar P.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1689-1696

Abstract

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manner
Observer-based tracking control for single machine infinite bus system via flatness theory Mohammad Pourmahmood Aghababa; Bogdan Marinescu; Florent Xavier
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1186-1199

Abstract

In this research, we aim to use the flatness control theory to develop a useful control scheme for a single machine connected to an infinite bus (SMIB) system taking into account input magnitude and rate saturation constraints. We adopt a fourth-order nonlinear SMIB model along an exciter and a turbine governor as actuators. According to the flatness-based control strategy, first we show that the adopted nominal SMIB model is a flat system. Then, we develop a full linearizing state feedback as well as an outer integral-type loop to ensure suitable tracking performances for the power and voltage as well as the angular velocity outputs. We assume that only the angular velocity of the generator is available to be measured. So, we provide a linear Luenberger observer to estimate the remaining states of the system. Also, the saturation nonlinearities are transferred to the linear part of the system and they are canceled out using their estimations. The efficiency and usefulness of the proposed observer-controller against faults are illustrated using simulation tests in Eurostag and Matlab. The results show that the clearing critical time of the introduced methodology is larger than the classical control approaches and the proposed observer-based flatness controller exhibits over much less control energy compared to the classic IEEE controllers.

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