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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 112 Documents
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Search results for , issue "Vol 12, No 4: August 2022" : 112 Documents clear
An efficient application of particle swarm optimization in model predictive control of constrained two-tank system Ahmad Kia Kojouri; Javad Mashayekhi Fard
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3540-3550

Abstract

Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region by increasing the prediction horizon exists. This causes the increment in computation complexity of control law. In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine. The solution of the constrained final time-optimal control problem (CFTOC) is derived, and then the PSO algorithm is used to reduce the computational complexity of the control law and set the physical parameters of the system to improve performance simultaneously. On the other hand, a new combined algorithm based on PSO is going to be used to reduce the complexity of explicit MPC-based solution CFTOC of the two-tank system; consequently, that the number of polyhedral is minimized, and system performance is more desirable simultaneously. The proposed algorithm is applied in simulation and our desired subjects are reached. The number of control law polyhedral reduces from 42 to 10 and the liquid height in both tanks reaches the desired certain value in 189 seconds. Search time and apply control law in 25 seconds.
Joint digital pre-distortion model based on Chebyshev expansion Elham Majdinasab; Abumoslem Jannesari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3781-3791

Abstract

In this paper, a new low complexity model is proposed for the joint digital pre-distortion of in-phase/quadrature-phase (I/Q) imbalance, local oscillator (LO) leakage, and power amplifier nonlinearity in direct-conversion transmitters (DCTs). In this structure, we proposed a set of orthogonal basis functions based on Chebyshev expansion to attenuate the problem of numerical instability created during the conventional model identification method. This robust joint digital pre-distortion (DPD) utilized the indirect learning architecture and updated the coefficients vector based on the recursive least square (RLS) algorithm. To verify the operation and efficiency of the proposed model, an extensive simulation in MATLAB was carried out. The results showed a significant reduction in the conditional number and the coefficient dispersion of the observation matrix. Furthermore, the power of the signal in the adjacent channel decreased by more than 16 dB for the orthogonal frequency division multiplexing (OFDM), 16 QAM input signal. In comparison to the previous digital pre-distorter models, the proposed DPD builds strong numerical stability with the least coefficients.
A novel multi-resonant and wideband fractal antenna for telecommunication applications Ibrahime Hassan Nejdi; Youssef Rhazi; Mustapha Ait Lafkih; Seddik Bri; Lamsalli Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3850-3858

Abstract

This letter presents the design, simulation, and measurement of a novel multiband fractal circular antenna for wireless applications. In the antenna design, we used a circular antenna where we took a ring. Then, in the first iteration, we added a new ring divided into two of the same size. For the second iteration, we added a ring of the same size after dividing it into two halves. In the third iteration, we added the third ring of the same size after dividing it into four. Due to the resonator defection, we were able to reduce the size of the starting antenna from 60×70×2 mm3 to 50×50×1.6 mm3, to get the frequency of 2.48 GHz, and we generated new bandwidths with a high gain that reaches 5.02 dB. The proposed antenna radiation characteristics, such as the impedance matching, the gain, the radiation pattern, and the surface current distribution are presented and discussed. We find that the simulated and measured results are in acceptable agreement and affirm the good performance of the proposed antenna. The results obtained affirm that the proposed fractal antenna is a better candidate for integration into wireless communication circuits.
Energy management system for distribution networks integrating photovoltaic and storage units Zedak, Chaimae; Belfqih, Abdelaziz; Boukherouaa, Jamal; Lekbich, Anass; Elmariami, Faissal
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3352-3364

Abstract

The concept of the optimization energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic Algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), TOPSIS and entropy-TOPSIS, compared to each other for more accurate results. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
Semantic-based visual emotion recognition in videos-a transfer learning approach Vaijayanthi Sekar; Arunnehru Jawaharlalnehru
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3674-3683

Abstract

Automatic emotion recognition is active research in analyzing human’s emotional state over the past decades. It is still a challenging task in computer vision and artificial intelligence due to its high intra-class variation. The main advantage of emotion recognition is that a person’s emotion can be recognized even if he is extreme away from the surveillance monitoring since the camera is far away from the human; it is challenging to identify the emotion with facial expression alone. This scenario works better by adding visual body clues (facial actions, hand posture, body gestures). The body posture can powerfully convey the emotional state of a person in this scenario. This paper analyses the frontal view of human body movements, visual expressions, and body gestures to identify the various emotions. Initially, we extract the motion information of the body gesture using dense optical flow models. Later the high-level motion feature frames are transferred to the pre-trained convolutional neural network (CNN) models to recognize the 17 various emotions in Geneva multimodal emotion portrayals (GEMEP) dataset. In the experimental results, AlexNet exhibits the architecture's effectiveness with an overall accuracy rate of 96.63% for the GEMEP dataset is better than raw frames and 94% for visual geometry group-19 VGG-19, and 93.35% for VGG-16 respectively. This shows that the dense optical flow method performs well using transfer learning for recognizing emotions.
Breast cancer histological images nuclei segmentation and optimized classification with deep learning Fawad Salam Khan; Muhammad Inam Abbasi; Muhammad Khurram; Mohd Norzali Haji Mohd; M. Danial Khan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4099-4110

Abstract

Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
Advancements in energy storage technologies for smart grid development Sharma, Pankaj; Reddy Salkuti, Surender; Kim, Seong-Cheol
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3421-3429

Abstract

In the modern world, the consumption of oil, coal natural gas, and nuclear energy has been causing by a serious environmental problem and an ongoing energy crisis. The generation and consumption of renewable energy sources (RESs) such as solar and wind tidal, can resolve the problem but the nature of the RESs is fluctuating and intermitted. This evolution brings a lot of challenges in the management of electrical grids. The paper reviewed the advancements in energy storage technologies for the development of a smart grid (SG). More attention was paid to the classification of energy storage technologies based on the form of energy storage and based on the form of discharge duration. The evaluation criteria for the energy storage technologies have been carried out based on technological dimensions such as storage capacity, efficiency, response time, energy density, and power density, the economic dimension such as input cost and economic benefit; and the environmental dimension such as emission and stress on ecosystem, social demission such as job creation and social acceptance were also presented in this paper.
Development and validation of a tool for measuring digital library engagement Rahimi Mohamad Rosman, Mohamad; Nasir Ismail, Mohd; Noorman Masrek, Mohamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4146-4154

Abstract

Digital library engagement can be defined as the extensive usage of the services, functions, and tools provided by the digital library (DL). Although many instruments have been developed to measure digital library usage, however, the instruments were only measuring a limited aspect of usage, such as behavioral perspective. Therefore, the aim of this study is to establish and confirm a research instrument to measure digital library engagement. The study is conducted on several empirical phases. First, a listof variables was selected. Second, an instrument was developed based on the variables selected. Third, the instrument measuring digital library engagement was validated through expert review process. Fourth, face validity was conducted, before confirming the reliability of the instrument through a pilot test. Lastly, the instrument was validated through quantitative data collection by 492 respondents. As a result, a valid instrument consisting of 14 variables underneath 5 dimensions (technological, individual, contextual, digital library engagement, perceived benefits) and 61 items were produced. The instrument could be employed to identify and assess the state of digital library engagement among practitioners, universities, government, and local communities. This study however is limited in few ways in relation to context coverage, generalization of theory, and variables selection.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimization tool Soukaina Mihi; Brahim Ait Ben Ali; Ismail El Bazi; Sara Arezki; Nabil Laachfoubi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4195-4205

Abstract

The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.
Choosing the best quality of service algorithm using OPNET simulation Mohamed Osman Eltaib; Hamoud H. Alshammari; Ammar Boukrara; Karim Gasmi; Olfa Hrizi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4079-4089

Abstract

The concept of quality of service (QoS) is a new computer technology. Previously, there was a slow internet connection to access the sites and it was slow to send information. But now, it requires speeding up the traffic and increasing the efficiency for audio and video. In this study, we discuss the concepts of QoS provided over the network to achieve these goals. This study aims to compare six algorithms to control the QoS, then, the best algorithm will be selected to improve the traffic. These algorithms are named first in first out (FIFO), priority queuing (PQ), custom queuing (CQ), CQ with low latency queuing (LLQ), weighted fair queuing (WFQ), WFQ with low latency queuing (LLQ), so the behavior of these algorithms can be measured. The results obtained by comparing between them using OPNETsimulation show that the best algorithm is the priority queuing algorithm, followed by CQ, then CQ with LLQ, then WFQ, then WFQ with LLQ and finally FIFO. All these results are plotted in the form of graphs to show the paths of these algorithms for the single state with an operation time of 5 minutes for each algorithm.

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