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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 6,301 Documents
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Prediction of recovery energy from ultimate analysis of waste generation in Depok City, Indonesia Mega Muitiara Sari; Iva Yenis Septiariva; Eva Nur Fauziah; Kuntum Khoiro Ummatin; Qurrotin Ayunina Maulida Okta Arifianti; Niswatun Faria; Jun-Wei Lim; I Wayan Koko Suryawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1-8

Abstract

Refuse derived fuel (RDF) is an environmentally friendly renewable fuel developed to reduce waste generation. RDF can consist of various kinds of waste such as paper and gardens. One of the critical parameters is the chemical element and calorific value. The purpose of this study was to determine the potential for waste reduction and the relationship of ultimate longevity in RDF to the calorific value. This study's paper and garden waste mixture were P0 (100% paper), P25 (75% paper and 25% garden), P50 (50% paper and 50% garden), P75 (25% paper and 75% garden), and P100 (100% garden). The calorific value of the mixture can reach 3.6-5.2 kWh/kg. Simultaneously the relationship of ultimate elements nitrogen (N), hydrogen (H), oxygen (O), and ash affects the heating value of RDF. Sampling the application in Depok City can reduce waste by 6.67%, with the potential for electrical energy from paper and garden wastes of 358,903.8 kWh and 48,681 kWh, respectively. This shows that this energy waste can supply 0.1% of the total daily electricity demand in Depok City.
An intelligent humidity control system for mushroom growing house by using beam-switching antennas with artificial neural networks Prapan Leekul; Thunyawat Limpiti; pornpimon Chaisaeng
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp549-560

Abstract

An automatic humidity control system for mushroom growing house based on the free-space technique is presented. The novelty of this work is the modified free-space technique by measuring the amplitude only of transmission coefficient |S21| that reflected from mushroom by using beam-switching antenna with artificial neural networks (ANNs) as a humidity sensor to control quantity and time of water misting nozzle. In the proposed system, the antenna is designed to act as the transmitting antenna at the frequency of 2.45 GHz. Its radiation patterns can be switched to 4 directions covering all corners of mushroom growing house. The measured |S21| from each direction are converted to direct current (DC) voltage by a radio frequency (RF) detector; then are trained with ANNs in the humidity range of 60-85%. The optimized ANNs structure consists of 4 input nodes, two layers of 5 hidden nodes, and 3 output nodes. To verify the proposed system, experiments were set up in controlled humidity mushroom growing house at the humidity level of 75-80% for 120 hours. The results showed that there was slightly average standard deviation (S.D.) of humidity level 1.36. Consequently, the performance of sensor system assures that it is able to apply for humidity control in large growing house.
Grouping based radio frequency identification anti-collision protocols for dense internet of things application Nnamdi H. Umelo; Nor K. Noordin; Mohd Fadlee A. Rasid; Tan K. Geok; Fazirulhisyam Hashim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5848-5860

Abstract

Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.
Classification of heterogeneous Malayalam documents based on structural features using deep learning models Bipin Nair Balakrishnan Jayakumari; Amel Thomas Kavana
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp894-901

Abstract

The proposed work gives a comparative study on performance of various pretrained deep learning models for classifying Malayalam documents such as agreement documents, notebook images, and palm leaves. The documents are classified based on their visual and structural features. The dataset was manually collected from different sources. The method of research proceeds with preprocessing, feature extraction, and classification. The proposed work deals with three fine-tuned deep learning models such as visual geometry group-16 (VGG-16), convolutional neural network (CNN) and AlexNet. The models attained high accuracies of 99.7%, 96%, and 95%, respectively. Among the three models, the fine-tuned VGG-16 model was found to perform better attaining a very high accuracy on the dataset. As a future work, methods to classify the documents based on content as well as spectral features can be developed.
Recommender systems: a novel approach based on singular value decomposition Francesco Colace; Dajana Conte; Massimo De Santo; Marco Lombardi; Beatrice Paternoster; Domenico Santaniello; Carmine Valentino
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6513-6521

Abstract

Due to modern information and communication technologies (ICT), it is increasingly easier to exchange data and have new services available through the internet. However, the amount of data and services available increases the difficulty of finding what one needs. In this context, recommender systems represent the most promising solutions to overcome the problem of the so-called information overload, analyzing users' needs and preferences. Recommender systems (RS) are applied in different sectors with the same goal: to help people make choices based on an analysis of their behavior or users' similar characteristics or interests. This work presents a different approach for predicting ratings within the model-based collaborative filtering, which exploits singular value factorization. In particular, rating forecasts were generated through the characteristics related to users and items without the support of available ratings. The proposed method is evaluated through the MovieLens100K dataset performing an accuracy of 0.766 and 0.951 in terms of mean absolute error and root-mean-square error.
Design and miniaturization of a microsystem to power biomedical implants using grey wolf optimizer-based cuckoo search algorithm Brahim Ouacha; Hamid Bouyghf; Mohammed Nahid; Said Abenna
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1329-1337

Abstract

One of the greatest techniques, inductive coupling is frequently utilized in the biomedical sector for wireless energy transfer to implants. The aim of this article is to develop and analyze the effect of inductor geometrical characteristics, distance between transmitter (TX) and receiver (RX) and also the operating frequency on the wireless power transfer system, using grey wolf optimizer-based cuckoo search (GWO-CS) algorithm. Power transfer efficiency (PTE), power provided to load, and other critical components must all be improved or maximized and miniaturaze the microsystem proposed. The invention, design, and optimization of coils square spirals in a wireless energy transfer system using a resonant inductive link are the emphasis of this paper. The GWO-CS approach is evaluated to existing methods, demonstrated by simulations and to demonstrate the effectiveness of the suggested strategy.
Optimized reduction approach of congestion in mobile ad hoc network based on Lagrange multiplier Marwa K. Farhan; Muayad S. Croock
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6341-6349

Abstract

Over the past decades, computer networks have experienced an outbreak and with that came severe congestion problems. Congestion is a crucial determinant in the delivery of delay-sensitive applications (voice and video) and the quality of the network. in this paper, the Lagrangian optimization rate, delay, packet loss, and congestion approach (LORDPC) are presented. A congestion avoidance routing method for device-to-device (D2D) nodes in an ad hoc network that addresses the traffic intensity problem. The method of Lagrange multipliers is utilized for active route election to dodge heavy traffic links. To demonstrate the effectiveness of our proposed method, we applied extensive simulation that presents path discovery and selection. Results show that LORDPC decreases delay and traffic intensity while maintaining a high bitrate and low packet loss rate and it outperformed the ad hoc on-demand distance vector (AODV) protocol and the Lagrangian optimization rate, delay, and packet loss, approach (LORDP).
Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques Imad El hajjami; Bachir Benhala
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6320-6331

Abstract

In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm Reham H. Mohammed; Mohamed E. Aboelmorsy; Basem E. Elnaghi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1449-1462

Abstract

Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.
Linear matrix inequalities tool to design predictive model control for greenhouse climate Ayoub Moufid; Noureddine Boutchich; Najib Bennis
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp258-269

Abstract

Modeling and regulating the internal climate of a greenhouse have been a challenge as it is a complex and time variant system. The main goal is to regulate the internal climate considering the difference between nighttime and diurnal phases of the day. To depict the comportment of the greenhouse, a multi model approach based on two multivariable black box models have been proposed representing the diurnal and nighttime phases of the day. The least-squares method is utilized to identify the parameters of these two models based on an experimental collected data. We have shown that these two models are more representative than one model to describe the dynamic behavior of the greenhouse. The second contribution is to control the internal temperature and hygrometry respecting constraints on actuators and controlled variables. For this purpose, a constrained model predictive control scheme based on the multi-modeling approach have been developed. The optimization problem of the control law is transformed to a convex optimization problem includes linear matrix inequalities (LMI). The simulation results show that the adopted control method of indoor climate allows rapid and precise tracking of set points and rejects effectively the external disturbances affecting the greenhouse.

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