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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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Designing smart pulse flow meters using diversion analysis Pavel S. Micheev; Konstantin A. Muraviev; Elena V. Rezchikova; Kirill V. Selivanov
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.pp1338-1345

Abstract

The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.
Comparative study of optimization algorithms on convolutional network for autonomous driving Fernando Martinez; Holman Montiel; Fredy Martinez
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.pp6363-6372

Abstract

he last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances.
An optimal design of current conveyors using a hybrid-based metaheuristic algorithm Soufiane Abi; 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.pp6653-6663

Abstract

This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
Software aging prediction – a new approach Shruthi Parashivamurthy; Nagaraj Girish Cholli
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.pp1773-1781

Abstract

To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.
A doctor recommender system based on collaborative and content filtering Qusai Y. Shambour; Mahran M. Al-Zyoud; Abdelrahman H. Hussein; Qasem M. Kharma
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.pp884-893

Abstract

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity.
Mobile network connectivity analysis for device to device communication in 5G network Ahmed Laguidi; Tarik Hachad; Lamiae Hachad
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.pp680-687

Abstract

Since long term evolved release 14 (LTE R14), the device to device (D2D) communications have become a promising technology for in-band or out-band mobile communication networks. In addition, D2D communications constitute an essential component of the fifth-generation mobile network (5G). For example, to improve capability communication, reduce the power dissipation, reduce latency within the networks and implement new applications and services. However, reducing the congestion in D2D communications and improving the mobile network connectivity are the essential problems to propose these new applications or services. This paper presents new solutions to reduce the congestion of devices around a base station and improve the performance of the D2D network; in terms of the number of connected devices or user equipment (UE). The simulation results show that our proposed solution can improve the network capacity by doubling the number of connected devices (or UE) and reducing the congestion. For this reason, our proposition makes it possible to reduce the financial cost by reducing the cost of deploying equipment. For example, instead of using two base stations, we can use only one station to connect the same number of devices.
Active vibration control of flexible beam system based on cuckoo search algorithm Aida Nur Syafiqah Shaari; Muhamad Sukri Hadi; Abdul Malek Abdul Wahab; Rickey Ting Pek Eek; Intan Zaurah Mat Darus
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.pp2289-2298

Abstract

A flexible beam is recognized as a lightweight structure that is prone to excessive vibration, resulting in poor performance. Thus, controlling unwanted vibration is necessary to maintain the system’s performance. Therefore, this study presents a technique to suppress undesired vibration in a flexible beam structure by introducing active vibration control (AVC). However, to develop an effective controller, an appropriate flexible beam model must first be obtained. In recent times, one of the best methods employed to model a flexible beam structure is system identification via a swarm intelligence algorithm. In this study, an intelligent algorithm acknowledged as cuckoo search (CS) was acquainted. The capability of the proposed algorithm was verified using three robustness techniques which were correlation test, pole-zero diagrams and mean square error (MSE). The simulation result showed that the CS algorithm achieved superior performance by achieving the lowest MSE of 6.1547x10-9, a correlation test between a 95% confidence level and high stability. Next, a proportional-integral-derivative (PID) controller tuned by the Ziegler-Nichols method was developed using the transfer function accomplished from the CS model. Two types of interference, namely single and multiple sine waves were introduced to validate the effectiveness of the controller. The controller successfully achieved a 30.2 dB of attenuation level for both disturbances.
Optimization of the structure of filter-compensating devices in networks with powerful non-linear power consumers based on fuzzy logic Evgeniy Vitalievich Zhilin; Dmitriy Aleksandrovich Prasol; Nikita Yurievich Savvin
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.pp5730-5737

Abstract

The article presents a solution to the problem of optimizing the structure of filter-compensating devices (FCD) when installed in high-voltage mine networks with powerful nonlinear electrical receivers. The urgency of the problem of choosing a rational structure of the FCD. The problem of choosing the design and installation location of the FCD is presented. The main technical means of compensation of higher harmonics of currents and voltages in high-voltage networks with powerful nonlinear electrical receivers are considered. Analysis of different types of passive filters (PF) and their frequency properties showed that the choice of specific types of PF refers to the multi-criteria optimization problem. The main methods of optimization of FCD design are considered. The variant of FCD construction based on the solution of multi-criteria optimization problem with the use of fuzzy sets is proposed and justified. To this end, the calculation of PF parameters and frequency characteristics of equivalent systems of the "filter-external network" type for four possible combinations of PF is performed. The optimal is a FCD with two resonant PF tuned to the 11th and 13th harmonics, and a second-order broadband PF tuned to compensate harmonics starting from the 23rd and above. The analysis of simulation results showed effective compensation of higher harmonic currents and voltages.
Performance evaluation of dynamic source routing protocol with variation in transmission power and speed Saad Elsayed; Mohammed Ibrahim Youssef
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.pp1795-1802

Abstract

Mobile ad-hoc network (MANET) is a set of mobile wireless nodes (devices) which is not rely on a fixed infrastructure. In MANETs, each device is responsible for routing its data according to a specific routing protocol. The three most common MANET routing protocols are: dynamic source routing protocol (DSR), optimized link state routing protocol (OLSR), and ad-hoc on-demand distance vector (AODV). This paper proposes an efficient evaluation of DSR protocol by testing the MANETs routing protocol with variation in transmission power at different speeds. The performance analysis has been given using optimized network engineering tools (OPNET) modeler simulations and evaluated using metrics of average end to end delay and throughput. The results show that the throughput increases as the transmission power increases up to a certain value after which the throughput decreases, also the network work optimally at a certain transmission power which varied at different speed.
Selective local binary pattern with convolutional neural network for facial expression recognition Syavira Tiara Zulkarnain; Nanik Suciati
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.pp6724-6735

Abstract

Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.

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