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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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Development of intelligent protection and automation control systems using fuzzy logic elements Ansabekova, Gulbakyt; Sarsikeyev, Yermek; Abdimuratov, Zhubanyshbai; Appakov, Nurbol; Kaliyev, Zhanybek; Umurzakova, Anara; Sarbassova, Nurbanu; Zhumatova, Assel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp556-565

Abstract

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.
Human movement detection and classification capabilities using passive Wi-Fi based radar Razali, Hidayatusherlina; Abd Rashid, Nur Emileen; Nasarudin, Muhammad Nazrin Farhan; Ismail, Nor Najwa; Ismail Khan, Zuhani; Enche Ab Rahim, Siti Amalina; Megat Ali, Megat Syahirul Amin; Zakaria, Nor Ayu Zalina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3545-3556

Abstract

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%.
Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic Nguyen Duc, Dien; Vu Viet, Thong
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2377-2385

Abstract

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.
Optical laser-generated electricity for powering tilt-meter sensor Nelfyenny, Nelfyenny; Bayuwati, Dwi; Suryadi, Suryadi; Husdi, Irwan Rawal; Mulyanto, Imam; Prasetio, Aditya Dwi; Irawan, Dedi; Widiyatmoko, Bambang; Setiono, Andi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6140-6147

Abstract

This research investigated the feasibility and efficacy of power over fiber (PoF) transmission systems for geotechnical monitoring applications, addressing challenges associated with traditional power transmission methods. Leveraging fiber optic technology, PoF systems offer advantages such as high reliability, minimal signal loss, and immunity to environmental factors. The study presents a detailed design and implementation of a PoF transmission system, integrating a high-power laser source (HPLS) and photovoltaic technology for efficient power transmission over extended distances. Results demonstrate impressive volt-ampere characteristics and conversion efficiencies, with the optimized system configuration achieving a peak power output of 682 mW. Furthermore, the study evaluated the performance of a surface inclinometer sensor powered by the PoF system, showcasing its effectiveness in monitoring soil movements with remarkable stability and consistent power supply. Future research directions include scalability studies, optimization of system efficiency, and field deployments to broaden the applicability of PoF technology in geotechnical monitoring, ultimately advancing disaster mitigation and infrastructure resilience efforts.
A trust based secure access control using authentication mechanism for interoperability in internet of things Narayanappa, Shashikala; Narayanareddy Anitha, Tulavanur; Mishra, Priti; Raichur Patil Herakal, Renuka; Kolur, Jayasudha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2262-2273

Abstract

The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Single phase robustness variable structure load frequency controller for multi-region interconnected power systems with communication delays Nguyen, Phan-Thanh; Nguyen, Cong-Trang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5064-5071

Abstract

This paper proposes an estimator-based single phase robustness variable structure load frequency controller (SPRVSLFC) for the multi-region interconnected power systems (MRIPS) with communication delays. The key attainments of this research consist of two missions: i) a global stability of the power systems is guaranteed by removing the reaching phase in traditional variable structure control (TVSC) technique; and ii) a novel output feedback load frequency controller is established based on the estimator tool and output information only. Initially, a single-phase switching function is constructed to disregard the reaching phase in TVSC. Then, an unmeasurable state variable of the MRIPS is estimated by using the proposed estimator tool. Next, a new SPRVSLFC for the MRIPS is suggested based on the support of the estimator tool and output data only. Furthermore, a sufficient constraint is constructed by retaining the linear matrix inequality (LMI) procedure for ensuring the robust stability of motion dynamics in sliding mode. Finally, the performance of interconnected power plant under changed multi-constraints is imitated with the novel control technique to validate the practicability of the plant.
Fire detection using deep learning methods Bayegizova, Aigulim; Abdikerimova, Gulzira; Kaliyeva, Samal; Shaikhanova, Aigul; Shangytbayeva, Gulmira; Sugurova, Laura; Sugur, Zharkynay; Saimanova, Zagira
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp547-555

Abstract

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.
Privacy-preserving reservation model for public facilities based on public Blockchain Basuki, Akbari Indra; Rosiyadi, Didi; Susanto, Hadi; Setiawan, Iwan; Salim, Taufik Ibnu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4418-4429

Abstract

Ensuring fairness in the utilization of government-funded public facilities, such as co-working spaces, sports fields, and meeting rooms, is imperative to accommodate all citizens. However, meeting these requirements poses a significant challenge due to the high costs associated with maintaining digital infrastructure, employee wages, and cybersecurity expenses. Fortunately, Blockchain smart contracts present an economical and secure solution for managing digital infrastructure. They offer a pay-per-transaction schema, immutable transaction records, and role-based data updates. Despite these advantages, public blockchains raise concerns about data privacy since records are publicly readable. To address this issue, this study proposes a privacy-preserving mechanism for public facilities' reservation systems. The approach involves encrypting the reservation table with fully-homomorphic encryption (FHE). By employing FHE with binary masking and polynomial evaluation, the reservation table can be updated without decrypting the data. Consequently, citizens can discreetly book facilities without revealing their identities and eliminating the risk of overlapping schedules. The proposed system allows anyone to verify reservations without disclosing requested data and table contents. Moreover, the system operates autonomously without the need for human administration, ensuring enhanced user privacy.
Optimized decoder for low-density parity check codes based on genetic algorithms El Ouakili, Hajar; El Ghzaoui, Mohammed; El Alami, Rachid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2717-2724

Abstract

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.
Neutrosophic enhanced convolutional neural network for occupancy detection: structured model development and evaluation Mittal, Ranjeeta; Kumar, Suresh; Chugh, Urvashi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6619-6627

Abstract

In this study, we introduce an advanced convolutional neural network (CNN) model tailored for house occupancy detection, designed to accommodate the inherent uncertainties and contradictory information often encountered in sensor data. By integrating neutrosophic layers into the CNN architecture, we enable the model to effectively handle indeterminacy, vagueness, and inconsistency present in real-world sensor readings. Our approach employs neutrosophic convolutional, max-pooling, and logic layers, providing a comprehensive framework for feature extraction and decision-making. Through a structured methodology encompassing data preprocessing, model initialization, training, evaluation, and optimization, we demonstrate the efficacy of the proposed model in accurately detecting occupancy status within residential environments. This enhanced CNN model offers improved accuracy, robustness, and interpretability, thereby facilitating its integration into smart home systems and building automation applications, contributing to enhanced efficiency, comfort, and energy savings.

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