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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 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Sliding mode control for the speed loop combined with adaptive coefficients for urban trains’ load variations of Nhon – Hanoi Station Metro line Anh, An Thi Hoai Thu; Cuong, Tran Hung; Dinh, Ha Van
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.pp5030-5037

Abstract

Electric trains are becoming increasingly popular due to their environmental protection and ability to transport a large number of passengers. Alongside this trend, traction motors for electric trains have become diverse thanks to the rapid development of power electronics. Among them, the permanent magnet synchronous motor (PMSM) stands out with advantages such as high efficiency, high torque-to-current ratio, and compactness compared to other motors of the same power, making it the top choice. However, PMSM motors are nonlinear objects, so the nonlinear control technique of sliding mode control has been applied to the speed loop in this paper. Additionally, electric trains' inertial torque and load torque vary due to changes in the number of passengers during peak and off-peak hours and weather conditions. Therefore, this paper introduces two adaptive coefficients to account for these variations. Simulation results show that the sliding mode control technique for the speed loop circuit provides a faster and more accurate speed response. Meanwhile, the two parameters also adapt to the inertial and load torque variations. This ensures the safety and efficiency of the electric train system, contributing to the advantages of this mode of transportation.
A 26 GHz rectenna based on a solar cell antenna for internet of things applications Baccouch, Chokri; Omar, Saleh; Rhaimi, Belgacem C.
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.pp5253-5262

Abstract

This paper presents a new rectenna system that combine a patch antenna with a solar cell to capture energy from both radio frequency (RF) signals and sunlight. The patch antenna collects RF signals, while the solar cell converts sunlight into electricity. This integration offers a sustainable energy solution for internet of things (IoT) sensors or drones. The antenna's performance at 26 GHz demonstrates impressive metrics, including a -68 dB S11 reflection, 700 MHz bandwidth, 6.25 dBi gain, 49.8 Ω impedance, and 42.25% RF-DC conversion efficiency. The "solar rectenna" integrates both technologies, driving technological advancement and fostering sustainability in wireless communication.
Development of a decision-making module in the field of real estate rental using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Ignatovich, Artyom; Bayegizova, Aigulim; Tanirbergenov, Adilbek; Tynykulova, Assemgul; Bapiyev, Ideyat; Mukhamedrakhimova, Galiya
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.pp5430-5442

Abstract

The research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype is an innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.
Conflict-driven learning scheme for multi-agent based intrusion detection in internet of things Attluri, Durga Bhavani; Prabhakara, Srivani
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.pp5543-5553

Abstract

This paper introduces an effective intrusion detection system (IDS) for the internet of things (IoT) that employs a conflict-driven learning model within a multi-agent architecture to enhance network security. A double deep Q-network (DDQN) reinforcement learning algorithm is implemented in the proposed IDS with two specialized agents, the defender and the challenger. These agents engaged in an antagonistic adaptation process that dynamically refined their strategies through continual interaction within a custom-made environment designed using OpenAI Gym. The defender agent aims to identify and mitigate threats by matching the actions of the challenger agent, which is designed to simulate potential attacks in the environment. The study introduces a binary reward mechanism to encourage both agents to explore and exploit different actions and discover new strategies as a response to adversarial actions. The results showcase the effectiveness of the proposed IDS in terms of higher detection rate the comparative analysis also validates the effectiveness of the proposed IDS scheme with an accuracy of approximately 96%, outperforming similar existing approaches.
Developing decision-making serious games using Ren’Py visual novel engine Hafizalshah, Muhammad Hariz; Ghazali, Aimi Shazwani; Sidek, Shahrul Na’im
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.pp5458-5467

Abstract

Serious games are effective tools defined as games designed with a focus on explicit utility rather than the generally construed notion of games purely as a source of entertainment. Decision-making games are a type of serious game that can be developed with the intent of studying behavior, educating, appraising or other similar applications that benefit through the information collected from the decision-making process. Digital versions of serious games are gaining prominence due to a higher level of interactivity and complexity, especially in Human-Agent Interaction (HAI) applications. The development of digital serious games generally extends beyond software developers, typically involving individuals from diverse backgrounds who may not possess the necessary programming skills required for the development process. The paper proposed the use of Ren’Py, an open-source visual novel game engine as a platform to develop decision-making games. The study examined the Ren'Py game engine’s potential through an assessment of the development process for the production of a decision-making serious game. Findings showed that Ren’Py satisfies the need for a relatively easy-to-develop platform for decision-making-based serious games due to its built-in systems that conform to currently applied serious decision-making game design principles.
Improvement of Philips MOS model 9 radio frequency performance with circuit level parasitic compensation Gadige, Aswini Kumar; Paremesha, Paremesha
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.pp4977-4986

Abstract

The two circuit-level parasitic compensation techniques for the Philips metal oxide semiconductor (MOS) model 9 metal oxide semiconductor field effect transistor (MOSFET) at high frequencies (in the GHz range) are presented in this paper. The first method involves connecting the series resonant LC circuit in parallel to the drain and grounded source/bulk of the MM9; the second method involves connecting two of these MM9s in parallel to increase the drain current at higher frequencies along with parasitic compensation. Using these compensatory techniques, it is possible to reduce the impact of drain-source parasitic capacitance on MOS model 9 by preventing the short circuit of MOSFET terminals at high frequencies. After adjustment, improvements were seen in a number of metrics, including output impedance, S-parameters, output power and stability. Finally, using a 10 dBm source power, these parasitic compensation techniques are applied to a single and two stage basic class-E power amplifier and simulated at 1.7 and 1.1 GHz, respectively. Improvements are noted in multiple performance parameters, including power Gain (16.5 dB), drain Efficiency (83%), power added efficiency (85.82%), output power (26 dBm), good Stability (K=2.23, B>0), and S-parameters (S11=-9.22 dB, S12=-39.78 dB, S21=16.38 dB, and S22=1.41 dB) in two-stage cascade power amplifier.
Reinforcement learning-empowered resource allocation with multi-head attention mechanism in V2X networks Khan, Irshad; Haladappa, Manjula Sunkadakatte
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.pp5691-5700

Abstract

Intelligent transport systems (ITS) offer safe and autonomous service in vehicular applications. The vehicle to everything (V2X) network aids in performing communication between any vehicle to other entities such as networks, pedestrians or other objects. However, the allocation of power in the V2X network is still seen as a challenging task in recent resource allocation approaches. So, multi-head attention mechanism with reinforcement learning (MHAMRL) is utilized in resource allocation. This work considers real traffic scenes in highway traffic model and wireless transmission model. Specifically, in the mode 4 cellular V2X, every individual vehicle is considered as a resource which does not rely on the base station for resource allocation. Vehicle users are classified into V2I or V2V links based on the varied service requirements of V2X. The combination of multi-head attention mechanism sequences the signal with minimal noises which diminishes the energy consumption and improves channel gain. In the velocity range of 20-25 m/s, the proposed approach achieves a sum rate of 53 Mb/s, surpassing the 50 Mb/s achieved by the existing multi-agent deep reinforcement learning-based attention mechanism (AMARL) algorithm.
Geographic information system-based spatio-temporal detection and mapping of COVID–19 hot/cold spots in Oman Al-Mulla, Yaseen; Al–Muqaimi, Mohammed; Ali, Ahsan; Al-Badi, Taif; Parimi, Krishna; Chowdary, Anusha
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.pp5779-5801

Abstract

Infected COVID-19 patients, especially after March 11, 2020, grew drastically in Oman. Hence, a variety of measures were issued to restrict all social gatherings, commercial activities, and mandating preventative health practices. This study aimed to i) understand distribution patterns and impact of decisions and responses at the spread of confirmed cases; ii) highlight and verify most concentrated regions with infections; and iii) overview spatial changes of cases overtime. The analysis was carried out using inverse–distance-weighted interpolation and hotspot (Getis–Ord GI*) techniques. Results showed a substantial relationship between spatial structure of COVID–19 and population distribution and density. COVID–19 has increased by 11.5% weekly in the capital, which were locked down since April 2020. However, after health quarantine was lifted on May 29, 2020, weekly cases surged in the capital. Al-Batinah-North and Dhofar recorded an increase of 32.1% and 30.5%, respectively, after restrictions had eased. The analysis illustrated that spread of COVID–19 was shifting from Northeast to Southeast and later shifted back to the Northeast of the country at the end of year 2022. This study is beneficial for pertinent organizations to perform detailed studies for developing and monitoring disease systems and dominating relevant factors.
Comparison of proposed electricity billing mechanism for residential clients of Maharashtra Belge, Archana Talhar; Bodkhe, Sanjay; Alegavi, Sujata; Ravankar, Arpit; Kasturiwale, Hemant; Ranjan, Alok
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.pp4815-4826

Abstract

The comparison of three modified electricity billing mechanisms (model I, model II, and model III) for low tension (LT-I) residential consumers of Maharashtra, India, is presented in this paper. Models I and II are presented in detail along with the results in the previous version of this paper in the year 2020 and year 2022. In continuation of this work, model III is presented in this paper in the year 2023. The main components of this mechanism are traditional billing, time-of-day billing, and an optional facility to use renewable energy by implementing net metering. The combination of these elements generates three distinct billing mechanisms. These have several advantages, like the profits of the existing mechanism, renewable integration, grid stability, demand management, cost saving, environmental benefits, and customer empowerment. The projected billing mechanism is developed and implemented in MATLAB software, and a real-time application is created. The comparison between these three mechanisms helps in giving the best mechanism with respect to residential consumers. Lastly, the philosophy for the future extension to this work is presented which is based on the concept of overseas billing mechanism i.e., seasonal time of day tariff of Sacramento Municipal Utility District and Arizona Public Service.
Phase structures-based hybrid approaches for defect detection in vials Vishwanatha, C. R.; Asha, V.
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.pp5185-5199

Abstract

Quality control and assurance in pharmaceutical vial manufacturing are paramount to ensure drug safety and efficacy. Defects such as cracks, bubbles, black spots, and wrinkles can compromise product quality and patient safety. This study proposes a novel methodology that integrates fast non-local means (FNLM) filtering with hybrid image processing techniques to detect these defects. Previous approaches have often struggled with subtle anomalies in texture and surface features. The proposed solution leverages phase structure analysis, utilizing phase stretch transform (PST) to effectively highlight subtle anomalies by extracting features sensitive to phase variations. These features are further refined using Gaussian filtering, with Otsu thresholding applied for precise segmentation and defect boundary identification. Morphological dilation enhances detection speed and accuracy, while region of interest (ROI) identification aids in localizing defects and facilitating decision-making. The system demonstrates significant improvements in quality control, achieving high performance metrics: precision (98.85%), recall (98.57%), accuracy (98.36%), specificity (98.0%), and F1-score (98.71%). It also achieves impressive AUC-ROC (98.18%) and AUC-PR (99.08%) values, demonstrating its robustness and suitability for defect detection in pharmaceutical vials.

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