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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 6,439 Documents
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Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system Benaissa, Tahar; Belazreg, Mohamed Fouzi; Halbaoui, Khaled; Djaroum, Belaid; Boukhetala, Djamel
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1148-1158

Abstract

This study employs the mixed logical dynamical (MLD) framework for modelling, simulating, and controlling hybrid dynamical systems. Hybrid systems, which combine continuous-time dynamics and discrete logical events, pose significant challenges for conventional control strategies, such as proportional-integral-derivative (PID) controllers, particularly under complex operational constraints. To address these challenges, the MLD formalism provides a unified representation that integrates differential equations, logical rules, and inequality constraints. Based on the MLD model, a multivariable hybrid model predictive control (HMPC) approach is designed to optimize control system performance and operational efficiency over a prediction time horizon. At each sampling time step, a mixed quadratic programming (MIQP) optimization problem is solved online to determine the control law. The proposed control approach is applied to a three-spherical tank system, where simulation and experimental results demonstrate its effectiveness in ensuring stability, minimizing tracking errors, and satisfying physical constraints. These results underscore the relevance of MLD-based predictive control approaches for the optimization and advanced control of complex multivariable hybrid dynamical systems in industrial fields.
Designing self-healing database fabrics for real-time payment rails Gollapudi, Raghu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1360-1368

Abstract

Real-time payment platforms operating at scale face an unforgiving operational reality: even brief outages translate directly into failed transactions, regulatory exposure, and eroded customer trust. Database replication and failover automation have matured considerably over the past two decades, yet a troubling blind spot remains. Recovery frameworks built for general-purpose distributed systems were never designed with settlement finality in mind, and that design omission leaves payment operators exposed to split-brain scenarios that generic high-availability tooling cannot reliably prevent. This paper addresses that omission head-on through a self-healing database fabric purpose-built for payment rail environments. The proposed autonomous resilience fabric architecture (ARFA) operates across three coordinated layers: a continuous monitoring layer that harvests telemetry from compute, storage, and network subsystems; a decision layer that fuses rule-based heuristics with an ensemble of isolation forests, recurrent neural networks, and gradient boosting classifiers to separate genuine fault conditions from transient noise; and a deterministic action layer that executes recovery procedures anchored to explicit settlement finality constraints. In fault injection trials covering node crashes, network partitions, replication lag, and performance degradation, the architecture cut average recovery times by 88% against manual baselines, restoring service in roughly 8 seconds rather than the 180 seconds that human-driven remediation typically requires. False positive rates held below 2% across all failure categories, and the system achieved a 98% recovery success rate. Taken together, these results make a practical case that autonomous resilience and regulatory compliance reinforce rather than conflict with each other when the regulatory constraints are designed in from the start.
AI-driven log reduction and storage optimization for security operations Chalaemwongwan, Nutthakorn
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1417-1424

Abstract

In this study, we present an AI-driven framework that integrates semantic log reduction with compliance-aware storage optimization, specifically designed for security operations center (SOC) and managed security service provider (MSSP) environments. Traditional approaches such as uniform compression, keyword filtering, and static tiering often either miss critical anomalies or preserve redundant noise, leading to excessive storage use, slower search performance, and analyst fatigue. The proposed framework addresses these challenges by combining three components: semantic reduction of repetitive entries, anomaly-focused retention supported by self-supervised models, and adaptive tiering aligned with regulatory requirements. Evaluations on HDFS, BGL, CICIDS2017, and Suricata datasets achieved 70%–80% log reduction, 55%–65% storage savings, recall rates above 95%, and a one-third reduction in query latency. These results demonstrate that pre-index reduction, together with anomaly- and compliance-aware retention, offers a scalable and regulator-ready solution for operational security environments.
A review of stability analysis in islanded microgrids with photovoltaic integration Ganeshan Viswanathan; Govindanayakanapalya Venkatagiriyappa Jayaramaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1832-1840

Abstract

Microgrids, emerging as a solution to meet rising energy demands and combat environmental issues, present unique challenges in stability analysis, especially when integrated with photovoltaic (PV) systems. This review explores the stability analysis of islanded microgrids with PV integration, addressing significant gaps in current understanding and methodologies. Firstly, the paper classifies microgrid stability into small signal, transient, and voltage stability, highlighting the distinct characteristics of each aspect. Subsequently, it provides an overview of stability analysis techniques, encompassing conventional, intelligent, and hybrid methodologies. The operational challenges faced by islanded microgrids are examined, along with effective control strategies to mitigate them. Moreover, the integration of photovoltaic systems into microgrids is scrutinized, including system configurations, stability impacts, and control methods. Finally, the paper discusses existing challenges and outlines future directions for advancing microgrid stability analysis. By explaining these critical aspects, this review underscores the necessity of enhancing stability analysis frameworks to ensure the robustness and reliability of islanded microgrids with PV integration in the evolving energy landscape.
Flicker noise suppression and tuning range linearization techniques for RF CMOS voltage-controlled oscillators Nam-Jin Oh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1792-1804

Abstract

This paper proposes a differential RF CMOS voltage-controlled oscillator (VCO) employing a series LC (SLC) network to suppress 1/f³ flicker noise and linearize the tuning range. The SLC network incorporates two coupling capacitors connected to each node of a parallel inductor-varactor tank. Each series connection node is cross-coupled to the gates of switching transistors, facilitating a large signal swing. By optimizing the coupling capacitance, 1/f³ flicker noise is effectively mitigated. Designed in 180 nm CMOS technology, the proposed NMOS-only SLC VCO is compared with a conventional differential VCO using a parallel LC (PLC) network. Targeted for 3.3 GHz applications, the VCO maintains a consistent phase noise slope of −20 dB/decade across offset frequencies from 1kHz to 10 MHz. The SLC VCO achieves a phase noise of −71.6 dBc/Hz at a 1 kHz offset and −131.5 dBc/Hz at a 1 MHz offset with a power consumption of 11.9 mW from a 1.5 V supply. The resulting figure of merit (FOM) is 191.3 dBc/Hz at a 1 MHz offset.
Enhancement of YOLOv8 for object detection in adverse weather conditions using generative adversarial network Talifhani Calvin Tshipota; Chunling Tu; Mukatshung Claude Nawej; Sempe Thom Leholo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2230-2246

Abstract

Detecting objects in bad weather like rain, fog, snow, or low light is still difficult because visibility drops, noise increases, and contrast gets worse, all of which hurt detection accuracy. Most current methods either improve detector designs or use image preprocessing on their own. They usually focus on just one type of weather and do not use a common way to evaluate results. This paper introduces a YOLOv8 framework improved with a generative adversarial network (GAN) for image enhancement before detection. Instead of just making images look better, the GAN is trained to help the object detector work better in tough conditions, so it can extract features more effectively when images are degraded. The model was tested on datasets with different weather conditions using standard metrics like Precision, Recall, F1-score, and mean average precision (mAP). Results show that this method consistently improves performance, with up to a 6.5% increase in mAP@0.5 over YOLOv8-STE and 9.2% over IA-YOLO, especially in foggy and low-light situations. These results show that adding GAN-based preprocessing to YOLOv8 makes detection more reliable and still keeps the process fast. This framework offers a practical and scalable solution for real-world uses like self-driving cars, smart transportation, and surveillance.
Hierarchical inner outer LQR-PID controller based on high-order sliding mode observer for underwater remotely operated vehicle Sedini Aicha; Mokhtari Abdellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1841-1852

Abstract

This paper presents a hierarchical inner–outer linear quadratic regulator-proportional integral derivative (LQR–PID) control architecture integrated with a high-order sliding mode (HOSM) observer for precise motion control of an underwater remotely operated vehicle (ROV) operating in uncertain and highly nonlinear environments. The proposed control strategy is structured into two coordinated layers: An inner-loop LQR controller for dynamic stabilization and disturbance attenuation, and an outer-loop PID controller for trajectory tracking and set-point regulation. The control system has been organized into a two-layer structure: The inner loop based on an LQR controller to manage fast dynamics and compensate for unmodelled underwater effects. The outer loop utilizes a PID controller to guaranty smooth tracking performance and maintain robustness against parameter variations. To deal with limited sensors, a HOSM observer is applied to estimate the states that cannot be measured and to handle uncertainties. By combining the PID–LQR controller with the HOSM observer, the system becomes more robust, faster in response, and more accurate than using either LQR–HOSM or PID control alone. Simulations with a comparative study between these scenarios show better tracking for PID_LQR_HOSM overall system under varying ocean currents, and tests on a 1-meter ROV confirm its effectiveness for advanced navigation and manipulation tasks.
Scenario-driven fault injection for realistic bugs in web application testing Asri Maspupah; Joe Lian Min; Yadhi Aditya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2014-2030

Abstract

Conventional mutation-based fault injection techniques generally produce single-line syntactic faults, which often fail to represent realistic errors at the functional requirement level because requirement context, execution paths, and functional dependencies are not considered. To address this limitation, this study proposes scenario-driven fault injection (SDFI), a scenario-based fault insertion approach that derives faults from functional requirements and test cases. SDFI integrates operational fault localization, web fault taxonomy, fault injection patterns, and functional scenario mapping to produce targeted fault injections at relevant code locations, resulting in a realistic bug dataset with multi-line faults. An experimental evaluation on a real web application produced 29 mutants, achieving a fault detection rate of 89.29% based on the RIP model. Further analysis shows that the generated mutants replicate common real-world bug characteristics, including logic errors, validation anomalies, inter-function data propagation, and multi-line faults affecting client–server application behavior. These results demonstrate that SDFI is effective in producing realistic bug datasets for evaluating software testing quality, improving test case effectiveness, and supporting further research on requirement-based fault realism.
Multiobjective framework for congestion management through coordinated scheduling of generation and demand Jayesh Priolkar; Govind Kunkolienkar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1688-1703

Abstract

The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
A systematic review and conceptual roadmap for sky computing: AI-enabled orchestration, interoperability, and governance beyond multi-cloud Abdullah Al-Bakri
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2247-2253

Abstract

The concept of sky computing is becoming known as an industry-independent “cloud of clouds” approach intended to address the issues of fragmentation inherent in today’s multi-cloud and hybrid cloud implementations. The objective of this systematic literature review is to examine the challenge of existing multi-cloud architectures, which despite delivering high reliability and purchasing flexibility, suffer from API heterogeneity, fragmented intercloud orchestration, insufficient workload mobility, and unresolved sovereignty and compliance challenges. Based on the PRISMA methodology, 325 papers released between January 2020 and June 2025 have been systematically selected in five scientific databases: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, and Scopus. Upon applying a process of elimination for duplicates, title-and-abstract screening, full-text evaluation, and quality assessment, a total of 35 peer-reviewed publications from the same timeframe have been thematically analyzed. Four thematic areas were examined: architectural architecture, intercloud orchestration, automation through artificial intelligence/machine learning (AI/ML), and security, privacy, and compliance. Not a single article predating the year 2020 was part of the final systematic literature review (SLR) database or bibliography. The results reveal that compatibility layers and intercloud brokers increase portability of workloads; scheduling based on artificial intelligence is useful in automating operations and achieving optimal cost performance; while zero trust architecture, self-sovereign identity, confidential computing, and policy-driven compliance are key in achieving trustworthy cross jurisdictional operations. The two major conclusions that arise from this study are: Firstly, future studies need to investigate explainability and energy-efficient AI orchestration in a real-world setting with multiple cloud providers, whereas secondly, cloud computing professionals need to incorporate principles of privacy, sovereignty, and compliance directly into the orchestration policies, not as an afterthought.

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