Scientific Journal of Engineering Research
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Articles
10 Documents
Search results for
, issue
"Vol. 2 No. 2 (2026): June"
:
10 Documents
clear
<![CDATA[Sustainable Construction Practices: Integrating Renewable Energy for Carbon Footprint Reduction ]]>
<![CDATA[Alabi, Oluwaseyi Omotayo]]>;
<![CDATA[Laoye, Adeoti Oyegbori]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.386
<![CDATA[The construction sector is a major contributor to resource depletion and greenhouse gas emissions, underscoring the importance of adopting sustainable practices to meet environmental and climate goals. However, current assessments often underestimate impacts because of narrow system boundaries and insufficiently localized material inventory data, creating a critical research gap in accurately evaluating building sustainability. This study therefore applies to a comprehensive Life Cycle Assessment (LCA) framework to evaluate the environmental performance of key construction materials and to investigate strategies for integrating circular design and renewable energy to reduce carbon footprints. The results reveal that medium-term environmental impacts are approximately 20–30% higher than previously reported, while the Global Warming Potential of conventional brick increases by about 23% when additional life-cycle stages are considered. Furthermore, the analysis demonstrates that design-for-disassembly and recycling-oriented approaches can significantly enhance material recovery and reduce waste. These findings imply that developing harmonized, region-specific material databases and promoting circular construction alongside renewable energy integration are essential for improving LCA accuracy and achieving meaningful reductions in the environmental footprint of buildings.]]>
<![CDATA[Federated Temporal Graph Learning for Weakly Supervised Bearing Anomaly Detection ]]>
<![CDATA[Darlami, Khagendra]]>;
<![CDATA[Awasthi, Lalit]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.413
<![CDATA[Industrial bearing health monitoring is hindered by four interrelated challenges: high class imbalance, the absence of fault-type annotations, stringent data privacy constraints prohibiting centralized aggregation, and non-independent and identically distributed (non-IID) degradation dynamics across geographically dispersed assets. To address these, we propose Fed-TGCN, a novel weakly supervised federated learning framework grounded in temporal graph neural networks. Each client represents a leave-one-bearing-out fold, comprising two training bearings, one validation bearing, and one held-out test bearing, constructs a hybrid spatio-temporal graph from six physics-informed statistical features derived from raw vibration signals; edges encode both sequential dependencies and feature-space similarity via k-nearest neighbors. Pseudo-anomaly labels are generated locally through adaptive thresholding of a degradation score using exponentially weighted moving average, eliminating reliance on expert annotations. Under a strict leave-one-bearing-out protocol on the NASA IMS dataset (12 bearings), local Temporal Graph Convolutional Networks are trained in isolation and aggregated globally via FedAvg. Our method achieves an Average Precision of 0.675 ± 0.276 and Matthews Correlation Coefficient of 0.636 ± 0.285, maintains stronger performance consistency across heterogeneous bearing conditions than isolated and non-graph baselines (ΔMCC = +0.130, p < 0.01). Ablation studies confirm the necessity of temporal modeling (MCC drops by 0.069 without GRU). To the best of our knowledge, this is the first work integrating weakly supervised, graph-based federated learning for bearing prognostics under, demonstrating that parameter coordination but not the data sharing which enables degradation-invariant representation learning across heterogeneous assets.]]>
<![CDATA[Numerical and Experimental Investigation of a Vortex Head for Back-Pressure Suppression in Petroleum Pumping Systems ]]>
<![CDATA[Effiom, Samuel Oliver]]>;
<![CDATA[Enoh, Maria Kaka Etete]]>;
<![CDATA[Willie, Godwin Effiong]]>;
<![CDATA[Effiom, Precious-Chibuzo]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.422
<![CDATA[Back-pressure accumulation and transient pressure surges remain critical operational challenges in petroleum pumping systems, particularly during high-rate product transfer into storage tanks. Conventional mitigation strategies such as pressure relief valves, surge vessels, and bypass lines are largely reactive, energy-intensive, and maintenance-dependent. Despite advances in computational fluid dynamics (CFD), limited research has addressed passive inlet-based hydrodynamic conditioning for petroleum storage tanks with full-scale industrial validation. This study presents a combined numerical and experimental investigation of a passive vortex head (VH) designed to suppress back pressure through vortex-induced flow redistribution at the tank inlet. Three-dimensional CFD simulations were performed using ANSYS Fluent with the realizable k–ε turbulence model to analyze pressure distribution, velocity fields, turbulence characteristics, and vortex formation. Controlled experimental validation was conducted using a prototype system under normalized inlet pressure conditions (0.07 bar) in an industrial petroleum storage facility. The results demonstrate that the vortex head induces a stable swirling flow that promotes gradual momentum dissipation and reduces localized pressure buildup near full capacity. Compared with a conventional straight inlet configuration, the vortex head reduced peak back pressure by approximately 20–30%, while decreasing total tank filling time by about 15% under identical flow conditions. CFD predictions agreed with experimental measurements within ±5%. The findings establish passive vortex-based inlet conditioning as a practical, energy-efficient strategy for preventive back-pressure suppression in petroleum storage infrastructure.]]>
<![CDATA[LSKD: Lightweight Self-Knowledge Distillation Framework for Fast and Robust Crowd Counting ]]>
<![CDATA[Raza, Muhammad]]>;
<![CDATA[Ling, Miaogen]]>;
<![CDATA[Ur Rahman, Atta]]>;
<![CDATA[Pallewatta, Pandula]]>;
<![CDATA[Hersi, Aboubakar Abdinur]]>;
<![CDATA[Beruwalage, Shehan Maxwell]]>;
<![CDATA[Kannangara, Deshan Sachintha]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.436
<![CDATA[Crowd counting plays an important role in the surveillance of the safety of the people, traffic, and intelligent surveillance systems. However, the exact density estimations remain hard to achieve in highly congested scenes due to the tough occlusion, large-scale variance, and complicated background. Although the recent deep-learning methods have high performance, several of them do not need computationally efficient underlying backbone networks, and rather, they employ an external teacher-student distillation architecture, which can limit their use in resource-constrained applications. To avoid this problem, we introduce LSKD, a lightweight self-knowledge distillation network that is density map regression-specific. Unlike other conventional teacher-dependent processes, LSKD can also independently carry out internal multi-level feature alignment within a single small network that is not in need of an external teacher model. The structure integrates a Feature Matching Block (FMB) and a Context Fusion (CoFuse) block to enhance the hierarchical match of features and global awareness of context. The large experiments demonstrate that LSKD obtain competitive performance using the number of parameters as 2.65 million and GFLOPs as 10.23. Particularly, it has 63.17 MAE on ShanghaiTech Part A, 8.94 on ShanghaiTech Part B, 143.7 on UCF-QNRF, and 223.88 on UCF-CC-50, which is a good ratio between the accuracy and the efficiency of the calculations. Such results indicate that LSKD has an implementable and efficient solution to the real-time counting of crowds at the edge devices.]]>
<![CDATA[Deep Residual Learning-Based Categorization of Gastric Pathologies: A Knowledge Transfer Framework ]]>
<![CDATA[Win, Ei Phyu Sin]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.429
<![CDATA[Early detection of gastric pathologies, such as polyps, esophagitis, and ulcerative colitis, plays a pivotal role in improving patient clinical outcomes and long-term treatment efficacy. Despite advancements in medical imaging, manual endoscopic analysis remains a labor-intensive process prone to human error and inter-observer variability, creating a critical research gap for automated diagnostic tools. This research introduces a robust automated classification framework employing the ResNet18 architecture, optimized through a refined Transfer Learning methodology. The study utilizes a comprehensive multi-class dataset, with input data undergoing meticulous preprocessing, including global normalization and strategic data augmentation, to enhance generalization. Empirical evaluations conducted over 50 epochs revealed superior performance, with the proposed model achieving an overall accuracy of 94.05%. Notably, a precision rate of 100% was attained, indicating zero false alarms, while a high sensitivity of 91.67% confirmed the model's effectiveness in distinguishing subtle cancerous features from healthy gastric folds. These quantitative findings underscore the framework's reliability and its potential for seamless integration into clinical decision-support systems. By providing high-fidelity diagnostic assistance, this study contributes to the evolution of computer-aided diagnosis (CAD), offering a scalable solution to reduce clinician workload while significantly increasing the accuracy of early-stage gastric pathology detection.]]>
<![CDATA[Isolation Forest–Based Intrusion Detection for Cyber-Physical Systems ]]>
<![CDATA[Oise, Godfrey Perfectson]]>;
<![CDATA[Konyeha, Susan]]>;
<![CDATA[Uloko, Felix Oshiorenoya]]>;
<![CDATA[Pius, Kevin Chinedu]]>;
<![CDATA[Eferoba–Idio, Enovwo]]>;
<![CDATA[Edobor, Michael Uyiosa]]>;
<![CDATA[Mintah, Evans]]>;
<![CDATA[Ukpebor, Osahon]]>;
<![CDATA[Sokoya, Oludare]]>;
<![CDATA[Jessa, Tejiri]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.434
<![CDATA[Cyber-physical engineering systems (CPES) form the backbone of critical infrastructures such as power generation, industrial automation, and water treatment facilities. Because cyber intrusions in these environments can directly disrupt physical processes, reliable intrusion detection mechanisms are essential for maintaining operational safety and system resilience. However, many existing intrusion detection approaches rely on supervised learning techniques that require large volumes of labeled attack data, which are rarely available in real industrial environments. In addition, advanced detection methods often introduce significant computational overhead, limiting their practicality for deployment in resource-constrained cyber-physical systems. To address these challenges, this study proposes a one-class anomaly detection framework based on the Isolation Forest algorithm for monitoring cyber-physical engineering systems. The proposed approach learns the statistical distribution of normal operational behavior using multivariate sensor, actuator, and control signals, and identifies deviations from this learned pattern as potential cyber intrusions. The framework is evaluated using the Hardware-in-the-Loop–based Augmented Industrial Control System (HAI) Security Dataset, which provides realistic industrial process measurements under both normal and attack scenarios. Experimental results show that the model achieves overall accuracy (0.89) and strong performance in identifying normal operational states (F1-score = 0.94). However, attack detection shows moderate recall (0.48) but low precision (0.04) due to class imbalance and overlapping anomaly score distributions. These findings indicate that Isolation Forest serves as a computationally efficient baseline anomaly detection mechanism for real-time CPS monitoring, while highlighting the need for hybrid and temporally aware detection strategies to improve attack discrimination in industrial cyber-physical environments.]]>
<![CDATA[Artificial Intelligence–Driven Simulation Models for Industrial Accident Prevention in Chemical Process Engineering ]]>
<![CDATA[Acuña Acuña, Edwin Gerardo]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.405
<![CDATA[Industrial accidents in chemical process engineering continue to pose a significant issue despite the widespread use of Industry 4.0 technology and data-driven monitoring systems. Traditional safety frameworks often depend on either purely empirical machine learning models or deterministic first-principles simulations, creating a methodological split that constrains prediction reliability in uncommon, high-impact situations. This work bridges the structural gap by incorporating physics-informed artificial intelligence into a digital twin architecture for the avoidance of industrial accidents. A methodological framework driven by simulation was established, integrating first-principles process modeling, synthetic data generation with controlled fault injection, supervised and unsupervised learning, and reinforcement learning for safety-constrained optimization. Physics-based limitations were included into the learning aim to maintain thermodynamic and transport consistency. The model's performance was assessed using safety-oriented criteria, such as detection delay, false negative rate, resilience to sensor noise, and stability amid parametric uncertainty. Results demonstrate that physics-informed models significantly reduce detection latency and false negatives in accident precursor regimes compared to purely data-driven baselines. The integration of constraint-aware learning improves extrapolation stability under class imbalance conditions typical of industrial safety datasets. Furthermore, explainable AI mechanisms enhance interpretability and regulatory transparency. These findings indicate that AI-enhanced simulation models reconfigure accident prevention strategies by shifting from reactive threshold systems to proactive, mechanism-consistent risk anticipation frameworks applicable to safety-critical chemical processes.]]>
<![CDATA[Probabilistic Finite Element Analysis of Temperature-Dependent Corrosion in Oil and Gas Pipelines ]]>
<![CDATA[Nrior, Million Matthew]]>;
<![CDATA[Nitonye, Samson]]>;
<![CDATA[Adumene, Sidum]]>;
<![CDATA[Orji, Charles Ugochukwu]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.427
<![CDATA[Marine pipeline systems are continually exposed to operating conditions that accelerate internal corrosion, posing risks to flow assurance and structural integrity. This study applies finite element modelling to evaluate the influence of operating temperature on corrosion progression and pipeline performance. The study addressed gaps in temperature-based corrosion propagation in a pipeline using ANSYS Design Modeler, meshing, and exporting for flow-corrosion modelling in ANSYS Fluent. A one-way coupling was established between Fluent and ANSYS Mechanical to assess the mechanical response under operating conditions. The base case at 62 °C showed a corrosion rate of 6.0 mm/year. To investigate the role of temperature, simulations were conducted at 30 °C, 50 °C, 62 °C, and 70 °C, representing the typical temperature range of Niger Delta fluid systems. Results indicate that lower temperatures significantly increase corrosion rates, leading to pronounced wall thinning and elevated stress concentrations. Conversely, higher temperatures reduce corrosion intensity by promoting the formation of protective corrosion films. However, localized stress elevations at higher temperatures were also observed, which may be attributed to combined thermal expansion effects and residual corrosion-induced weakening. This demonstrates a non-linear interaction between temperature, corrosion progression, and stress response. The study recommends maintaining sufficiently high fluid temperatures to mitigate corrosion. Further studies are needed to define the temperature range where corrosion behaves linearly, to support optimal design and operation while preventing conditions that could impair system performance and flow assurance. The result provides technical insight for the development of an integrity management strategy for optimum pipeline safety.]]>
<![CDATA[Parameter-Efficient Fine-Tuning for Sonar Shipwreck Segmentation: A Seed Averaged Study with SegFormer and LoRA ]]>
<![CDATA[Beruwalage, Shehan Maxwell]]>;
<![CDATA[Yin, Chunyong]]>;
<![CDATA[Raza, Muhammad]]>;
<![CDATA[Kannangara, Deshan Sachintha]]>;
<![CDATA[Hendavitharana, Sachini Amani]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.454
<![CDATA[Accurate segmentation of shipwreck targets in sonar imagery is important for underwater archaeology, marine monitoring, and search operations, but the task remains difficult because labeled sonar masks are scarce and full adaptation of transformer models can be computationally expensive. This study evaluates whether parameter-efficient fine-tuning can provide a practical alternative for binary sonar shipwreck segmentation. Using SegFormer-B0 initialized from a pretrained checkpoint, three adaptation strategies were compared under a consistent protocol: full fine-tuning of all model parameters (FullFT), training only the segmentation head (Head-only), and LoRA-based adaptation of selected linear layers together with head training (LoRA-A+Head). Models were selected by the best validation epoch and evaluated on a held-out test set. Across three random seeds, FullFT achieved the best performance, with a Dice score of 0.614 ± 0.008 and IoU of 0.487 ± 0.007. LoRA-A+Head achieved a Dice score of 0.546 ± 0.010 and IoU of 0.401 ± 0.008 while updating only 1.57% of the parameters, whereas Head-only reached 0.494 ± 0.010 Dice and 0.354 ± 0.008 IoU. These results show a clear accuracy efficiency trade off, full fine-tuning gives the highest accuracy, whereas LoRA-A+Head offers a practical option when reducing the number of updated parameters is important. The findings support the use of parameter-efficient adaptation for sonar segmentation in compute-limited settings.]]>
<![CDATA[Aerodynamic Optimization of Horizontal Axis Wind Turbine Blades Using Winglet and Spoiler Add-Ons: A CFD Study ]]>
<![CDATA[Alabi, Oluwaseyi Omotayo]]>;
<![CDATA[Laoye, Adeoti Oyegbori]]>;
<![CDATA[Salisu, Saidat Abisoye]]>
<![CDATA[ Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June ]]>
Publisher : <![CDATA[PT. Teknologi Futuristik Indonesia ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.64539/sjer.v2i2.2026.459
<![CDATA[The aerodynamic performance of wind turbine blades plays a critical role in maximizing energy generation and overall system efficiency, making it a key consideration in modern renewable energy design. Despite extensive research on blade optimization, there remains a notable gap in understanding the combined effects of spoiler and winglet geometries, particularly their size and orientation on aerodynamic efficiency under low wind speed conditions. This study aims to address this gap by conducting a comprehensive numerical investigation into the influence of spoiler and winglet configurations on wind turbine performance. Computational Fluid Dynamics (CFD) simulations were performed using COMSOL Multiphysics, with the k-ε turbulence model employed to accurately capture turbulent flow behavior. A detailed parametric analysis was carried out, considering winglet height (4%–13% of blade radius), cant angle (20°–90°), twist angle (−2° to 12°), and tip speed ratio (0.02–1.12) at a wind velocity of 3 m/s. The results reveal that optimal combinations of spoiler and winglet parameters significantly enhance aerodynamic efficiency. The study identifies specific design ranges that maximize power output, achieving a peak aerodynamic power of 62.8 W. Although the addition of these aerodynamic devices increases the inertia of the turbine, the system performance improves, with an observed increase in output power of approximately 12%. These findings provide valuable insights for the design and optimization of wind turbine blades, particularly for low wind speed applications.]]>
