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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Darkenbayev, Dauren
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Computer Science & IT Electrical & Electronics Engineering

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Parallel numerical simulation of the 2D acoustic wave equation Altybay, Arshyn; Darkenbayev, Dauren; Mekebayev, Nurbapa
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.pp6519-6525

Abstract

Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.
Development of a machine learning-based framework for predicting failures in heat supply networks Darkenbayev, Dauren; Balakayeva, Gulnar; Zhapbasbayev, Uzak; Zhanuzakov, Mukhit
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10327

Abstract

The increasing complexity and scale of heat supply systems leads to a higher risk of failures, which may cause significant economic and environmental consequences. This study develops a predictive mathematical framework for the early detection of emergency conditions in heat supply networks (HSNs) using machine learning (ML). The proposed approach is based on the LightGBM gradient boosting (GB) algorithm, chosen for its high accuracy and efficiency in handling large datasets. Real operational data (temperature, pressure, flow, and vibration) were considered. Data preprocessing, feature engineering (including SHAP analysis), and hyperparameter tuning with grid search and 5-fold cross-validation improved prediction quality. The model achieved accuracy of 85%, F1-score of 0.82, and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.96, outperforming logistic regression (LR) and decision trees. The framework may be integrated into monitoring systems for predictive maintenance, reducing downtime and optimizing costs.
Co-Authors Altybay, Arshyn Balakayeva, Gulnar Mekebayev, Nurbapa Zhanuzakov, Mukhit Zhapbasbayev, Uzak