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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Kunelbayev, Murat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty Tyulepberdinova, Gulnur; Kunelbayev, Murat; Shiryayeva, Olga; Sakypbekova, Meruyert; Sarsenbayev, Nurlan; Bayandina, Gulmira
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.pp6656-6674

Abstract

In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.
Assembling and testing optoelectronic system to record and process signals from fiber-optic sensors Kalizhanova, Aliya; Kozbakova, Ainur; Wojcik, Waldemar; Kunelbayev, Murat; Amirgaliyev, Beibut; Aitkulov, Zhalau
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp812-822

Abstract

The given research presents assembling and testing optoelectronic system to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controller and optical power detector. There was assembled controller diagram, which apart from light source includes current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modelled for specifying nominal and maximum operation criteria. Construction has been designed in the way, that light source controller includes structures of the current regulation and stabilization super luminescent diode (SLED) and temperature stabilization. Apart from that, there was assembled the microsystem of optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level and sufficient progress.
Development of a Smart Lung Health Monitoring System Using Sensors and Data Analytics for Early Disease Detection Tyulepberdinova, Gulnur; Kunelbayev, Murat; Amirkhanova, Gulshat; Sakypbekova, Meruyert; Adilzhanova, Saltanat; Tolepberdinova, Ardak
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.857

Abstract

This study introduces a novel multimodal wearable sensor system for real-time monitoring and analysis of respiratory and cardiac activity. The primary objective is to facilitate the early detection of cardiopulmonary abnormalities by integrating electrical (ECG) and acoustic data. A total of 30 participants, aged 25 to 50 years, were involved in controlled breathing experiments, which included deep (1000 ml, 15 breaths/min), moderate (750 ml, 20 breaths/min), and shallow (500 ml, 30 breaths/min) breathing, as well as coughing simulations. Signal processing using a 7th-order polynomial approximation yielded the lowest modeling error at 6.8%, ensuring precise waveform reconstruction. The system demonstrated a clear differentiation of respiratory patterns via Area Under the Curve (AUC) metrics, with average AUC values increasing from 1200 µV·s during shallow breathing to 3200 µV·s during deep breathing. Further analysis of the first derivative of AUC values revealed a strong correlation (r = 0.89) between respiratory volume and ECG amplitude fluctuations, highlighting robust cardiorespiratory coupling. Notably, the system achieved a 92% accuracy in detecting abnormal breathing events, such as shallow breathing and coughing fits. By combining ECG-derived heart rate variability with respiratory data, the system offers a comprehensive assessment of cardiopulmonary interaction. The key contribution of this work lies in its real-time, continuous monitoring capability using a compact wearable form factor, which distinguishes it from existing single-modality systems. This approach represents a significant advancement in non-invasive health monitoring, with strong potential for application in clinical diagnostics and home-based tracking of chronic conditions, such as asthma, COPD, and cardiac dysregulation.
Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor Amirkhanov, Bauyrzhan; Kunelbayev, Murat; Issa, Sabina; Amirkhanova, Gulshat; Nurgazy, Tomiris; Zhumasheva, Ainur; Alipbeki, Ongarbek
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.779

Abstract

This article presents the design and research of a modular horizontal tubular bioreactor for efficient biogas production based on anaerobic digestion technology. The study combines a digital twin implemented in the MATLAB/Simulink environment with a physical bioreactor equipped with a sensor and control system. The developed mathematical model describes the biochemical processes of acidogenesis and methanogenesis, the thermal regime and the sensitivity of the system to key parameters. Numerical modeling and visualization methods were used for the analysis. The experiments were carried out for 30 days at a mesophilic temperature of 37 ° C, repeated three times to increase reliability. The raw material used was a mixture of cattle manure and food waste in a 3:1 ratio, with a total volume of 60 liters. Readings from temperature, pH, and methane sensors were taken every 10 minutes. Experimental data confirmed the high efficiency of the design: removal of up to 70.5% of volatile substances and methane yield of up to 80.5%. Predictive analysis has shown that the digital twin is able to predict the behavior of the system and apply corrective actions in real time. The novelty of the work lies in the integration of a digital twin with a physical bioreactor in real time through industrial communication protocols.
An internet of things-enabled wearable device for stress monitoring and control Tyulepberdinova, Gulnur; Abduvalova, Ainur; Kunelbayev, Murat; Amirkhanova, Gulshat; Adilzhanova, Saltanat
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9599

Abstract

The development of a wearable sensor device integrated into the internet of things (IoT) infrastructure is presented, with functionality aimed at continuous measurement of the user's physiological parameters and their intelligent processing for real-time stress level assessment. The system enables continuous monitoring of physiological parameters, allowing early detection of stress signals and supporting adaptive behavioral responses. The hardware platform is designed to consolidate various biomedical sensors, enabling continuous acquisition and intelligent processing of physiological data in real time. During testing, heart rate (HR) ranged from 68 to 89 beats per minute (bpm), respiratory rate varied from 11 to 15 breaths per minute, and skin conductivity ranged from 63 to 77 µS. Acquired physiological data were uploaded to a cloud-based infrastructure to enable advanced processing and analysis. The system achieved an overall stress detection accuracy of 87%, and signal stability remained high even under changing conditions. The proposed wearable solution demonstrates strong potential for use in healthcare, education, and occupational environments. It also offers scalability through the integration of intelligent algorithms and additional sensor modules.
Digital twins and IIoT: comparison of Prometheus and InfluxDB Amirkhanov, Bauyrzhan; Ishmurzin, Timur; Kunelbayev, Murat; Amirkhanova, Gulshat; Aidynuly, Azim; Tyulepberdinova, Gulnur
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9687

Abstract

This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.
Co-Authors Abduvalova, Ainur Adilzhanova, Saltanat Aidynuly, Azim Aitkulov, Zhalau Alipbeki, Ongarbek Amirgaliyev, Beibut Amirkhanov, Bauyrzhan Amirkhanova, Gulshat Bayandina, Gulmira Ishmurzin, Timur Issa, Sabina Kalizhanova, Aliya Kozbakova, Ainur Nurgazy, Tomiris Sakypbekova, Meruyert Sarsenbayev, Nurlan Shiryayeva, Olga Tolepberdinova, Ardak Tyulepberdinova, Gulnur Wojcik, Waldemar Zhumasheva, Ainur