International Journal of Innovative Research and Scientific Studies

Показано: 1 - 3 из 3

Некоторые метрики заблокированы вашими
настройками согласия
Machine learning-based predictive maintenance system for urban heating networks for real-time failure detection and analysis
(2025-09-19)
Dauren Darkenbayev
;
Uzak K. Zhapbasbayev
;
Gulnar Tultayevna Balakayeva
This study presents a comprehensive framework for predictive maintenance of urban heat supply networks utilizing advanced machine learning algorithms. The primary objective is to enable early detection of potential system failures, thereby improving operational reliability and minimizing unplanned downtimes. A synthetically generated dataset of 10,000 records was employed, simulating real – world operational parameters such as temperature, pressure, flow rate, and vibration, sampled at 5–minute intervals to replicate actual monitoring conditions. Data preprocessing involved outlier removal using the interquartile range (IQR) method, normalization through Min-Max scaling, and imputation of missing values, ensuring data quality and consistency. Feature importance was further analyzed using SHAP values to enhance interpretability and identify critical predictors influencing system behavior. Five machine learning models – Logistic Regression, Support Vector Machine (SVM), Random Forest, Artificial Neural Networks (ANN), and Gradient Boosting (LightGBM) – were implemented and evaluated using 10 – fold cross – validation. The Gradient Boosting model demonstrated superior performance, achieving an accuracy of 99.9%, F1-score of 0.999, ROC-AUC of 1.0, and LogLoss of 0.004. Logistic Regression and Random Forest also performed well (AUC = 1.0, F1 = 0.999), whereas SVM and ANN exhibited limited predictive capabilities (AUC ≈ 0.50, F1 = 0.038 and 0.632, respectively). These results underscore the robustness of Gradient Boosting in modeling complex nonlinear relationships and its applicability for real-time anomaly detection in heating systems. The proposed framework holds significant practical potential for integration into existing monitoring infrastructures, facilitating proactive maintenance planning, optimizing resource allocation, and reducing operational costs. Future research will focus on validating the approach with real – world datasets and exploring hybrid machine learning architectures to enhance model generalizability and resilience.
Некоторые метрики заблокированы вашими
настройками согласия
Experimental studies of sensors based on fiber Bragg gratings embedded in the internal structure of composite plates
(2024-10-04)
Aliya Kalizhanova
;
Айнур Козбакова
;
Murat Kunelbayev
;
Beibut Amirgaliyev
;
Zhalau Aitkulov
The main purpose of this article is to study the structural characteristics of carbon fiber samples printed on a 3D printer, including embedded sensors, during 3D printing, through numerical and experimental studies. There is a widespread tendency to replace metals with modern composite materials. Various methods can be used to measure the destruction of the composite structure. Fiber Bragg array sensors are known as intelligent localized and global structural condition prediction devices for all kinds of structural applications, especially those using composite materials. In this work, we created control plates by impregnating a selected number of layers of carbon fabric with epoxy resin. Fiber-optic Bragg sensors were additionally located between the layers of carbon fabric. In the course of experimental studies, a fiber Bragg grating sensor was embedded in the middle of the sample, and a second sensor was attached to the finished surface of the sample. We conducted environmental tests to examine the durability of the additively manufactured standard’s components and the impact of integrated sensors on the composite sample. We applied the concept of equations, known as conjugate modes, and used the transition matrix method to solve them numerically. The scientific novelty of the work is the development of a new method and technology for measuring spatial deformation in composite materials using fiber-optic Bragg gratings.
Цитирования (OpenAlex) 1
Некоторые метрики заблокированы вашими
настройками согласия
Shaping digital communication culture in prospective teachers: The role of digital etiquette training in Kazakhstan
(2025-02-24)
Ulzharkyn M. Abdigapbarova
;
Aigul Syzdykbayeva
;
Elmira A. Aitenova
;
Sabira Nishanbayeva
;
Saniya Nurgaliyeva
This study examines the impact of digital etiquette training on the development of a digital communication culture among future teachers by providing new perspectives and access to scientific knowledge in the field of digital etiquette. A mixed-methods approach was used both before and after the test. The study included 115 respondents. The preliminary testing between the groups showed that both groups were at the same level in terms of communicative culture indicators. Additionally, participants had no prior knowledge of digital etiquette. An authors' program was created specifically for the course and implemented to help students become more skilled with technology. A set of measures was carried out to develop a digital communicative culture through the means of netiquette and the introduction of technology for individualization and personalization of learning. This study found the impact of digital etiquette on the development of a digital communication culture in future teachers by providing new perspectives and access to scientific knowledge in the field of digital etiquette, as well as the practical application of this knowledge to support cultural digital interaction.

International Journal of Innovative Research and Scientific Studies

Фильтры

Настройки

Сортировать по

результатов на странице