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Recent Submissions
A SOFTWARE SOLUTION FOR INTELLIGENT FAULT DETECTION IN WHEEL BEARINGS
(Manuscript, 2025-01-23) Glugovskiy, Glib
Wheel bearings are critical components in machinery and vehicles, and their failure can lead to costly downtime and safety risks. This project focuses on the research, development, and implementation of an intelligent software solution for fault detection in wheel bearings. By combining advanced signal processing techniques and machine learning, the solution enables early and accurate fault diagnosis efficiently for all users.
The software incorporates Fast Fourier Transform (FFT) for feature extraction and Self-Organizing Maps (SOMs) for clustering and fault classification, achieving high accuracy in detecting bearing defects. This approach marks a significant advancement in the fields of machine maintenance and fault diagnostics by offering a more precise and efficient method for identifying defects in wheel bearings.
A key aspect of the project involves the development and training of a SOM model. This model is specifically designed to analyze patterns in the cyclostationary signal data, enabling the accurate identification of potential bearing faults. The SOM model, along with the other signal processing and machine learning components, is integrated into a robust software application. Built using Django and React.js for the web interface and enhanced with Rust libraries for computational efficiency, this solution presents a novel integration of traditional web technologies with modern performance optimization techniques.
MULTIMODAL RETRIEVAL AUGMENTED GENERATION SYSTEM
(Manuscript, 2025-01-23) Yuvzhenko, Denys
This study presents an asynchronous, web-based Retrieval-Augmented Generation (RAG) system that integrates multimodal inputs (text, images, tables) to enhance information retrieval and generation in various contexts. The system is developed in Python and hosted on AWS, combining Chroma DB for vector storage and Anthropic’s Claude-3-Haiku LLM model, which is accessible via AWS Bedrock. By leveraging modern cloud capabilities, the solution scales efficiently and handles diverse data modalities in real time.
Through systematic experiments, this project highlights the effectiveness of multimodal embedding techniques for refining retrieval accuracy and providing context-aware responses. The architecture’s modular design supports seamless feature integration, making it adaptable for different use cases such as customer support, educational tools, and content creation. Key findings emphasize the role of vector databases in dynamic information updates and confirm that large language models, when appropriately curated and grounded, can produce high-quality, relevant outputs
AUTOMATED QUIZ CREATION USING CHATGPT WITH INTEGRATION INTO THE CANVAS LMS
(Manuscript, 2025-06-09) Lazebnyi, Vitalii
This study explores the automation of quiz generation using Large Language Models (LLMs) like GPT-4, and their integration into Learning Management Systems (LMS) Canvas. It discusses the potential of LLMs to transform assessment creation by generating diverse question types aligned with learning objectives. The research also addresses ethical considerations, including data privacy and algorithmic bias, and highlights the importance of prompt engineering for effective AI-human interaction. The findings suggest that LLMs can significantly streamline quiz creation, saving educators time and enhancing student learning experiences while emphasizing the need for ongoing refinement and ethical oversight.
WEB ACCESSIBILITY OPTIMIZATION IN FRONT-END DEVELOPMENT
(Manuscript, 2025-01-23) Starchenko, Anastasiia
This capstone project explores the critical importance of web accessibility in front-end development, emphasizing inclusivity for users with diverse abilities. With over 1 billion individuals globally experiencing some form of disability, this study focuses on creating a web accessibility analysis and optimization web app. The research identifies gaps in existing tools like Lighthouse, which often miss nuanced accessibility issues. Using WCAG standards, the app analyzes HTML code to detect violations, such as missing alt text, improper ARIA roles, and low contrast ratios. Featuring visualization, recommendations, and downloadable reports, the app aids developers in achieving WCAG AA compliance. The project underscores the value of inclusive digital design, offering practical implications for developers, businesses, and educators, while setting a foundation for future enhancements, including AI integration and internationalization.
Stochastic Model and Rhythm-Adaptive Technologies of Statistical Analysis and Forecasting of Economic Processes with Cyclic Components
(2025-05-19) Lupenko, Serhii; Horkunenko, Andrii
This article presents a mathematical model of cyclical economic processes, formulated as the sum of a deterministic polynomial function and a cyclic random process that simultaneously captures trend, stochasticity, cyclicity, and rhythm variability. Building on this stochastic framework, we propose rhythm-adaptive statistical techniques for estimating the probabilistic characteristics of the cyclic component; by adjusting to rhythm changes, these techniques improve estimation accuracy. We also introduce a forecasting procedure that constructs a system of rhythm-adaptive confidence intervals for future cycles. The effectiveness of the model and associated methods is demonstrated through a series of computational experiments using Federal Reserve Economic Data. Results show that the rhythm-adaptive forecasting approach achieves mean absolute errors less than half of those produced by a comparable non-adaptive method, underscoring its practical advantage for the analysis and prediction of cyclic economic phenomena.