People
Tetiana Reznychenko

2025, Young Transportation Engineers Conference 2024, Praha, České vysoké učení technické v Praze), p. 63-68), ISBN 978-80-01-07445-9, ISSN 2336-5382

Current methods for diagnosing the quality of the railway superstructure are mainly based on optical sensors, which are relatively expensive compared to acoustic sensors. As part of the HLUKOS research project, a pair of microphones is installed near the wheel-rail contact point on a diagnostic vehicle of the Railway Administration (Czech railway infrastructure manager). The research task is to detect when the sound level changes significantly. A likelihood ratio method has been used in this paper to detect abrupt changes, which is a current scientific topic. Experiments with different input thresholds are performed on a sample of 250 m of track data. Initial experimental results show that this method is meaningfully able to detect locations of abrupt changes with input threshold values h = 4.58 and number of steps from N = 5 to N = 40.

2025, Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics, Lisboa, SCITEPRESS – Science and Technology Publications, Lda), p. 219-226), ISBN 978-989-758-770-2, ISSN 2184-2809

This paper deals with the analysis of high-dimensional discrete data values from questionnaires, with the aim of identifying explanatory variables that influence a target variable. We propose a hybrid algorithm that combines categorical model estimation with an ant colony optimization scheme for feature selection. The main contributions are: (i) the efficient selection of the most significant explanatory variables, and (ii) the estimation of a categorical model with reduced dimensionality. Experimental results and comparisons with well-known algorithms (e.g., random forest, categorical boosting, k-nearest neighbors) and feature selection techniques are presented.

2024, 2024 Smart City Symposium Prague (SCSP), New York, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC), p. 1-5), ISBN 979-8-3503-6095-0, ISSN 2691-3666

The paper deals with a comparative analysis of three widely used data analysis methods: logistic regression, random forest, and neural networks. These methods have been evaluated in terms of accuracy, and computational efficiency and applied to different types of data sets, including both simulated and real MaaS data. The study aims to compare the efficiency of each method in classification tasks. The study leads to specific recommendations on which method to use under various circumstances, contributing to the decision-making process in data analysis projects. We have shown that random forests generally provide better accuracy and are resistant to over-training. Neural networks can achieve comparable performance under certain conditions, although at a high computational cost. Logistic regression shows limitations in dealing with complex data structures.

2023, 2023 Smart City Symposium Prague, New York, IEEE Press), p. 1-6), ISBN 979-8-3503-2162-3, ISSN 2691-3666

The paper deals with the analysis of histograms of discrete data collected in questionnaires obtained for individual realizations of the target variable. The main aim of the analysis isto explore the influence of combinations of explanatory variables, represented by responses to the questionnaire, on the behavior of the target variable of the questionnaire. In this paper, an automated approach to histogram comparison is proposed based on coding combinations of data and detecting significant differences in frequencies using the Marascuilo procedure. This is the main contribution of the paper. The approach is validated using a simulated questionnaire in which respondents answered regarding their intention to purchase an electric vehicle subject to finance, leasing, and charging availability, as well as their driving style. The results of the experiments are demonstrated.

2023, Proceedings of the The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) IDAACS’2023, Dortmund, IEEE), p. 51-56), ISBN 979-8-3503-5804-9, ISSN 2770-4254

The paper considers the problem of online prediction of a count variable based on real-time explanatory data of mixed count and categorical nature. The presented solution is based on (i) recursive Bayesian estimation of a mixture model of Poisson-distributed explanatory counts, using the categorical explanatory variable as a measurable pointer of the mixture, (ii) construction of a mixture of local Poisson regressions on the clustered data, and (iii) use of the pre-estimated mixtures for online prediction of the target count using actual measured explanatory data. The latter is one of the main contributions of the proposed approach. In addition, the dynamic model of the categorical explanatory variable preserves the functionality of the algorithm in case of its measurement failure. The experiments with simulations and real data report lower prediction errors compared to theoretical counterparts.