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2 2025

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Searching form the model of human facial expressions and its implementation in MATLAB and Simulink Environments

Tchórzewski Jerzy Rudolf, Rosiński Jakub

Studia Informatica : systems and information technology

2025

vol. 1(32)

93--111

The publication contains selected research results on the creation of a neural model of human facial expressions along with its implementation in the MATLAB and Simulink environments using the library called Deep Learning Toolbox. The model was generated as an object to recognize selected human faces and emotional states based on visual data recorded on the human face in real time. The study was placed against the background of available literature on the analysis of facial expressions and emotion classification methods. In addition to the concept of the original solution, the assumptions of the research experiment were given, a method for measuring facial expressions was selected for the experimental conditions, and a set of data was developed for training a neural model of the facial expression system with their preparation for ANN learning. Ultimately, various artificial neural networks were trained to model the facial expression system and sensitivity and comparative tests were performed in Simulink to, among others: assess the quality of the model in relation to real data. Very high results of ANN training of the facial expression system model were obtained (MSE on the order of 10−14, R close to 1), as well as relatively high model quality relative the facial expression system, measured, among others, average relative error, the value of which was several percent. However, in terms of prediction effectiveness, because of the obtained results, which are not very high for the assumed high measurement accuracies, the research that has been started is continued.

Functioning of an intelligent system on the example of the National Power Demand System

Tchórzewski Jerzy Rudolf, Marlęga Radosław, Nabiałek Wojciech

Studia Informatica : systems and information technology

2025

vol. 1(32)

47--60

This publication includes a concept and research results regarding the search for regularity in the functioning of an intelligent system using the example of a system for forecasting national power demand. The research employs the ARX regression machine learning method to derive models of the functioning of the electricity forecasting system. The study uses actual values of electricity generated by both uniform and non-uniform domestic electricity systems as input quantities, and forecasted national power demand as the output values from January 2023. The main aim of the research was to obtain several hourly models and several monthly models to demonstrate changes in selected system parameters and to show whether, and what changes occur in the direction of increasing the system’s independence, enhancing the system control level, etc. The research methodology also employs, in addition to the machine learning method, the method of control theory and systems. At the same time, an example of obtaining a model of the national system demand for electric power using the ARX machine learning method, which was transformed into state space model, whose matrix elements were used to interpret the correctness of changes in the intelligent system.