This paper presents a novel approach to sustainable urban revitalization. Care Spaces are defined, and an area selected for revitalization is described. Transformation of city space is of fundamental importance for everyday life, its comfort as regards the functional aspect as well as the psychological and cultural ones. The presented projects are in accord with 2030 Agenda for Sustainable Development and the conception of Baukultur. Both approaches tend to create well designed environment that support health and well-being of people and other living creatures while taking into account cultural aspects in design and construction. Focusing on the combination of soundscape analysis with design elements. To monitor the soundscape, a custom database of urban sound recordings was constructed, and key analytical methods such as Mel-Frequency Cepstral Coefficients (MFCC), feature extraction, Recurrent Neural Networks (RNN), and permutation of feature importance were applied. The effectiveness of these algorithms was confirmed through field investigations.

The present work discusses the classification of distribution of values of impulses. We solve the problem in which random impulses occur before the vibrations caused by previous random impulses expire. The goal was to achieve a correct classification of three groups of distributions characteristic of three different processes: correct, transitory and flawed. The starting moment of the analysis as well as the length of time interval are the objects of study of this article. In order to determine the significance of the extracted features of the random time series under study, SHapley Additive exPlanations method was applied. The paper discussed the stage of study which is focused on finding the first three stochastic raw moments that allow for the most precise classification of distributions characterizing different processes in the shortest time. The analysis using the random forest classifier made it possible to distinguish the distributions characteristic for different types of processes, that is, it is possible to distinguish correct processes and transitory ones, etc. It is hardest to distinguish correct processes from one another, since they are highly similar to one another, but different from the other types.

The paper describes the model of an oscillator with damping, whose vibrations are forced by a random series of impulses. The mathematical model of the inverse problem used to calculate the distributions can only be applied when the values of the random impulses are known. If impulse values cannot be estimated based on the vibration signal, machine learning algorithms and feature engineering should be used to determine their distribution. In the discussed paper, unsupervised machine learning (specifically, the agglomerative hierarchical clustering) is employed to evaluate the applicability of the algorithms to the problem of recognizing the magnitudes of random impulses and characterizing their distributions.

There are many studies that present mathematical models of a system’s response to excitation by a random sequence of impulses, but none of these models has been experimentally verified. This article presents a study investigating the discrepancies between a mathematical model, which includes...