The design of automotive suspension systems involves compromise between the ride quality and driving stability. The change of the damping and stiffness coefficients could provide adaptive characteristic of the suspension system. Air springs, as suspension elements, could provide change of the stiffness coefficient by simply including or excluding additional volumes and thus could provide a controlled variable spring rate. Study of the influence of air springs on the vehicle vertical dynamics, requires use of dynamic air spring models. This paper deals with air spring models. Dynamic model of an air spring with frequency dependent characteristics is presented. The flow resistance in air springs is present when two volumes are connected and it depends on the design characteristics of the connecting pipeline. In order to have the dynamic model without many experimentally obtained parameters, the methodology for defining flow resistance coefficients is developed and presented in this paper. The verification of the dynamic air spring model is done by an experiment. The simulation and experimental results are analysed.

This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating conditions was used for model training. Parallel signal processing techniques were applied to capture fault-related information across multiple frequency bands including time-domain analysis, frequency-domain analysis, baseband analysis, and envelope analysis. From the corresponding signal representations, statistical, spectral, and physics-based features associated with characteristic fault frequencies were extracted and combined into integrated feature sets. The diagnostic performance of models trained using purely data-driven features was systematically compared with models incorporating integrated data-driven and physics-informed features. Support Vector Machine, Random Forests, Gradient Boosting, and an ensemble classifier were evaluated using accuracy, precision, recall, and F1-score metrics. The proposed framework employs a two-layer classification strategy, where the first layer performs multiclass fault identification, while the second layer evaluates the presence of imbalance as a coexisting fault. In addition, the influence of different feature groups as well as individual measurement axes and their combinations on diagnostic performance were analyzed. Validation using a new dataset measured in laboratory conditions confirmed the robustness and generalization capability of the proposed diagnostic framework.

Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities.

The lockdown that the city of Skopje has suffered during the months of April to June 2020 in order to control the spread of COVID-19 has significantly changed the acoustic environment in the urban parts of the city. The absence of vehicles, people on the streets and closed restaurants has led to a noise reduction captured by the low-cost wireless sensor network in the City of Skopje. The analysis carried out in this paper show reduction in noise pollution strongly correlated with the population's activity and behavior to the new circumstances. Overall, the sound pressure levels vary around 65 dB; however, some extreme decrease can be noticed, especially during the quarantine weekends. Also, the noise levels were compared between the same time periods during the year of 2019 and 2020, where it can be perceived reduction in the sound level for 36,5% for the day-evening-night noise level indicator (L). Significant variations occur for the indicators L, L, and L, especially during the lockdown weekends.