Anachkova, Maja

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Anachkova, Maja
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Anachkova, Maja

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    Statistical Analysis of Urban Noise Measurements Data: Case Study for the City of Skopje
    (EuroRegio, 2022)
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    Nikolovski, Filip
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    Noise from the road transport, particularly from vehicles in urban city areas largely accounts for the general noise level and annoyance of the citizens. The numerous volumes of motor vehicles flow can be treated statistically, which can establish a deeper insight into the contribution of the road noise to the prevalent noise pollution and its' characteristics. According to ISO 362 and ISO 1996:2, the environmental noise level from traffic is highly dependent on the vehicle category regarding the factor of contribution to the overall urban noise level. The purpose of this study is to analyze the dependence between the number and types of vehicles and measured standardized parameters (Leq, LAF and L95) for noise level assessment by implementing a statistical model analysis ofthe collected results. The number and the type of the vehicles is obtained from the States' traffic management and control center for a chosen road in the center of the city, whereas noise level measurements have been conducted with a Bruel&Kjaer sound level meter by using a standardized noise level measurement methodology procedure for the selected period on the given location. This study provides a detailed statistical approach of the collected noise and traffic volume data to obtain conclusions and prediction models for further management of the noise pollution problem in the city.
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    A Multimodal Hybrid Piezoelectric–Electromagnetic Vibration Energy Harvester Exploiting the First and Second Resonance Modes for Broadband Low-Frequency Applications
    (MDPI AG, 2026-03-27)
    Shishkovski, Dejan
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    Markovska, Simona Domazetovska
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    Pecioski, Damjan
    The increasing demand for autonomous wireless sensors in Internet of Things (IoT) ap-plications has intensified research on vibration energy harvesting, particularly in the low-frequency range where ambient vibrations are most prevalent. However, most vibra-tion energy harvesters operate efficiently only at a single resonance mode, resulting in a narrow operational bandwidth and pronounced performance degradation under fre-quency detuning. To address this limitation, this paper proposes a multimodal hybrid pi-ezoelectric–electromagnetic vibration energy harvester that exploits both the first and sec-ond resonance modes of a cantilever-based structure to achieve broadband low-frequency operation. The design is guided by the complementary utilization of strain-dominated and velocity-dominated regions associated with different vibration modes. Numerical model-ing and finite element simulations are employed to investigate the influence of mass dis-tribution, deformation characteristics, and relative velocity on energy conversion perfor-mance. A secondary cantilever carrying the electromagnetic coil is introduced to enhance the relative motion between the coil and the magnetic field, thereby extending the effective operational bandwidth. The experimental results demonstrate increased harvested power, improved energy conversion efficiency, and a significantly broadened effective frequency range compared to conventional single-mode piezoelectric and electromagnetic energy harvesters.
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    Fault Diagnosis of Rotating Machinery Using Supervised Machine Learning Algorithms with Integrated Data-Driven and Physics-Informed Feature Sets
    (MDPI AG, 2026-03-17)
    Ignjatovska, Anastasija Angjusheva
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    Shishkovski, Dejan
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    Domazetovska Markovska, Simona
    This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed fea-ture 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 analy-sis, frequency-domain analysis, baseband analysis, and envelope analysis. From the cor-responding signal representations, statistical, spectral, and physics-based features associ-ated with characteristic fault frequencies were extracted and combined into integrated feature sets. The diagnostic performance of models trained using purely data-driven fea-tures 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 con-ditions confirmed the robustness and generalization capability of the proposed diagnostic framework.