Wyniki wyszukiwania - Biblioteka Nauki

1

Assessment of the Electromagnetic Radiation Level of vehicles, computers, household appliances, personal care products, mobile phones and smartphones

100%

Przystupa K. , Pohrebennyk V. , Mitryasova O. , Kochan O.

Przegląd Elektrotechniczny

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2020

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tom R. 96, nr 1

190--193

EN

The results of experimental studies are presented the level of electromagnetic radiation of vehicles, computers, household appliances, personal care products, mobile phones and smartphones.

PL

Wyniki badań eksperymentalnych przedstawiają poziom promieniowania elektromagnetycznego pojazdów, komputerów, sprzętu gospodarstwa domowego, produktów higieny osobistej, telefonów komórkowych i smartfonów.

2

Electromagnetic Pollution: Case study of energy transmission lines and radio transmission equipment

88%

Przystupa K. , Vasylkivskyi I. , Ishchenko V. , Pohrebennyk V. , Kochan O.

Przegląd Elektrotechniczny

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2020

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tom R. 96, nr 2

52--

EN

General characteristics of electromagnetic pollution in towns is analyzed. Health statistics for people living under the influence of electromagnetic radiation is analysed. The electromagnetic pollution is measured for the case studies of energy transmission lines and radio transmission equipment in Ukraine. The induction of magnetic field near overhead power lines is found in the range of 5.5–10.5 μT while being 5 times less at the distance of 50 m. Also, strong dependence of electromagnetic field strength on the distance from radio transmitters is proved.

PL

W artykule analizowana jest ogólna charakterystyka zanieczyszczenia elektromagnetycznego w miastach. Pokazano statystyki zdrowotne osób żyjących pod wpływem promieniowania elektromagnetycznego. Zanieczyszczenie elektromagnetyczne mierzone jest dla przykładów linii przesyłowych energii i urządzeń transmisji radiowej na Ukrainie. Indukcja pola magnetycznego w pobliżu napowietrznych linii energetycznych znajduje się w zakresie 5,5–10,5 μT i jest 5 razy mniejsza w odległości 50 m. W artykule pokazano również silną zależność natężenia pola elektromagnetycznego od odległości od nadajników radiowych.

3

Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes

75%

Sun S. , Przystupa K. , Wei M. , Yu H. , Ye Z. , Kochan O.

Eksploatacja i Niezawodność

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2020

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tom Vol. 22, no. 4

730--740

EN

Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.

4

Comparison of methods for correcting outliers in ECG-based biometric identification

75%

Jun S. , Szmajda M. , Khoma V. , Khoma Y. , Sabodashko D. , Kochan O. , Wang J.

Metrology and Measurement Systems

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2020

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tom Vol. 27, nr 3

387--398

EN

The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.

5

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

63%

Chen X. , Przystupa K. , Ye Z. , Chen F. , Wang C. , Liu J. , Gao R. , Wei M. , Kochan O.

Eksploatacja i Niezawodność

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2022

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tom Vol. 24, no. 1

153--162

EN

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.