Szczegóły publikacji
Opis bibliograficzny
Cubic SVM neural classification algorithm for Self-Excited Acoustical System / Krzysztof LALIK, Mateusz KOZEK // W: MSM'2020 [Dokument elektroniczny] : Mechatronic Systems and Materials : 15th international conference : 1–3 July 2020, Białystok, Poland / eds. Z. Kulesza, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2020. — e-ISBN: 978-1-7281-6956-9. — S. 153–157. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 157, Abstr. — Abstract W: MSM 2020 [Dokument elektroniczny] : 15th international conference : Mechatronic Systems and Materials : 1–3 July 2020, Białystok, Poland : book of abstracts. — Wersja do Windows. — Dane tekstowe. — [Białystok : Bialystok University of Technology], [2020]. — S. 21. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.msm2020.pb.edu.pl/app/uploads/2020/06/MSM2020-BOOK-OF-ABSTRACTS.pdf [2020-08-31].
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129810 |
|---|---|
| Data dodania do BaDAP | 2020-09-09 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MSM49833.2020.9201724 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
This paper proposes a special classification system based on artificial neural networks. The algorithm was used to interpret the results for the Self-Excited Acoustical System (SAS) for ultrasonic stress measurement in elastic structures. The results obtained with the SAS system were transformed by Short-Timed Fourier Transform (STFT), and the resulting characterization images were used to train the artificial neural network using the Cubic SVM (Support Vector Machine) algorithm. Trained ANN was then used to classify materials on the basis of time-frequency characteristics. The article shows the principle of operation of the SAS system for materials such as stone, metal and composite material. The theoretical basis for usage of time-frequency transformations was presented, as well as the principle of operation of the Cubic SVM algorithm for classification tasks.
