E-viri
Recenzirano
Odprti dostop
-
Monteiro, A; de Oliveira, M; de Oliveira, R; da Silva, T
Electronics letters, 05/2018, Letnik: 54, Številka: 11Journal Article
To date, the authors are not aware of an in-depth investigation about embedded applications of the convolutional neural network (CNN) algorithm on small, lightweight, and low-cost hardware (e.g. microcontroller, FPGA, DSP, and Raspberry Pi) applied to detect faults in structural health monitoring (SHM) systems. In this Letter, the authors implement and evaluate both feasibility and performance of an embedded application of the CNN algorithm on the Raspberry Pi 3. The CNN-embedded algorithm quantifies and classifies dissimilarities between the frames representing healthy and damaged structural conditions. In a case study, the CNN-embedded application was experimentally evaluated using three piezoelectric patches glued onto an aluminium plate. The results reveal an impressively effective 100% hit rate. This performance may significantly impact the design and analysis of CNN-based SHM systems where embedded applications are required for identifying structural damage such as those encountered by aerospace structures, rotating machineries, and wind turbines.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.