Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Xi, Kopsaftopoulos, Fotis, Wu, Qi, Ren, He, Chang, Fu-Kuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982412/
https://www.ncbi.nlm.nih.gov/pubmed/29710832
http://dx.doi.org/10.3390/s18051379

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982412/
https://www.ncbi.nlm.nih.gov/pubmed/29710832
http://dx.doi.org/10.3390/s18051379

Ejemplares similares