Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network
With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. The relative output intensities of HMF sensor at dif...
Guardado en:
Autores principales: | Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/8c7793bcc0a54932b21f588ace0db1f6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Effects of Tungsten Disulphide Coating on Tapered Microfiber for Relative Humidity Sensing Applications
por: Norazida Ali, et al.
Publicado: (2021) -
Improvised centrifugal spinning for the production of polystyrene microfibers from waste expanded polystyrene foam and its potential application for oil adsorption
por: Marco Laurence M. Budlayan, et al.
Publicado: (2021) -
Framed slant helices in Euclidean 3-space
por: Osman Zeki Okuyucu, et al.
Publicado: (2021) -
Study of Pressure and Curing Temperature in Reactive Powder Concretes (RPC) with different amounts of Metallic Microfibers
por: Christ,R, et al.
Publicado: (2013) -
Use of experimental design to obtain polymeric microfibers with carbon nanotubes
por: Andressa Giombelli Rosenberger, et al.
Publicado: (2020)