Showing 2 results for Neural Network
Jafar Bakhtiar Shohani, Morteza Hajimahmoodzadeh, Hamidreza Fallah,
Volume 16, Issue 2 (7-2022)
Abstract
In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two kinds of intensity are used, one is in-focus and the other is out-of-focus of the telescope. After these simulations, a convolutional neural network (CNN) is configured and designed and its input is simulated intensity patterns. After learning the network, we could recognize double stars at severe turbulence without needing phase correction with a very high accuracy level of more than 98%.
Ehsan Adibnia, Majid Ghadrdan, Mohammad Ali Mansouri-Birjandi,
Volume 17, Issue 2 (6-2023)
Abstract
This research addresses the complexities and inefficiencies encountered in fabricating fiber Bragg gratings (FBGs), which are crucial for applications in optical communications, lasers, and sensors. The core challenge lies in the intricate relationship between fabrication parameters and the FBG's physical properties, making optimization time-consuming. To circumvent these obstacles, the study introduces an artificial intelligence-based approach, utilizing a neural network to predict FBG physical parameters from transmission spectra, thereby streamlining the fabrication process. The neural network demonstrated exceptional predictive accuracy, significantly reducing the parameter prediction time from days to seconds. This advancement offers a promising avenue for enhancing the efficiency and precision of FBG sensor design and fabrication. The research not only showcases the potential of artificial intelligence in revolutionizing FBG production but also contributes to the broader field of optical technology by facilitating more rapid and informed design decisions, ultimately paving the way for developing more sophisticated and sensitive FBG-based applications.
