Development of de-noised image reconstruction technique using Convolutional AutoEncoder for fast monitoring of fuel assemblies
The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1–2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-...
Main Authors: | Se Hwan Choi, Hyun Joon Choi, Chul Hee Min, Young Hyun Chung, Jae Joon Ahn |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-03-01
|
Series: | Nuclear Engineering and Technology |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573320308561 |
Similar Items
-
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
by: Hsin-Tien Chiang, et al.
Published: (2019-01-01) -
Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise
by: Deshan Feng, et al.
Published: (2021-05-01) -
Emotion Recognition using AutoEncoders and Convolutional Neural Networks
by: Luis Antonio Beltrán Prieto, et al.
Published: (2018-06-01) -
Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network
by: Zhiyu Qu, et al.
Published: (2019-01-01) -
Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks
by: Ebtesam Almazrouei, et al.
Published: (2021-04-01)