scholarly journals Using Deep Neural Networks to Extract Fireball Parameters from Infrared Spectral Data

2020 ◽  
Author(s):  
Derek Elswick Armstrong ◽  
Joseph Gabriel Gorka
Keyword(s):  
Neural Networks ◽  
Spectral Data ◽  
Deep Neural Networks ◽  
Infrared Spectral

scholarly journals Analysis of the Oil Content of Rapeseed Using Artificial Neural Networks Based on Near Infrared Spectral Data

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Dazuo Yang ◽  
Hao Li ◽  
Chenchen Cao ◽  
Fudi Chen ◽  
Yibing Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spectral Data ◽  
Oil Content ◽  
Near Infrared ◽  
Nonlinear Method ◽  
Practical Applications ◽  
Infrared Spectral ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Analytical Approaches

The oil content of rapeseed is a crucial property in practical applications. In this paper, instead of traditional analytical approaches, an artificial neural network (ANN) method was used to analyze the oil content of 29 rapeseed samples based on near infrared spectral data with different wavelengths. Results show that multilayer feed-forward neural networks with 8 nodes (MLFN-8) are the most suitable and reasonable mathematical model to use, with an RMS error of 0.59. This study indicates that using a nonlinear method is a quick and easy approach to analyze the rapeseed oil’s content based on near infrared spectral data.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

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