Analyzing rocket plume spectral data with neural networks

1995 ◽  
Author(s):  
Kevin Whitaker ◽  
K Krishnakumar ◽  
Daniel Benzing
Keyword(s):  
Neural Networks ◽  
Spectral Data ◽  
Rocket Plume

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.

The Cocktail Party Listener

1994 ◽  
Vol 23 (482) ◽  
Author(s):  
A. R. Kian Abolfazlian ◽  
Brian K. Karlsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Model ◽  
Spectral Data ◽  
Spatial Data ◽  
Speaker Identification ◽  
Cocktail Party ◽  
Human Ability

A complex computational model of the human ability to listen to certain signals in preference of others, also called the cocktail party phenomenon, is built on the basis of surveys into the relevant psychological, DSP, and neural network literature. This model is basically binaural and as such it makes use of both spectral data and spatial data in determining which speaker to listen to. The model uses two neural networks for filtering and speaker identification. Results from some experimentation with type and architecture of these networks are presented along with the results of the model. These results indicate that the model has a distinctive ability to focus on a particular speaker of choice.

Artificial neural networks in classification of NIR spectral data: Design of the training set

1996 ◽  
Vol 33 (1) ◽  
pp. 35-46 ◽  
Author(s):  
W. Wu ◽  
B. Walczak ◽  
D.L. Massart ◽  
S. Heuerding ◽  
F. Erni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Data ◽  
Training Set ◽  
Artificial Neural ◽  
Data Design

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.

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