scholarly journals Machine Learning as a Tool to Aid in the Interpretation of Spectroscopic Data: Applications to Lunar and Planetary Exploration

2021 ◽  
Author(s):  
Prabhakar Misra ◽  
Dina Bower ◽  
Robert Coleman
Keyword(s):  
Machine Learning ◽  
Spectroscopic Data ◽  
Planetary Exploration

Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data

Food Control ◽  
2021 ◽  
pp. 108318
Author(s):  
Dimitrios Stefas ◽  
Nikolaos Gyftokostas ◽  
Panagiotis Kourelias ◽  
Eleni Nanou ◽  
Vasileios Kokkinos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectroscopic Data ◽  
Olive Cultivar ◽  
Olive Oils

scholarly journals Machine learning of two-dimensional spectroscopic data

2019 ◽  
Vol 520 ◽  
pp. 52-60 ◽  
Author(s):  
Mirta Rodríguez ◽  
Tobias Kramer
Keyword(s):  
Machine Learning ◽  
Spectroscopic Data ◽  
Two Dimensional

Prediction of Natural Product Classes Using Machine Learning and 13C NMR Spectroscopic Data

2020 ◽  
Vol 60 (7) ◽  
pp. 3376-3386 ◽  
Author(s):  
Saúl H. Martínez-Treviño ◽  
Víctor Uc-Cetina ◽  
María A. Fernández-Herrera ◽  
Gabriel Merino
Keyword(s):  
Machine Learning ◽  
Natural Product ◽  
13C Nmr ◽  
Spectroscopic Data

Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

Robotica ◽  
2021 ◽  
pp. 1-14
Author(s):  
Masafumi Endo ◽  
Shogo Endo ◽  
Kenji Nagaoka ◽  
Kazuya Yoshida
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Planetary Exploration ◽  
Estimation Accuracy ◽  
Mobility Prediction ◽  
Rough Terrain ◽  
Wheel Slip ◽  
Slope Estimation ◽  
3D Information

SUMMARY Wheel slip prediction on rough terrain is crucial for secure, long-term operations of planetary exploration rovers. Although rough, unstructured terrain hampers mobility, prediction by modeling wheel–terrain interactions remains difficult owing to unclear terrain conditions and complexities of terramechanics models. This study proposes a vision-based approach with machine learning for predicting wheel slip risk by estimating the slope from 3D information and classifying terrain types from image information. It considers the slope estimation accuracy for risk prediction under sharp increases in wheel slip due to inclined ground. Experimental results obtained with a rover testbed on several terrain types validate this method.

Correcting replicate variation in spectroscopic data by machine learning and model-based pre-processing

2021 ◽  
pp. 104350
Author(s):  
Valeria Tafintseva ◽  
Volha Shapaval ◽  
Uladzislau Blazhko ◽  
Achim Kohler
Keyword(s):  
Machine Learning ◽  
Spectroscopic Data ◽  
Model Based
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