Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices

• Published in: IEEE access Vol. 6; pp. 72345 - 72356
• Main Authors: Farias, Gonzalo, Fabregas, Ernesto, Dormido-Canto, Sebastian, Vega, Jesus, Vergara, Sebastian, Bencomo, Sebastian Dormido, Pastor, Ignacio, Olmedo, Alvaro
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; auto-encoder; Classification; Coders; Deep learning; Diagnostic systems; Feature extraction; Fusion reactors; future extraction; Heating systems; Image classification; Images classification; Machine learning; Nuclear fusion; Plasmas; Scattering; Support vector machines; Thermonuclear fusion; Thomson scattering
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2881832

Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach.