Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Neural Networks on the DIII-D Tokamak-Reference-Cited by-同舟云学术

Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Neural Networks on the DIII-D Tokamak

Published:2022-11 Issue:11 Volume:50 Page:4126-4130
ISSN:0093-3813
Container-title:IEEE Transactions on Plasma Science
language:
Short-container-title:IEEE Trans. Plasma Sci.

Author:

Anand H.¹^ORCID,Sammuli B. S.¹^ORCID,Olofsson K. E. J.¹,Humphreys D. A.¹

Affiliation:

1. DIII-D National Fusion Facility, General Atomics, San Diego, CA, USA

Funder

U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a Department of Energy (DOE) Office of Science user facility

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Condensed Matter Physics,Nuclear and High Energy Physics

Link

https://ieeexplore.ieee.org/ielam/27/9966429/9810123-aam.pdf

Reference13 articles.

1. Real time equilibrium reconstruction for tokamak discharge control

2. A design retrospective of the DIII-D tokamak

3. DIII-D Integrated plasma control solutions for ITER and next-generation tokamaks

4. Implementing a finite-state off-normal and fault response system for disruption avoidance in tokamaks

5. JET real-time object-oriented code for plasma boundary reconstruction

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