Open Access
Issue
E3S Web Conf.
Volume 477, 2024
International Conference on Smart Technologies and Applied Research (STAR'2023)
Article Number 00039
Number of page(s) 25
DOI https://doi.org/10.1051/e3sconf/202447700039
Published online 16 January 2024
  1. Barbarino, M. A brief history of nuclear fusion. Nature Physics, 16(9), 890-893, (2020). [CrossRef] [Google Scholar]
  2. Bohr, N., & Wheeler, J. A. The mechanism of nuclear fission. Physical Review, 56(5), 426 (1939). [CrossRef] [Google Scholar]
  3. Cohen, B. L. The disposal of radioactive wastes from fission reactors. Scientific American, 236(6), 21-31, (1977) [CrossRef] [PubMed] [Google Scholar]
  4. Schumacher, U. Status and problems of fusion reactor development. Naturwissenschaften, 88(3), 102-112, (2001) [CrossRef] [PubMed] [Google Scholar]
  5. Boozer, A. H. Theory of tokamak disruptions. Physics of plasmas, 19(5), 058101, (2012) [CrossRef] [Google Scholar]
  6. Sartori, F., de Tommasi, G. I. A. N. M. A. R. I. A., & Piccolo, F. The joint European torus. IEEE Control Systems Magazine, 26(2), 64-78, (2006) [Google Scholar]
  7. Li, J., & Wan, Y. The experimental advanced superconducting tokamak. Engineering, 7(11), 1523-1528, (2021) [CrossRef] [Google Scholar]
  8. Luxon, J. L. A design retrospective of the DIII-D tokamak. Nuclear Fusion, 42(5), 614, (2002) [CrossRef] [Google Scholar]
  9. Svoboda, V., Bromova, E., Duran, I., Grover, O., Kocman, J., Markovic, T., ... & Zara, J. The golem tokamak for fusion education. In Contributed Papers 38th European Physical Society Conference on Plasma Physics, Eu-rophysics conference abstracts G (Vol. 35), (2011) [Google Scholar]
  10. Bhatt, S. B., Ghosh, J., & Tanna, R. L. The upgradation of Aditya Tokamak. In Proceedings of the tenth Asia plasma and fusion association conference: book of abstracts, (2015) [Google Scholar]
  11. Stacey, Weston M. Fusion plasma physics. John Wiley & Sons, 2005. [CrossRef] [Google Scholar]
  12. Kikuchi, M. “A note on the Mirnov signal analysis in tokamaks.” Nuclear fusion 26.1: 101(1986). [CrossRef] [Google Scholar]
  13. Figueiredo, A. C. A., M. F. F. Nave, and EFDA–JET Contributors. “Time–frequency analysis of non-stationary signals in fusion plasmas using the Choi–Williams distribution.” Nuclear fusion 44.10: L17, (2004). [CrossRef] [Google Scholar]
  14. Figueiredo, A. C. A., M. F. F. Nave, and EFDA-JET contributors. “Time–frequency analysis of nonstationary fusion plasma signals: a comparison between the Choi–Williams distribution and wavelets.” Review of scientific instruments 75.10 :4268-4270, (2004). [CrossRef] [Google Scholar]
  15. Coelho, R., D. Alves, and C. Silva. “Magnetohydrodynamic and turbulence activity analysis in the ISTTOK tokamak using empirical mode decomposition.” Review of scientific instruments 77.10 :10F512, (2006). [Google Scholar]
  16. Jha, R., D. Raju, and A. Sen. “Analysis of tokamak data using a novel Hilbert transform based technique.” Physics of plasmas 13.8 : 082507, (2006). [CrossRef] [Google Scholar]
  17. Hole, M. J., and Lynton C. Appel. “Fourier decomposition of magnetic perturbations in toroidal plasmas using singular value decomposition.” Plasma Physics and Controlled Fusion 49.12 :1971, (2007). [CrossRef] [Google Scholar]
  18. Coelho, R., and D. Alves. “Real-time estimation of the poloidal wavenumber of ISTTOK tokamak magnetic fluctuations.” Review of Scientific Instruments 79.10 :10F121, (2008). [Google Scholar]
  19. C. Marchetto, F. Gandini, et al., In AIP Conference Proceedings (Vol. 1187, No. 1, pp. 519-522). American Institute of Physics, (2009). [CrossRef] [Google Scholar]
  20. Galperti, C., et al. “Development of real-time MHD markers based on biorthogonal decomposition of signals from Mirnov coils.” Plasma Physics and Controlled Fusion 56.11 :114012, (2014). [CrossRef] [Google Scholar]
  21. Kim, J. S., et al. “MHD mode identification of tokamak plasmas from Mirnov signals.” Plasma physics and controlled fusion 41.11 : 1399, (1999). [CrossRef] [Google Scholar]
  22. Alves, Diogo, and Rui Coelho. “Kalman filter methods for real-time frequency and mode number estimation of MHD activity in tokamak plasmas.” Plasma Physics and Controlled Fusion 55.10 :105003, (2013). [CrossRef] [Google Scholar]
  23. Saadat, Shervin, et al. “Comparison study of fourier and SVD method for plasma mode analysis in Tokamaks.” Journal of fusion energy 30.1 : 100-104, (2011). [CrossRef] [Google Scholar]
  24. Xu, L. Q., et al. “Time–frequency analysis of nonstationary complex magneto-hydro- dynamics in fusion plasma signals using the Choi–Williams distribution.” Fusion Engineering and Design 88.11 : 2767-2772, (2013). [CrossRef] [Google Scholar]
  25. Goodarzi, Z., M. Ghoranneviss, and A. Salar Elahi. “Investigation of tokamak plasma MHD activity using FFT analysis of Mirnov coils oscillations.” Journal of Fusion Energy 32.1 :103-106, (2013). [CrossRef] [Google Scholar]
  26. Mirmoeini, S. R., A. Salar Elahi, and M. Ghoranneviss. “Analysis of tokamak plasma confinement modes using the fast Fourier transformation.” Pramana 87.5 :1-6, (2016). [Google Scholar]
  27. M. R. Ghanbari, M. Ghoranneviss, & M. N. Ardebili, International journal of hydrogen energy, 43(24), 11173, (2018) [CrossRef] [Google Scholar]
  28. Jha, R., D. Raju, and A. Sen. “Analysis of tokamak data using a novel Hilbert transform based technique.” Physics of plasmas 13.8 : 082507, (2006). [CrossRef] [Google Scholar]
  29. Liu, Yangqing, et al. “Time-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform.” Review of Scientific Instruments 85.7 : 073502, (2014). [CrossRef] [PubMed] [Google Scholar]
  30. H. Faridyousefi, et al., Journal of Fusion Energy, 39(6), 512, (2020) [CrossRef] [Google Scholar]
  31. Cannas, Barbara, et al. “Support vector machines for disruption prediction and novelty detection at JET.” Fusion engineering and design 82.5-14 : 1124-1130, (2007). [CrossRef] [Google Scholar]
  32. G. Sias, B. Cannas, et al., In 2019 PhotonIcs & Electromagnetics Research Symposium- Spring(PIERS-Spring) (pp. 2880-2890), IEEE, (2019, June) [Google Scholar]
  33. Camplani, Massimo, et al. “Tracking of the plasma states in a nuclear fusion device using SOMs.” Neural Computing and Applications 20.6 : 851-863, (2011). [CrossRef] [Google Scholar]
  34. Cannas, Barbara, et al. “Manifold learning to interpret JET high-dimensional operational space.” Plasma Physics and Controlled Fusion 55.4 : 045006, (2013). [CrossRef] [Google Scholar]
  35. Cannas, Barbara, et al. “Overview of manifold learning techniques for the investigation of disruptions on JET.” Plasma Physics and Controlled Fusion 56.11 : 114005, (2014). [CrossRef] [Google Scholar]
  36. Haykin, Simon. “Neural Networks, a comprehensive foundation, Prentice-Hall Inc.” Upper Saddle River, New Jersey 7458 : 161-175, (1999). [Google Scholar]
  37. Zedda, MARIA KATIUSCIA, et al. “Disruption classification at JET with neural techniques.” Proc. 30th EPS Conf. on Controlled Fusion and Plasma Physics, (2003). [Google Scholar]
  38. Sharkey, Amanda JC. “Linear and order statistics combiners for pattern classification.” Combining artificial neural nets. Springer, London, 127-161, (1999). [Google Scholar]
  39. B. Cannas, A. Fanni, et al., In PIERS Progress in Electromagnetics Research Symposium (pp. 28-31), (2004) [Google Scholar]
  40. Cannas, Barbara, et al. “Disruption forecasting at JET using neural networks.” Nuclear fusion 44.1 : 68, (2003). [Google Scholar]
  41. Ferreira, Diogo R., Pedro J. Carvalho, and Horácio Fernandes. “Deep learning for plasma tomography and disruption prediction from bolometer data.” IEEE Transactions on Plasma Science 48.1 : 36-45, (2019). [Google Scholar]
  42. Huber, Alexander, et al. “Upgraded bolometer system on JET for improved radiation measurements.” Fusion Engineering and Design 82.5-14 : 1327-1334, (2007). [CrossRef] [Google Scholar]
  43. LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 : 2278-2324, (1998). [CrossRef] [Google Scholar]
  44. Moreno, R., et al. “Robustness and increased time resolution of JET Advanced Predictor of Disruptions.” Plasma Physics and Controlled Fusion 56.11 : 114003, (2014). [CrossRef] [Google Scholar]
  45. Vega, Jesús, et al. “Results of the JET real-time disruption predictor in the ITER-like wall campaigns.” Fusion Engineering and Design 88.6-8 :1228-1231, (2013). [CrossRef] [Google Scholar]
  46. Rattá, G. A., et al. “Improved feature selection based on genetic algorithms for real time disruption prediction on JET.” fusion Engineering and Design 87.9 : 1670-1678, (2012). [CrossRef] [Google Scholar]
  47. Zhang, Kai, et al. “Density limit disruption prediction using a long short-term memory network on EAST.” Plasma Science and Technology 22.11 :115602, (2020). [CrossRef] [Google Scholar]
  48. [48] Guo, B. H., et al. “Disruption prediction using a full convolutional neural network on EAST.” Plasma Physics and Controlled Fusion 63.2 : 025008, (2020). [Google Scholar]
  49. Wang, Bo, et al. “Establishment and assessment of plasma disruption and warning databases from EAST.” Plasma Science and Technology 18.12 : 1162, (2016). [CrossRef] [Google Scholar]
  50. Guo, B. H., et al. “Disruption prediction on EAST tokamak using a deep learning algorithm.” Plasma Physics and Controlled Fusion 63.11: 115007, (2021). [CrossRef] [Google Scholar]
  51. BURRELL, HK. Overview of recent experimental results from the DIII-D advanced tokamak program. General Atomics, San Diego, CA (United States), (2002). [Google Scholar]
  52. Rea, Christina, et al. “A real-time machine learning-based disruption predictor in DIII-D.” Nuclear Fusion 59.9 : 096016, (2019). [CrossRef] [Google Scholar]
  53. Fu, Yichen, et al. “Machine learning control for disruption and tearing mode avoidance.” Physics of Plasmas 27.2 : 022501, (2020). [CrossRef] [Google Scholar]
  54. Boos, Dennis D. “Introduction to the bootstrap world.” Statistical science 18.2 : 168- 174, (2003). [Google Scholar]
  55. Geurts, Pierre, Damien Ernst, and Louis Wehenkel. “Extremely randomized trees.” Machine learning 63.1 : 3-42, (2006). [CrossRef] [Google Scholar]
  56. Freund, Yoav, Robert Schapire, and Naoki Abe. “A short introduction to boosting.” Journal- Japanese Society For Artificial Intelligence 14.771-780 : 1612, (1999). [Google Scholar]
  57. Eidietis, N. W., et al. “Implementing a finite-state off-normal and fault response system for disruption avoidance in tokamaks.” Nuclear Fusion 58.5 : 056023, (2018). [CrossRef] [Google Scholar]
  58. Louppe, Gilles, et al. “Understanding variable importances in forests of randomized trees.” Advances in neural information processing systems 26 (2013). [Google Scholar]
  59. Churchill, R. M., et al. “Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data.” Physics of Plasmas 27.6 : 062510, (2020). [CrossRef] [Google Scholar]
  60. Yun, G. S., et al. “Development of KSTAR ECE imaging system for measurement of temperature fluctuations and edge density fluctuations.” Review of Scientific Instruments 81.10 : 10D930, (2010). [Google Scholar]
  61. Rea, Cristina, and Robert S. Granetz. “Exploratory machine learning studies for disruption prediction using large databases on DIII-D.” Fusion Science and Technology 74.1-2 : 89-100, (2018). [CrossRef] [Google Scholar]
  62. Jayakumar Chandrasekaran, Surendar Madhawa, and J. Sangeetha. “Data-Driven-Based Disruption Prediction in GOLEM Tokamak with Missing Values.” Intelligent Systems, Technologies and Applications: Proceedings of Sixth ISTA 2020, India 1353 : 129, (2021). [Google Scholar]
  63. González, Sergio, et al. “A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities.” Information Fusion 64 : 205-237, (2020). [CrossRef] [Google Scholar]
  64. Chandrasekaran, Jayakumar, and Sangeetha Jayaraman. “Data-driven technique for disruption prediction in GOLEM tokamak using stacked ensembles with active learning.” Review of Scientific Instruments 93.3 : 033501, (2022). [CrossRef] [PubMed] [Google Scholar]
  65. Settles, Burr. “Active learning.” Synthesis lectures on artificial intelligence and machine learning 6.1 : 1-114, (2012). [Google Scholar]
  66. Chandrasekar, Jayakumar, Surendar Madhawa, and J. Sangeetha. “Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers.” Journal of Intelligent & Fuzzy Systems 39.6 : 8365-8376, (2020). [CrossRef] [Google Scholar]
  67. Loh, Wei‐Yin. “Classification and regression trees.” Wiley interdisciplinary reviews: data mining and knowledge discovery 1.1 : 14-23, (2011). [CrossRef] [Google Scholar]
  68. Shaik, Anjaneyulu Babu, and Sujatha Srinivasan. “A brief survey on random forest ensembles in classification model.” International Conference on Innovative Computing and Communications. Springer, Singapore, (2019). [Google Scholar]
  69. G. Pelletier, In Jets from Young Stars (pp. 77-101). Springer, Berlin, Heidelberg, (2007) [CrossRef] [Google Scholar]
  70. Jayakumar, C., and J. Sangeetha. “Kernellized support vector regressive machine based variational mode decomposition for time frequency analysis of Mirnov coil.” Microprocessors and Microsystems 75 : 103036, (2020). [CrossRef] [Google Scholar]
  71. Sengupta, A., and P. Ranjan. “Prediction of density limit disruption boundaries from diagnostic signals using neural networks.” Nuclear fusion 41.5 : 487, (2001). [CrossRef] [Google Scholar]
  72. Kleva, Robert G., and J. F. Drake. “Density limit disruptions in tokamaks.” Physics of Fluids B: Plasma Physics 3.2 :372-383, (1991). [CrossRef] [Google Scholar]
  73. Sudo, S., et al. “Scalings of energy confinement and density limit in stellarator/heliotron devices.” Nuclear Fusion 30.1 :11, (1990). [CrossRef] [Google Scholar]
  74. Agarwal, Aman, et al. “Using LSTM for the Prediction of Disruption in ADITYA Tokamak.” arXiv preprint arXiv:2007.06230 (2020). [Google Scholar]
  75. Sengupta, A., and P. Ranjan. “Forecasting disruptions in the ADITYA tokamak using neural networks.” Nuclear fusion 40.12 : 1993, (2000). [CrossRef] [Google Scholar]
  76. Agarwal, Aman, et al. “Deep sequence to sequence learning-based prediction of major disruptions in ADITYA tokamak.” Plasma Physics and Controlled Fusion 63.11 : 115004, (2021). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.