@article{oai:nifs-repository.repo.nii.ac.jp:00011645,
 author = {YOKOYAMA, Tatsuya and YAMADA, Hiroshi and MASUZAKI,  Suguru and PETERSON,  Byron and PETERSON,  Byron J. and SAKAMOTO,  Ryuichi and GOTO,  Motoshi and OISHI,  Tetsutaro and KAWAMURA,  Gakushi and KOBAYASHI,  Masahiro and TSUJIMURA, Toru I. and MIZUNO,  Yoshinori and MIYAZAWA,  Junichi and MIYAZAWA, Jun-ichi and MUKAI,  Kiyofumi and TAMURA,  Naoki and MOTOJIMA,  Gen and IDA,  Katsumi},
 issue = {Special Issue 1},
 journal = {Plasma and Fusion Research Volume 17, 2402042},
 month = {Mar},
 note = {A radiative collapse predictor has been developed using a machine-learning model with high-density plasma experiments in the Large Helical Device (LHD). The model is based on the collapse likelihood, which is quantified by the parameters selected by the sparse modeling, including ne, CIV, OV, and Te,edge. The control system implementing this model has been constructed with a single-board computer to apply this predictor model to the LHD experiment. The controller calculates the collapse likelihood and regulates gas-puff fueling and boosts electron cyclotron resonance heating in real-time. In density ramp-up experiments with hydrogen plasma, high-density plasma has been maintained by the control system while avoiding radiative collapse. This result has shown that the predictor based on the collapse likelihood has the capability to predict a radiative collapse in real-time.},
 title = {Data-Driven Control for Radiative Collapse Avoidance in Large Helical Device},
 volume = {17},
 year = {2022}
}