Results 101 to 110 of about 2,015 (219)

GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad-Shafranov equation. [PDF]

Sci Rep, 2023
Joung S, Ghim YC, Kim J, Kwak S, Kwon D, Sung C, Kim D, Kim HS, Bak JG, Yoon SW.   +9 more
europepmc   +1 more source

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data. [PDF]

Nat Commun, 2022
Crowl S, Jordan BT, Ahmed H, Ma CX, Naegle KM.   +4 more
europepmc   +1 more source

Forward model of KSTAR Thomson scattering diagnostic system [PDF]

, 2017
학위논문(석사) - 한국과학기술원 : 원자력및양자공학과, 2017.2,[v, 52 p. :]톰슨 산란 진단 장치는 핵융합 장치에서 플라즈마 전자의 온도와 밀도를 측정하는데 이용되고 있다. 본 학위 논문에서는 우선 KSTAR 톰슨 산란 진단 시스템에 대해 자세히 살펴본다. 그리고 KSTAR 톰슨 산란 진단 시스템으로 구한 플라즈마 전자의 온도와 밀도가 과연 얼마나 신뢰할 수 있는가를 평가하기 위해 포워드 모델을 도입한다.
김건희
core  

Radiation-front position control with scenario adjustments during the first tungsten divertor operation on KSTAR

Nuclear Fusion
This study reports the first experimental demonstration of a radiation front-based detachment control system during the initial tungsten divertor campaign in KSTAR.
C.S. Byun, S. Oh, S.K. Kim, B.S. Kim, Y.S. Han, S.H. Hahn, H.H. Lee, A. Jalalvand, Y.H. Lee, J.H. Hwang, R. Shousha, J.-S. Park, E. Kolemen   +12 more
doaj   +1 more source

Early detection of student degree-level academic performance using educational data mining. [PDF]

PeerJ Comput Sci, 2023
Meghji AF, Mahoto NA, Asiri Y, Alshahrani H, Sulaiman A, Shaikh A.   +5 more
europepmc   +1 more source

OVERVIEW OF KSTAR RESULTS IN PHASE-I OPERATION [PDF]

, 2013
The KSTAR (Korea Superconducting Tokamak Advanced Research) pursued to develop key technologies for superconducting tokamak operation and to contribute to a few research items for ITER relevant issues. As a result, the KSTAR achieved highly confined mode
Oh, Y. K., Cho, M. H., Park, Y. S., Park, Y. M., Mueller, D., Chu, Y., Joung, S. H., Na, Y. S., Inoue, T., Sabbagh, S. A., Grisham, L. R., Eidietis, N. W., Choe, Wonho, In, S. R., Ahn, J. W., Kim, W. C., Lee, S. G., Kim, H. K., Gorelov, Y., Ellis, R., Walker, M., Kwak, J. G., Watanabe, K., Park, K. R., Hyatt, A. W., Park, H. K., Hwang, Y. S., Kim, J. Y., Park, M. K., Litaudon, X., Oda, Y., Kwon, M., Kajiwara, K., Hoang, T. T., Leuer, J. A., Sakamoto, K., Bae, Y. S., Hosea, J., Delpech, L., Yang, H. L., Namkung, W., Lohr, J., Park, J. G., Kim, J. S.   +43 more
core  

Preliminary assessment of the safety factors in K-DEMO for fusion compatible regulatory framework. [PDF]

Sci Rep, 2022
Kim BS, Hong SH, Kim K.
europepmc   +1 more source

Are We There Yet? Predicting Conversion to Psychosis Using Machine Learning. [PDF]

Am J Psychiatry, 2023
Smucny J, Davidson I, Carter CS.
europepmc   +1 more source

Assessment of Model Accuracy in Eyes Open and Closed EEG Data: Effect of Data Pre-Processing and Validation Methods. [PDF]

Bioengineering (Basel), 2022
Mattiev J, Sajovic J, Drevenšek G, Rogelj P.   +3 more
europepmc   +1 more source

Observation of minor and major disruption processes in the KSTAR plasmas with ECEI diagnostics [PDF]

, 2014
Yun, Gunsu, Park, Hyeon Keo, Luhmann, Jr, NC, KSTAR team, Lee, Woochang, Choi, Minjun, Domier, CW   +6 more
core