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Using a stereo camera system, a new diagnostic for the safety factor of the core plasma based on the pellet ablation trail is applied on the Experimental Advanced Superconducting Tokamak (EAST). In EAST discharge No. 128 874, a shattered pellet injection system is applied to inject a shattered neon pellet into the EAST. Since the strong magnetic field in tokamaks binds the ablated pellet material, the orientation of the pellet ablation trail is the same as the local magnetic field direction. Thus, from the three-dimensional reconstruction result of the pellet ablation trail, the local safety factor q can be obtained. The motional Stark effect (MSE) diagnostic is applied to determine the safety factor q profile in this shot. The determined safety factor q results for this new diagnostic are in quantitative agreement with those from the MSE diagnostic with the mean relative difference of only 6.8%, confirming the effectiveness of this new diagnostic of the safety factor. [ABSTRACT FROM AUTHOR]

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails. [ABSTRACT FROM AUTHOR]