國立虎尾科技大學 |

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning./
作者:	Wei, Yumou.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
標題:	Information technology. -
電子資源:	click for full text (PQDT)
ISBN:	9798383194478

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning.
Wei, Yumou.

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning. - 1 online resource (152 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Thesis (Ph.D.)--Columbia University, 2024.

Includes bibliographical references

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Among various diagnostics, optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications.This thesis reports the first application of high-speed imaging videography and deep learning as real-time diagnostics of rotating MHD modes in a tokamak device. The developed system uses a convolutional neural network (CNN) to predict the amplitudes of the \uD835\uDC5B=1 sine and cosine mode components using solely optical measurements acquired from one or more cameras. Using the newly assembled high-speed camera diagnostics on the High Beta Tokamak - Extended Pulse (HBT-EP) device, an experimental dataset consisting of camera frame images and magnetic-based mode measurements was assembled and used to develop the mode-tracking CNN model. The optimized models outperformed other tested conventional algorithms given identical image inputs.A prototype controller based on a field-programmable gate array (FPGA) hardware was developed to perform real-time mode tracking using the high-speed camera diagnostic with the mode-tracking CNN model. In this system, a trained model was directly implemented in the firmware of an FPGA device onboard the frame grabber hardware of the camera's data readout system. Adjusting the model size and its implementation-related parameters allowed achieving an optimal trade-off between a model's prediction accuracy, its FPGA resource utilization and inference speed. Through fine-tuning these parameters, the final implementation satisfied all of the design constraints, achieving a total trigger-to-output latency of 17.6 \uD835\uDF07s and a throughput of up to 120 kfps. These results are on-par with the existing GPU-based control system using magnetic sensor diagnostic, indicating that the camera-based controller will be capable to perform active feedback control of MHD modes on HBT-EP.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383194478Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

High-speed imagingIndex Terms--Genre/Form:

554714
Electronic books.

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning.
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