東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Optical-Based Microsecond Latency MH...

Wei, Yumou.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning./
作者:	Wei, Yumou.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	152 p.
附註:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
標題:	Plasma physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31330889
ISBN:	9798383194478

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

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 152 p.

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

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

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.

ISBN: 9798383194478Subjects--Topical Terms:

3175417
Plasma physics.
Subjects--Index Terms:

High-speed imaging

Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning.
LDR:03230nmm a2200409 4500 001 2399642
005 20240916075424.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798383194478
035 $a (MiAaPQ)AAI31330889
035 $a AAI31330889
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wei, Yumou. $3 3769614
245 1 0 $a Optical-Based Microsecond Latency MHD Mode Tracking Through Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 152 p.
500 $a Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
500 $a Advisor: Mauel, Michael E.;Navratil, Gerald A.
502 $a Thesis (Ph.D.)--Columbia University, 2024.
520 $a 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.
590 $a School code: 0054.
650 4 $a Plasma physics. $3 3175417
650 4 $a Computer engineering. $3 621879
650 4 $a Physics. $3 516296
650 4 $a Information technology. $3 532993
653 $a High-speed imaging
653 $a Convolutional neural network
653 $a CNN model
653 $a Magnetic sensor
653 $a Camera diagnostic
690 $a 0759
690 $a 0800
690 $a 0464
690 $a 0489
690 $a 0605
710 2 $a Columbia University. $b Applied Physics. $3 3187824
773 0 $t Dissertations Abstracts International $g 86-01B.
790 $a 0054
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31330889