Mr. William Leach Profile

William Leach

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 March 2019 Presentation + Paper

Using convolutional neural networks to classify static x-ray imager diagnostic data at the National Ignition Facility

William Leach, James Henrikson, Robert Hatarik, Judy Liebman, Nathan Mundhenk, Nathan Palmer, Matt Rever

Proceedings Volume 10898, 108980I (2019) https://doi.org/10.1117/12.2512605

KEYWORDS: National Ignition Facility, Diagnostics, X-ray imaging, X-rays, Convolutional neural networks, Image processing, Image filtering, Image quality, Databases

Read Abstract +

Hohlraums convert the laser energy at the National Ignition Facility (NIF) into X-ray energy to compress and implode a fusion capsule, creating fusion. The Static X-ray Imager (SXI) diagnostic collects time-integrated images of hohlraum wall X-ray illumination patterns viewed through the laser entrance hole (LEH). NIF image processing algorithms calculate the size and location of the LEH opening from the SXI images. Images obtained come from different experimental categories and camera setups and occasionally do not contain applicable or usable information. Unexpected experimental noise in the data can also occur where affected images should be removed and not run through the processing algorithms. Current approaches to try and identify these types of images are done manually and on a case-by-case basis, which can be prohibitively time-consuming. In addition, the diagnostic image data can be sparse (missing segments or pieces) and may lead to false analysis results. There exists, however, an abundant variety of image examples in the NIF database. Convolutional Neural Networks (CNNs) have been shown to work well with this type of data and under these conditions. The objective of this work was to apply transfer learning and fine tune a pre-trained CNN using a relatively small-scale dataset (~1500 images) and determine which instances contained useful image data. Experimental results are presented that show that CNNs can readily identify useful image data while filtering out undesirable images. The CNN filter is currently being used in production at the NIF.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years