ICALEPCS2013 - List of Keywords (scattering)

Paper	Title	Other Keywords	Page
TUPPC048	Adoption of the "PyFRID" Python Framework for Neutron Scattering Instruments	controls, framework, interface, software	677
	M. Drochner FZJ, Jülich, Germany
	M.Drochner, L.Fleischhauer-Fuss, H.Kleines, D.Korolkov, M.Wagener, S.v.Waasen Adoption of the "PyFRID" Python Framework for Neutron Scattering Instruments To unify the user interfaces of the JCNS (Jülich Centre for Neutron Science) scattering instruments, we are adapting and extending the "PyFRID" framework. "PyFRID" is a high-level Python framework for instrument control. It provides a high level of abstraction, particularly by use of aspect oriented (AOP) techniques. Users can use a builtin command language or a web interface to control and monitor motors, sensors, detectors and other instrument components. The framework has been fully adopted at two instruments, and work is in progress to use it on more.

THMIB04	Optimizing Blocker Usage on NIF Using Image Analysis and Machine Learning	site, laser, optics, target	1079
	L.M. Kegelmeyer, A.D. Conder, L.A. Lane, M.C. Nostrand, J.G. Senecal, P.K. Whitman LLNL, Livermore, California, USA
	Funding: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. #LLNL-ABS-633358 To optimize laser performance and minimize operating costs for high energy laser shots it is necessary to locally shadow, or block, flaws from laser light exposure in the beamline optics. Blockers are important for temporarily shadowing a flaw on an optic until the optic can be removed and repaired. To meet this need, a combination of image analysis and machine learning techniques have been developed to accurately define the list of locations where blockers should be applied. The image analysis methods extract and measure evidence of flaw candidates and their correlated downstream hot spots and this information is passed to machine learning algorithms which rank the probability that candidates are flaws that require blocking. Preliminary results indicate this method will increase the percentage of true positives from less than 20% to about 90%, while significantly reducing recall – the total number of candidates brought forward for review.
	Slides THMIB04 [0.243 MB]
	Poster THMIB04 [2.532 MB]

Paper

Title

Other Keywords

Page

TUPPC048

Adoption of the "PyFRID" Python Framework for Neutron Scattering Instruments

controls, framework, interface, software

677

M. Drochner
FZJ, Jülich, Germany

M.Drochner, L.Fleischhauer-Fuss, H.Kleines, D.Korolkov, M.Wagener, S.v.Waasen Adoption of the "PyFRID" Python Framework for Neutron Scattering Instruments To unify the user interfaces of the JCNS (Jülich Centre for Neutron Science) scattering instruments, we are adapting and extending the "PyFRID" framework. "PyFRID" is a high-level Python framework for instrument control. It provides a high level of abstraction, particularly by use of aspect oriented (AOP) techniques. Users can use a builtin command language or a web interface to control and monitor motors, sensors, detectors and other instrument components. The framework has been fully adopted at two instruments, and work is in progress to use it on more.

THMIB04

Optimizing Blocker Usage on NIF Using Image Analysis and Machine Learning

site, laser, optics, target

1079

L.M. Kegelmeyer, A.D. Conder, L.A. Lane, M.C. Nostrand, J.G. Senecal, P.K. Whitman
LLNL, Livermore, California, USA

Funding: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. #LLNL-ABS-633358
To optimize laser performance and minimize operating costs for high energy laser shots it is necessary to locally shadow, or block, flaws from laser light exposure in the beamline optics. Blockers are important for temporarily shadowing a flaw on an optic until the optic can be removed and repaired. To meet this need, a combination of image analysis and machine learning techniques have been developed to accurately define the list of locations where blockers should be applied. The image analysis methods extract and measure evidence of flaw candidates and their correlated downstream hot spots and this information is passed to machine learning algorithms which rank the probability that candidates are flaws that require blocking. Preliminary results indicate this method will increase the percentage of true positives from less than 20% to about 90%, while significantly reducing recall – the total number of candidates brought forward for review.

Slides THMIB04 [0.243 MB]

Poster THMIB04 [2.532 MB]