Difference between revisions of "SRGS 2022"

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</table>
 
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== PHASM: neural net models of PDE solvers==  
 
== PHASM: neural net models of PDE solvers==  
  
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* Colin Wolfe
 
* Colin Wolfe
  
Useful links:
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=== Useful links: ===
 
* [[Media:Phasm Intro Slides.pdf|Phasm Intro Slides.pdf]]
 
* [[Media:Phasm Intro Slides.pdf|Phasm Intro Slides.pdf]]
 
* [[SciML curriculum]]
 
* [[SciML curriculum]]
 
* PHASM repository: [https://github.com/nathanwbrei/phasm]
 
* PHASM repository: [https://github.com/nathanwbrei/phasm]
  
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== AI Feature Recognition: Extract Spectrometer Angle from Image ==
 
== AI Feature Recognition: Extract Spectrometer Angle from Image ==
  

Revision as of 19:24, 28 June 2022

General Info

Presentation Schedule

     June 27 - orientation
     June 28 - Will S.
     June 29 - Dhruv B.
     June 30 - Anna R.
     July  1 - Hari G.
     July 4 Independence Day 
     July 6 - Will S.
     July 7 - Colin W.
     July 8 - Anna R.
     July 5 - Dhruv B.
     July 11 - Hari G.
     July 12 - Will S.
     July 13 - Colin W.
     July 14 - Anna R.
     July 15 - Dhruv B.
     July 18 - Hari G.
     July 19 - Will S.
     July 20 - Colin W.
     July 21 - Anna R.
     July 22 - Dhruv B.


PHASM: neural net models of PDE solvers

Students:

  • Dhruv Bejugam
  • Hari Gopal
  • Colin Wolfe

Useful links:



AI Feature Recognition: Extract Spectrometer Angle from Image

Students:

  • Anna Rosner
  • William Savage

Useful links/info:

Initial thoughts from Brad

Brad's initial thoughts on approaching the problem (Click "Expand" to the right for details -->):

   I had been imagining splitting the photos into two regions: one with
   the digits, and a second with the vernier scale. Each region would be
   evaluated/interpreted separately with some 'optimized' algorithms.
   
   'Real' errors/discrepancies would be best indicated by a scanning for a
   mismatch between MYA and the analysis database record and/or the value
   flagged in the logbook which has generally been vetted and updated by a
   human. The simplest way to test 'bad' angles would be just to
   (randomly) shift the truth angle by a small amount -- that would be
   indistinguishable from an observed drift in the EPICS encoder system.
   
   I (or the students) can also look for angle shifts in the 'real' data,
   but that will take some poking around. It should be indicated by a
   sharp (small) jump in the MYA value as an offset is changed to bring
   the EPICS value in agreement with the camera readback.
   
   One other dataset that I could obtain is a movie of the angle changing
   over a range (the movie is just a compilation of frame grabs). The
   individual frames could be pulled out of the mp4 and evaluated
   individually over a continuously varying range of angles.