Difference between revisions of "SRGS 2022"
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* [[Media:Mini_research_projects_v2.pdf | Potential Projects Summaries]] | * [[Media:Mini_research_projects_v2.pdf | Potential Projects Summaries]] | ||
* [https://jeffersonlab.sharepoint.com/:f:/r/sites/SciEdStudents/Shared%20Documents/2022_Summer/David%20Lawrence,%20Nathan%20Brei,%20Carl%20Timmer?csf=1&web=1&e=btBNDn Sharepoint Link] | * [https://jeffersonlab.sharepoint.com/:f:/r/sites/SciEdStudents/Shared%20Documents/2022_Summer/David%20Lawrence,%20Nathan%20Brei,%20Carl%20Timmer?csf=1&web=1&e=btBNDn Sharepoint Link] | ||
| + | |||
| + | == PHASM: neural net models of PDE solvers== | ||
| + | |||
| + | Students: | ||
| + | * Dhruv Bejugam | ||
| + | * Hari Gopal | ||
| + | * Colin Wolfe | ||
| + | |||
| + | Useful links: | ||
| + | * Project slides: | ||
| + | * Background reading material | ||
| + | * PHASM repository: [[https://github.com/nathanwbrei/phasm]] | ||
== AI Feature Recognition: Extract Spectrometer Angle from Image == | == AI Feature Recognition: Extract Spectrometer Angle from Image == | ||
Revision as of 19:10, 27 June 2022
General Info
PHASM: neural net models of PDE solvers
Students:
- Dhruv Bejugam
- Hari Gopal
- Colin Wolfe
Useful links:
- Project slides:
- Background reading material
- PHASM repository: [[1]]
AI Feature Recognition: Extract Spectrometer Angle from Image
Students:
- Anna Rosner
- William Savage
Useful links/info:
- angle-cam-image-recognition.pdf
- Location of example images: /work/hallc/shms/spring17_angle_snaps/
- Time the image was acquired is embedded in the image file
- The numbers in the snapshot filenames are the run numbers
- 4,265 images ; ~92kB/file ; 391MB total
- The value of the encoders are stored in the MYA EPICS archive
- PV names are:
- ecSHMS_Angle
- ecHMS_Angle
- PV names are:
- Example logbook entry
Initial thoughts from Brad
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.