- Mon 02 March 2015
- notebook
- Jason K. Moore
- #notebook, #aio
AIO for March 2, 2015
Last week's accomplishments
Yeadon Paper
- [5 hr, Done 4.7 hr] Finish and submit the second revision by March 1.
- Second revision is here: https://github.com/chrisdembia/python-yeadon-paper
Perturbed Data Paper
- [15 min, Done 20 min] Respond to PeerJ about my concerns with the reviewers.
Job Search
- [5 hr, Done 3.4 hr] Finish teaching statement draft and ask for reviews from Rebecca, Sarah, and Cass.
- [1 hr, Done 1.5 hr] Request reference letters for lecturer position.
- [1 hr, Done 1.5 hr] Second interview with robot/machine learning company.
- [0.5 hr, Done] Respond to Sherm and setup a day to do the coding test for the Opensim job.
- [2.5 hr, Done 3 hr] Meet with Prof. Kong at UCD about post doc.
- [9.7 hr] Interviewed with sports biomechanics startup.
- [30 min] Turned down the opensim job.
Last week's issues
Perturbed Data Paper
- [5 hr] Fix as many issues as possible in the alloted time. (20 days
left till due date).
- See below. Didn't make time.
Quiet Standing Identification Paper
- [20 hr, Failed] Finish first draft by February 28.
- I went for some spontaneuous itnerviews in the bay area instead of meeting this deadline. I did get some work done but managed to booger the direct collocation solutions by trying a "better" input perturbation. I think this threw off the scaling. I'm going to generalize the scaling so hopefully it works. I was trying to have a sum of sines with a human scale bandwidth with decreasing amplitude vs frequency for a realistic "random" input. Got some tips from my old PI on this.
- I also updated some of the paper, but the results are still lackluster.
Job Search
- [2 hr] Make a new CV version that focuses on teaching and service.
This week's objectives
Perturbed Data Paper
- [5 hr] Fix as many issues as possible in the alloted time. (20 days left till due date).
Quiet Standing Identification Paper
- [20 hr] Finish first draft by end of week.
- Introduce optimal random input.
- Generalize scaling.
- Create plot to show the optimal step size in direct collocation.
- Rerun the 10,000 identifications with the optimal step size.