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- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
-
- Creator:
- Attari, Ali
- Description:
- Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
- Keyword:
- Digital Manometry
- Citation to related publication:
- Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
- Discipline:
- Engineering, Science, and Health Sciences
-
- Creator:
- Mathieu, Johanna L, Balzano, Laura, and Ledva, Gregory S
- Description:
- This data set contains the relevant time series for constructing and testing electricity load models within the related paper. The files within are a '.mat' file that contains the data and a 'readme.txt' file detailing the contents of the data.
- Keyword:
- Output feedback, Online learning, Machine learning, Real-time filtering, and Energy disaggregation
- Discipline:
- Engineering
-
- Creator:
- Hegedus, Alexander M
- Description:
- This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891 , The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information. , This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements: , and CASA 4.7.1 (or greater?) built on python 2.7 Example link for Red Hat 7 https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz Users may follow this guide to download and install the correct version of CASA for their system https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing CASA executables should be fairly straightforward to extract from the untarred files. gcc 4.8.5 or above (or below?) GCC installation instructions can be found here: https://gcc.gnu.org/install/ SPICE (I use cspice here) https://naif.jpl.nasa.gov/naif/toolkit_C.html As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc' '/home/alexhege/SPICE/LunarEph/moon_080317.tf' '/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf' '/home/alexhege/SPICE/LunarEph/pck00010.tpc' '/home/alexhege/SPICE/LunarEph/naif0008.tls' '/home/alexhege/SPICE/LunarEph/de430.bsp' ) All of which can be found at https://naif.jpl.nasa.gov/pub/naif/generic_kernels/ SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
- Citation to related publication:
- Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
- Discipline:
- Engineering and Science
-
- Creator:
- Wu, Ziyou, Brunton, Steven L, and Revzen, Shai
- Description:
- These codes were produced as part of the Army Research Office Multi-University Research Initiative ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" The code can be run using the runAll.sh shell script (in Linux and OS-X); code should work similarly under windows.
- Keyword:
- DMD, dimensionality reduction, dynamical systems, and nonlinear dynamics
- Discipline:
- Engineering and Science
-
- Creator:
- BIRDS Lab, U. Michigan
- Description:
- These data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion of Multipod robots with 6,8,10 and 12 legs. The data is stored in .csv.gz files, one file for each robot morphology. Details of how to run the processing code on the raw dataset to generate the processed files found here, as well as example code for loading the data found here, are in the README. This dataset is self contained and can be used on its own without running any of the provided code.
- Citation to related publication:
- Zhao, D. & Revzen, S. Multi-legged steering and slipping with low DoF hexapod robots Bioinspiration & biomimetics, 2020, 15, 045001 https://doi.org/10.1088/1748-3190/ab84c0, Zhao, D. Ph.D. Thesis "Locomotion of low-DOF multi-legged robots" University of Michigan 2021 https://deepblue.lib.umich.edu/handle/2027.42/169985, and BIRDS Lab Multipod robot motion tracking data - RAW data, doi:10.7302/m05a-0d90
- Discipline:
- Engineering and Science
-
- Creator:
- BIRDS Lab, U. Michigan
- Description:
- These data were produced in an attempt to characterize the turning and steering behaviors of 1-DoF multi-legged (hexpedal in this case) robots. Such turning behaviors require sliding contact points. The .tar file contains multiple trials in .csv.gz format, with names following an informative naming convention documented in the README. Additional metadata for the trials is given in the metadata.py file in both machine and human readable form.
- Keyword:
- robot, multilegged, and steering
- Citation to related publication:
- Dan Zhao and Shai Revzen 2020 Bioinspir. Biomim. 15 045001 https://doi.org/10.1088/1748-3190/ab84c0
- Discipline:
- Engineering
-
- Creator:
- BIRDS Lab, U. Michigan
- Description:
- These data were produced in an attempt to characterize the turning and steering behaviors of 1-DoF multi-legged (hexpedal in this case) robots. Such turning behaviors require sliding contact points. All the data is provided in a single, large .csv.gz file (416256 rows); additional details and example code in the README
- Keyword:
- robot, multilegged, and steering
- Citation to related publication:
- BIRDS Lab, U. BigAnt v6 robot motion tracking data - RAW dataset [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/024q-kk06, Revzen, S., & Guckenheimer, J. (2008). Estimating the phase of synchronized oscillators. Phys. Rev. E, 78, 051907. http://dx.doi.org/10.1103/PhysRevE.78.051907, and Dan Zhao and Shai Revzen 2020 Bioinspir. Biomim. 15 045001 https://doi.org/10.1088/1748-3190/ab84c0
- Discipline:
- Engineering
-
- Creator:
- Reed, Matthew P., Boyle, K.
- Description:
- This is the first physical anthropomorphic test device to be based both on statistical body shape models as well as 3D printing.
- Keyword:
- Child Belt Fit Manikin
- Discipline:
- Engineering
-
- Creator:
- Curlis, JD, Renney, TJ, Davis Rabosky, AR, and Moore, TY
- Description:
- Efficient comparisons of biological color patterns are critical for understanding the mechanisms by which organisms evolve in ecosystems, including sexual selection, predator-prey interactions, and thermoregulation. However, elongate or spiral-shaped organisms do not conform to the standard orientation and photographic techniques required for automated analysis. Currently, large-scale color analysis of elongate animals requires time-consuming manual landmarking, which reduces their representation in coloration research despite their ecological importance. We present Batch-Mask: an automated and customizable workflow to facilitate the analysis of large photographic data sets of non-standard biological subjects. First, we present a user guide to run an open-source region-based convolutional neural network with fine-tuned weights for identifying and isolating a biological subject from a background (masking). Then, we demonstrate how to combine masking with existing manual visual analysis tools into a single streamlined, automated workflow for comparing color patterns across images. Batch-Mask was 60x faster than manual landmarking, produced masks that correctly identified 96% of all snake pixels, and produced pattern energy results that were not significantly different from the manually landmarked data set. The fine-tuned weights for the masking neural network, user guide, and automated workflow substantially decrease the amount of time and attention required to quantitatively analyze non-standard biological subjects. By using these tools, biologists will be able to compare color, pattern, and shape differences in large data sets that include significant morphological variation in elongate body forms. This advance will be especially valuable for comparative analyses of natural history collections, and through automation can greatly expand the scale of space, time, or taxonomic breadth across which color variation can be quantitatively examined.
- Keyword:
- convolutional neural network, photography, sensory ecology, color evolution, vision, and image segmentation
- Citation to related publication:
- Curlis, Renney, Davis Rabosky, Moore (submitted) Batch-Mask: An automated Mask R-CNN workflow to isolate non-standard biological specimens for color pattern analysis.
- Discipline:
- Engineering and Science