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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
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- Creator:
- Sugrue, Dennis P.
- Description:
- This data was collected and processed as part of ongoing research to characterize waterway infrastructure performance in the Great Lakes. These dataset enable researchers to evaluate both travel time and vessel carrying capacity in the waterway., I assembled AIS data from the MarineCadastre website for UTM Zones 15-18 for the years 2015-2017 available in csv format. I combined files for Navigation Seasons, defined as March to January and clipped data for a set of predefined features using a python code (AIS Data Processor.ipynb). The code writes the appended and clipped files to csv for a single Navigation Year. The written files are submitted here: Trimmed_NY2015_new.csv (n=13,228,824); Trimmed_NY2016_new.csv (n=18,782,779); Trimmed_NY2017_new.csv (n=16,816,603), Data fusion of AIS and LPMS used the following algorithm for a subset of 30 vessels on the waterway. Let A be the original AIS data and let B be the subset of records for vessel i within geographic feature j. The script for this analysis is attached (Maritime Data Fusion.ipynb), For Connecting Channels and select segments of the Great Lakes: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic feature, Gj. Let B_ij⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as vessel i arrival to feature j, b_ijt | 4. IF feature j is a harbor or lock, select tmax for each unique date or any consecutive dates, record as departure from feature j, b_ijt | 5. Calculate time elapsed between features for each vessel, For vessel passage through the Soo Locks: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic boundaries (46.5<Lat<46.6, -84.4<Lon<-84.3). Let C_(i,lock)⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as arrival to Soo Locks | 4. Select tmax for each unique date or any consecutive dates, record as departure to Soo Locks | 5. Calculate time delta between arrival and departure times, and The merged dataset is included here along with the raw LPMS data: Merged_Data_new.csv (n=42,021), LPMS obscured.csv (n=55,342). VesselNames have been obscured in these datasets to protect proprietary information for shipping companies.
- Keyword:
- Maritime Transportation Efficiency, Data Fusion, Waterway Performance
- Citation to related publication:
- Sugrue, D., Adriaens, P. (in review) Multi-dimensional Data Fusion to Evaluate Waterway Performance: Maritime Transport Efficiency of Iron Ore on the Great Lakes. Water Resources Research.
- Discipline:
- Engineering
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- Creator:
- Agrawal, Mayank and Glotzer, Sharon C
- Description:
- Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. The research dataset includes simulation, visualization, and analysis scripts and results generated for the 2D chain loops of self-propelling particles. File Description:, -- arrows_folding - Contains the data for the folded chain loop shapes resembling an arrowhead., -- bending_vs_variation - Contains the data to study the stability of a particular shape in simulations as one of the segments of the shape bends and/or the distribution of propulsion on it varies., -- curved_triangle - Contains the data to study motion and bending of a triangle shape made using chain loop., -- example_shapes - Contains data for various examples of shapes that can be generated by designing the chain loops., -- nskT_vs_fakT - Contains the data for a specific shape to study the effect of scaling up the number of particles (governed by ns) and the propulsion (governed by fa) in its chain., -- stability - Contains the data and theoretical model (stability.py) to study the stability of the six different shapes., -- tuning_design_forM - Contains the data for sequential tuning the design parameters to fold the shape "M" as described in the corresponding publication., and -- two_neighboring_cds_segments_ - Contains the data to study a system of two neighboring chain segments with respect to different parameters discussed in the publication.
- Keyword:
- active particles, colloidal robotics, design, kilobots, and morphological control
- Citation to related publication:
- Agrawal, M, Glotzer SC. (2020). Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors. PNAS. www.pnas.org/cgi/doi/10.1073/pnas.1922635117
- Discipline:
- Engineering
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- Creator:
- Moniri, Saman, Xiao, Xianghui, and Shahani, Ashwin J.
- Description:
- The data is comprised of 22 directories, each housing a .hdf file of the X-ray projections recorded during solidification of Al-Si-Cu-Sr. The flat and dark projections are also included as two separate .hdf files (total file count: 24). The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.
- Keyword:
- Crystallization, growth modifiers, silicon, in situ, X-ray tomography
- Citation to related publication:
- Wang, Y., Gao, J., Ren, Y., De Andrade, V., & Shahani, A. J. (2020). Formation of a Three-Phase Spiral Structure Due to Competitive Growth of a Peritectic Phase with a Metastable Eutectic. JOM, 72(8), 2965–2973. https://doi.org/10.1007/s11837-020-04237-x
- Discipline:
- Engineering
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- Creator:
- Whitaker, Steven T., Nataraj, Gopal, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A.
- Description:
- File: P,jf06Sep2019,mese.7 The multi-echo spin echo (MESE) data was acquired using a 3D acquisition with an initial 90 degree excitation pulse followed by 32 refocusing (180 degree) pulses, resulting in 32 echoes with echo spacing of 10 ms. The repetition time of the sequence was 1200 ms. Each refocusing pulse was flanked by crusher gradients to impart 14 cycles of phase across the imaging volume. The initial excitation pulse had time-bandwidth product of 6, duration of 3 ms, and slab thickness of 0.9 cm, and each refocusing pulse had time-bandwidth product of 2, duration of 2 ms, and slab thickness of 2.1 cm. The scan took 36 min 11 s and covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: P,jf06Sep2019,b1.7 The Bloch-Siegert (BS) scans were acquired using a 3D acquisition. The excitation pulse of these scans had time-bandwidth product of 8 and duration of 1 ms. The pair of scans used +/-4 kHz off-resonant Fermi pulses between excitation and readout. The BS scans took 2 min 40 s in total and covered a FOV of 22 x 22 x 0.99 cm^3 with matrix size 200 x 50 x 9., File: P,jf06Sep2019,mwf.7 The small-tip fast recovery (STFR) scans were acquired using a 3D acquisition. The first two and last two scans were pairs of spoiled gradient-recalled echo (SPGR) scans with echo time difference of 2.3 ms. (In the related paper, only the first set was used, i.e., only 11 of the 13 scans were used.) The remaining scans used scan parameters that were optimized to minimize the Cramer-Rao Lower Bound (CRLB) of estimates of myelin water fraction (MWF). The RF pulses had time-bandwidth product of 8 and duration of 1 ms. Each pair of SPGR scans took 58 s and the nine STFR scans took 3 min 36 s for a total scan time of 5 min 32 s (4 min 34 s if one pair of SPGR scans is ignored). The scans covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: meseslice5.mat Contains the 32 echoes of the MESE image data for the middle slice of the imaging volume. Saved using Mathworks MATLAB R2019a., File: b1slice5.mat Contains the transmit field inhomogeneity map for the middle slice of the imaging volume., File: recon.jld Key "img" contains the 11 STFR images for the middle slice of the imaging volume. Key "b0map" contains a field map estimated from the two SPGR scans. Key "mask" contains a mask of the voxels for which to estimate MWF. Key "T1img" contains a T1-weighted image for anatomical reference., File: headmask.mat Contains a mask for visualizing just the brain (ignores the skull) for the middle slice of the imaging volume., File: rois.mat Contains masks for various regions of interest (ROIs), used for computing statistics. Keys "mtopleft", "mtopright", "mbottomleft", and "mbottomright" refer to the corresponding locations on the anatomical reference image (see related paper). Key "mic" refers to the internal capsules, and key "mgm" refers to a gray matter ROI., The raw data files (P-files) can be read into the Julia programming language using the Julia version of the Michigan Image Reconstruction Toolbox ( https://github.com/JeffFessler/MIRT.jl) or into MATLAB using TOPPE ( https://github.com/toppeMRI/toppe). The reconstructed slices used in the related paper are provided for convenience, and are stored in .mat files that can be loaded into Julia (using the package MAT.jl) or MATLAB, and a .jld file that can be loaded into Julia (using the package JLD.jl). The Julia code for processing the data to create MWF maps is hosted publicly on GitHub at https://github.com/StevenWhitaker/STFR-MWF., and Files: toppe-master.zip and MIRT.jl-master.zip are archived versions of the TOPPE and Michigan Image Reconstruction Toolbox code sets from GitHub as of 2/28/2020.
- Keyword:
- myelin, machine learning, kernel learning, magnetic resonance imaging, and scan design
- Citation to related publication:
- Whitaker, S. T., Nataraj, G., Nielsen, J.-F., & Fessler, J. A. (2020). Myelin water fraction estimation using small-tip fast recovery MRI. Magnetic Resonance in Medicine, 84(4), 1977–1990. https://doi.org/10.1002/mrm.28259
- Discipline:
- Health Sciences and Engineering
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- Creator:
- Ahluwalia, Vinayak S., Steimle, Lauren N., and Denton, Brian T.
- Description:
- This repository includes test instances of infinite-horizon Markov decision processes with multiple models of parameters (i.e., "Multi-model Markov decision processes"). We generated each test instance in the dataset using a Python script. The test instances can be read in using the provided C++ and Python script. See the README for details.
- Keyword:
- Markov decision processes, mixed-integer programming, stochastic programming, and dynamic programming
- Citation to related publication:
- Ahluwalia, Steimle, and Denton. "Policy-based branch-and-bound for infinite-horizon Multi-model Markov decision processes". 2020.
- Discipline:
- Engineering
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- Creator:
- Holmes, Patrick
- Description:
- This work was collected to evaluate Stability Basins for characterizing the limits of human stability during Sit-to-Stand. and MATLAB code was used to process the data into individual trials. Trials are labeled by Sit-to-Stand type (Natural, Momentum-Transfer, or Quasi-Static) and experimental condition. MATLAB code for analyzing the data and computing Stability Basins is provided. A GUI is provided to animate a subject's movement and display projections of the Stability Basins in the horizontal and vertical planes.
- Keyword:
- biomechanics, mathematical model, locomotion, fall risk, reachability, and feedback control
- Citation to related publication:
- https://arxiv.org/abs/1908.01876 and Holmes, P. D., Danforth, S. M., Fu, X.-Y., Moore, T. Y., & Vasudevan, R. (n.d.). Characterizing the limits of human stability during motion: Perturbative experiment validates a model-based approach for the Sit-to-Stand task. Royal Society Open Science, 7(1), 191410. https://doi.org/10.1098/rsos.191410
- Discipline:
- Engineering
-
- Creator:
- Chen, Yang and Manchester, Ward IV
- Description:
- GOES_flare_list: contains a list of more than 10,000 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time, GOES_B_flare_list: contains time series data of SDO/HMI SHARP parameters for B class solar flares , GOES_MX_flare_list: contains time series data of SDO/HMI SHARP parameters for M and X class solar flares, SHARP_B_flare_data_300.hdf5 and SHARP_MX_flare_data_300.hdf5 files contain time series more than 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model., and B_HARPs_CNNencoded_part_xxx.hdf5 and M_X HARPs_CNNencoded_part_xxx.hdf5 include neural network encoded features derived from vector magnetogram images derived from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI). These data files typically contains one or two sequences of magnetograms covering an active region for a period of 24h with a 1 hour cadence. We encode each magnetogram with frames of a fixed size of 8x16 with 512 channels.
- Keyword:
- machine learning, data science, and solar flare prediction
- Citation to related publication:
- Chen, Y., Manchester, W., Hero, A., Toth, G., DuFumier, B. Zhou, T., Wang, X., Zhu, H., Sun, Zeyu, Gombosi, T., Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters, Space Weather, 17, 1404–1426. https://doi.org/10.1029/2019SW002214 and Jiao, Z., Chen, Y., Manchester, W. (2020). Data for Solar Flare Intensity Prediction with Machine Learning Models [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/b07j-bj08
- Discipline:
- Engineering and Science
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- Creator:
- Bowen Li, Yiling Zhang, Siqian Shen, and Johanna Mathieu
- Description:
- The project outputs summarize all the publications, talks, and codes we accomplished under this NSF funding. In the project, we develop methodologies to manage uncertainty in future electric power systems and quantify how uncertainty affects power system sustainability. and Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149653
- Keyword:
- chance constraint, distributionally robust optimization, optimal power flow, demand response, and unimodality
- Citation to related publication:
- B. Li and J. L. Mathieu, "Analytical reformulation of chance-constrained optimal power flow with uncertain load control," 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 1-6. https://doi.org/10.1109/PTC.2015.7232803, B. Li, J. L. Mathieu and R. Jiang, "Distributionally Robust Chance Constrained Optimal Power Flow Assuming Log-Concave Distributions," 2018 Power Systems Computation Conference (PSCC), Dublin, 2018, pp. 1-7. https://doi.org/10.23919/PSCC.2018.8442927, B. Li, M. Vrakopoulou and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1618-1625, March 2019. https://doi.org/10.1109/TSG.2017.2773603, B. Li, R. Jiang and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow Assuming Unimodal Distributions With Misspecified Modes," in IEEE Transactions on Control of Network Systems, vol. 6, no. 3, pp. 1223-1234, Sept. 2019. https://doi.org/10.1109/TCNS.2019.2930872, B. Li, R. Jiang and J. L. Mathieu, "Distributionally robust risk-constrained optimal power flow using moment and unimodality information," 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 2425-2430. https://doi.org/10.1109/CDC.2016.7798625, B. Li, S. D. Maroukis, Y. Lin and J. L. Mathieu, "Impact of uncertainty from load-based reserves and renewables on dispatch costs and emissions," 2016 North American Power Symposium (NAPS), Denver, CO, 2016, pp. 1-6. https://doi.org/10.1109/NAPS.2016.7747830, G. Martínez, J. Liu, B. Li, J. L. Mathieu and C. L. Anderson, "Enabling renewable resource integration: The balance between robustness and flexibility," 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, 2015, pp. 195-202. https://doi.org/10.1109/ALLERTON.2015.7447004, J. Liu, M. G. Martinez, B. Li, J. Mathieu and C. L. Anderson, "A Comparison of Robust and Probabilistic Reliability for Systems with Renewables and Responsive Demand," 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, 2016, pp. 2373-2380. https://doi.org/10.1109/HICSS.2016.297, Li, B., Jiang, R. & Mathieu, J.L. "Ambiguous risk constraints with moment and unimodality information." Math. Program. 173, 151–192 (2019). https://doi.org/10.1007/s10107-017-1212-x, M. Vrakopoulou, B. Li and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part I: Formulation and Scenario-Based Analysis," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1608-1617, March 2019. https://doi.org/10.1109/TSG.2017.2773627, Y. Zhang, S. Shen and J. L. Mathieu, "Data-driven optimization approaches for optimal power flow with uncertain reserves from load control," 2015 American Control Conference (ACC), Chicago, IL, 2015, pp. 3013-3018. https://doi.org/10.1109/ACC.2015.7171795, Y. Zhang, S. Shen and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads," in IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 1378-1388, March , and Y. Zhang, S. Shen, B. Li and J. L. Mathieu, "Two-stage distributionally robust optimal power flow with flexible loads," 2017 IEEE Manchester PowerTech, Manchester, 2017, pp. 1-6. https://doi.org/10.1109/PTC.2017.7981202
- Discipline:
- Engineering
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- Creator:
- Siegel, Jason B. , Stefanopoulou, Anna G., and McKahn, Denise (McKay)
- Description:
- This section contains work related to the modeling of voltage degradation in a 24 cell PEM fuel cell stack with 300 cm2 cross-sectional area. The experimental hardware used to validate the model consists of a computer controlled system that coordinates air, hydrogen, cooling, and electrical subsystems to operate the stack. Dry hydrogen is pressure regulated for full utilization in the dead-ended anode. Using a solenoid valve, the anode is periodically purged to recover the gradual degradation in voltage. A membrane based humidifier controls the vapor content of the cathode gas stream while a mass flow controller is used to regulate the flow to the desired stoichiometry.
- Citation to related publication:
- Denise A. McKay, Jason B. Siegel, William Ott, and Anna G. Stefanopoulou. Parameterization and prediction of temporal fuel cell voltage behavior during flooding and drying conditions. Journal of Power Sources, 178(1):207 - 222, 2008. https://doi.org/10.1016/j.jpowsour.2007.12.031
- Discipline:
- Engineering