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- Creator:
- Wu, Ziyou and Revzen, Shai
- Description:
- The data in this repository is a nearly unique dataset at the time of its making -- precise measurements of all contact forces of a 6-legged robot during multi-legged slipping motions and regular walking. These data were collected to establish the validity of the observation presented in this article: Zhao et al. Walking is like slithering: A unifying, data-driven view of locomotion. (2022) PNAS 119(37): e113222119. DOI: https://doi.org/10.1073/pnas.2113222119
- Keyword:
- robot, locomotion, and multilegged
- Citation to related publication:
- Science Robotics paper being submitted
- Discipline:
- Engineering
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- Creator:
- Skinner, Katherine A. , Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection released in support of an IROS 2023 workshop publication, with a supporting website ( https://sites.google.com/umich.edu/novelsensors2023). To enable new research in the area of novel sensors for autonomous vehicles, these datasets are designed for the task of place recognition with novel sensors. To our knowledge, this new dataset is the first to include stereo thermal cameras together with stereo event cameras and stereo monochrome cameras, which perform better in low-light than RGB cameras., The dataset collection platform is a Ford Fusion vehicle with roof-mounted sensing suite, which consists of forward-facing stereo uncooled thermal cameras (FLIR Boson 640+ ADK), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) aligned with ground truth position from a high precision navigation system. Sequences include ~10 km routes, which may be driven repeatedly under varying lighting conditions and feature instances of direct sunlight and low-light that challenge conventional cameras., and A software toolkit to facilitate efficient use of the dataset including dataset download, application of calibration parameters, and evaluation of place recognition results based on standard metrics (e.g., maximum recall at 100% precision). These software tools for converting, managing, and viewing datafiles can be found at the associated GitHub repository ( https://github.com/umautobots/nsavp_tools).
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023 and https://github.com/umautobots/nsavp_tools
- Discipline:
- Engineering
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- Creator:
- Skinner, Katherine A. , Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection released in support of an IROS 2023 workshop publication, with a supporting website ( https://sites.google.com/umich.edu/novelsensors2023). To enable new research in the area of novel sensors for autonomous vehicles, these datasets are designed for the task of place recognition with novel sensors. To our knowledge, this new dataset is the first to include stereo thermal cameras together with stereo event cameras and stereo monochrome cameras, which perform better in low-light than RGB cameras., The dataset collection platform is a Ford Fusion vehicle with roof-mounted sensing suite, which consists of forward-facing stereo uncooled thermal cameras (FLIR Boson 640+ ADK), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) aligned with ground truth position from a high precision navigation system. Sequences include ~10 km routes, which may be driven repeatedly under varying lighting conditions and feature instances of direct sunlight and low-light that challenge conventional cameras., and A software toolkit to facilitate efficient use of the dataset including dataset download, application of calibration parameters, and evaluation of place recognition results based on standard metrics (e.g., maximum recall at 100% precision). These software tools for converting, managing, and viewing datafiles can be found at the associated GitHub repository ( https://github.com/umautobots/nsavp_tools).
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://sites.google.com/umich.edu/novelsensors2023 and https://github.com/umautobots/nsavp_tools
- Discipline:
- Engineering
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- Creator:
- Hepner, Shadrach, T
- Description:
- This data provided evidence of the presence of a lower hybrid drift instability in a magnetic nozzle. It was used in DOI: 10.1063/5.0012668 to estimate the effective electron collision frequency that it induced in the context of cross-field electron transport. It is also used to determine the effective reduction in heat flux resulting from propagation along magnetic field lines in an upcoming work.
- Keyword:
- Magnetic nozzle, heat flux, plasma instabilities
- Citation to related publication:
- Hepner, S., Jorns, B. (2020). Wave-driven non-classical electron transport in a low temperature magnetically expanding plasma. Appl. Phys. Lett, 116(263502). https://doi.org/10.1063/5.0012668
- Discipline:
- Engineering
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- Creator:
- Lin, Austin J, Lei, Shunbo, Keskar, Aditya, Hiskens, Ian A, Johnson, Jeremiah X , Mathieu, Johanna L, Kennedy, Tim, DeMink, Scott, Morgan, Kevin, Flynn, Connor, Giessner, Paul, Anderson, David, Dongmo, Jordan, Afshari, Sina, Li, Han, and Ceilsinki, Andrew
- Description:
- This is a subset of the SHIFDR dataset collection containing data from 14 buildings in Southeast Michigan. The full dataset collection can be found at https://deepblue.lib.umich.edu/data/collections/vh53ww273?locale=en and Organization: We include a subfolder for each building, identified by name. All buildings have been renamed after lakes to protect the identity of the building. Within each building subfolder, there is fan power (i.e. current measurements from which fan power can be computed), building automation system (BAS), whole building electrical load (WBEL), and voltage data collected over the course of our experimentation from 2017 to 2021. All experiments were conducted in the summer months and a full schedule of Demand Response (DR) events is included along with each building in the ‘Event_Schedule.csv’ file. The building information file contains general information about the buildings, pertinent to the experiments we conducted. There is also a folder labeled ‘2021 Preprocessed data’ which contains combined BAS and fan power data from the summer of 2021. This data has been lightly processed to calculate fan power from current measurements and interpolate BAS data to 1 minute intervals. These act as an easy-to-use starting point for data analysis.
- Citation to related publication:
- A.J. Lin, S. Lei, A. Keskar, I.A. Hiskens, J.X. Johnson, and J.L. Mathieu. “The Sub-metered HVAC Implemented For Demand Response (SHIFDR) Dataset,” Submitted, 2023.
- Discipline:
- Engineering
-
- Creator:
- Gill, Tate M.
- Description:
- Data included in raw format in addition to the MATLAB scripts used for processing into final results. If there are issues or confusion regarding this data or the codes, feel free to contact me at tategill@umich.edu.
- Keyword:
- Electric Propulsion
- Discipline:
- Engineering
-
- Creator:
- Jones, Kaylin and Cotel, Aline J
- Description:
- To enhance environmental turbulence measurements, we have designed and constructed a novel Particle Image Velocimetry (PIV) instrument intended for field use. The data contained here was used for either validation of the instrument, or was produced by the instrument in proof-of-concept field testing.
- Keyword:
- particle image velocimetry, environmental turbulence, and field instrumentation
- Citation to related publication:
- Jones, K., and Cotel, A.J. 2023. Low-cost field particle image velocimetry for quantifying environmental turbulence. Journal of Ecohydraulics.
- Discipline:
- Engineering
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- Creator:
- Kim, Wonhui, Ramanagopal, Manikandasriram Srinivasan, Barto, Charles , Yu, Ming-Yuan, Rosaen, Karl , Goumas, Nick , Vasudevan, Ram, and Johnson-Roberson, Matthew
- Description:
- PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.
- Citation to related publication:
- https://doi.org/10.48550/arXiv.1809.03605, https://github.com/umautobots/pedx, and http://pedx.io/
- Discipline:
- Engineering
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- Creator:
- Limon, Garrett C.
- Description:
- The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
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Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works