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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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- Creator:
- Limon, Garrett C.
- Description:
- The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
- 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 [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
- Creator:
- Habbal, Osama, Orabi, Mohamad , Mohanty, Pravansu, and Pannier, Christopher
- Description:
- This research introduces a novel method to produce biomimetic shapes using low cost soluble 3D printed molds. Mesenchymal stem cells in alginate matrix cell viability was studied. The alginate stem cell structure is made in a construct that is 21 mm wide, 6 mm high, with an arbor diameter of 1 mm (see Combined_Test_Channels.stl). The cells showed 64% survivability at 7 days in the 3D constructs.
- Keyword:
- 3D Printing, Additive Manufacturing, and 3D bio scaffold
- Discipline:
- Engineering