Mondal, Shanka Subhra; Webb, Taylor; Cohen, Jonathan
Abstract:
A dataset of Raven’s Progressive Matrices (RPM)-like problems using realistically rendered
3D shapes, based on source code from CLEVR (a popular visual-question-answering dataset) (Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901-2910)).
The dataset is a compilation of real time ground observations of criteria pollutants monitored at the Central Pollution Control Board (CPCB) continuous stations in India, from 2015-2019. Pollutants included are PM2.5, PM10, SO2, NO2 and O3 and are archived at every hour for all stations across India.
Yang, Yuan; Pan, Ming; Beck, Hylke; Fisher, Colby; Beighley, R. Edward; Kao, Shih-Chieh; Hong, Yang; Wood, Eric
Abstract:
Conventional basin-by-basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present-day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid-cell using machine learning-based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid-cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small-to-medium-sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small-to-medium-sized catchments not used to derive the streamflow characteristics) as well as five large basins. Comparisons indicated marked improvements in bias and Nash-Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof-of-concept for a new promising approach for fine-scale hydrologic model calibrations.
Hvasta, M. G.; Dudt, D.; Fisher, A. E.; Kolemen, E.
Abstract:
A 'weighted magnetic bearing' has been developed to improve the performance of
rotating Lorentz-force flowmeters (RLFFs). Experiments have shown that the new bearing
reduces frictional losses within a double-sided, disc-style RLFF to negligible levels.
Operating such an RLFF under 'frictionless' conditions provides two major benefits.
First, the steady-state velocity of the RLFF magnets matches the average velocity of the
flowing liquid at low flow rates. This enables an RLFF to make accurate volumetric flow
measurements without any calibration or prior knowledge of the fluid properties. Second,
due to minimized frictional losses, an RLFF is able to measure low flow rates that cannot
be detected when conventional, high-friction bearings are used. This paper provides a
brief background on RLFFs, gives a detailed description of weighted magnetic bearings,
and compares experimental RLFF data to measurements taken with a commercially available
flowmeter.
