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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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On the existence of solutions to stochastic quasi-variational inequality and complementarity problems
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Exploiting negative curvature in deterministic and stochastic optimization
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Two-stage linear decision rules for multi-stage stochastic programming
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Regional complexity analysis of algorithms for nonconvex smooth optimization
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Probability maximization via Minkowski functionals: convex representations and tractable resolution
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Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs
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On the computation of equilibria in monotone and potential stochastic hierarchical games
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On the complexity of separating cutting planes for the knapsack polytope
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SILVERBACK+: scalable association mining via fast list intersection for columnar social data
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The tradeoff between noise, data rate, and power consumption of transimpedance amplifiers for optical receivers
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Time-aware deep reinforcement learning with multi-temporal abstraction
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High precision control and deep learning-based corn stand counting algorithms for agricultural robot
This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with C robot =1.02×C human -0.86 and a correlation coefficient R=0.96. The mean relative error given by the algorithm is -3.78%, and the standard deviation is 6.76%. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.
Lightweight control-flow instrumentation and postmortem analysis in support of debugging
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Overlapping domain decomposition based exponential time differencing methods for semilinear parabolic equations
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Enhanced alternating energy minimization methods for stochastic galerkin matrix equations
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