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Full-Text Articles in Physical Sciences and Mathematics
Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
SDSU Data Science Symposium
Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …
Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco
Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco
SDSU Data Science Symposium
Self-propelled sprayers are commonly used in agriculture to disperse agrichemicals. These sprayers commonly have two boom wings with dozens of nozzles that disperse the chemicals. Automatic boom height systems reduce the variability of agricultural sprayer boom height, which is important to reduce uneven spray dispersion if the boom is not at the target height.
A computational model was created to simulate the spray dispersion under the following conditions: a) one stationary nozzle based on the measured spray pattern from one nozzle, b) one stationary model due to an angled boom, c) superposition of multiple stationary nozzles due an angled boom, …