University of South Florida

Usens, Inc.

Yuan Wang

Yu Zhang

Yue Fei

Gengyu Ma

Numerical weather prediction data during a time period of interest for a multi-airport system is used to generate a three-dimensional (3-D) tensor data for the time period of interest. The 3-D tensor data is then used to predict hourly runway configurations for each of a plurality of airports of the multi-airport system during the time period of interest using the 3-D tensor data as input to a plurality of runway configuration convolutional neural networks (CNN) branches and to predict an AAR for each of the plurality of airports of the multi-airport system during the time period of interest using the 3-D tensor data as input to an airport acceptance rate (AAR) CNN branch. The predicted hourly runway configuration for each of the plurality of airports and the predicted AAR for each of the plurality of airports are then used to manage flights within the multi-airport system.

System and method for the prediction of runway configuration and airport acceptance rate (AAR) for multi-airport system

A system for computer vision is disclosed. The system may comprise a processor and a non-transitory computer-readable storage medium coupled to the processor. The non-transitory computer-readable storage medium may store instructions that, when executed by the processor, cause the system to perform a method. The method may comprise obtaining a first and a second images of at least a portion of an object, extracting a first and a second 2D contours of the portion of the object respectively from the first and second images, matching one or more first points on the first 2D contour with one or more second points on the second 2D contour to obtain a plurality of matched contour points and a plurality of mismatched contour points, and reconstructing a shape of the portion of the object based at least in part on at least a portion of the matched points and at least a portion of the mismatched contour points.

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San Jose, CA, United States of America

Yuan Wang