Optimizing neural networks for enhancing air traffic security
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | en_US |
| Published: |
Massachusetts Institute of Technology
2005
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/28382 |
| _version_ | 1826193045920415744 |
|---|---|
| author | Cooney, Geoffrey T. (Geoffrey Thomas), 1980- |
| author2 | Amar Gupta. |
| author_facet | Amar Gupta. Cooney, Geoffrey T. (Geoffrey Thomas), 1980- |
| author_sort | Cooney, Geoffrey T. (Geoffrey Thomas), 1980- |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T09:32:56Z |
| format | Thesis |
| id | mit-1721.1/28382 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:32:56Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/283822019-04-11T13:55:56Z Optimizing neural networks for enhancing air traffic security Building an optimized neural network for enhancing air safety Cooney, Geoffrey T. (Geoffrey Thomas), 1980- Amar Gupta. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. Includes bibliographical references (leaves 81-83). This thesis contains the process and results related to optimizing a neural network to predict future positions of airplanes in the vicinity of airports. These predicted positions are then used to calculate future separation distances between pairs of airplanes. The predicted values of the separation distance are used to ensure adequate distances between adjacent aircrafts in the air and, if necessary, to create early warning alarms to alert air traffic control tower personnel about planes that may pass too near each other in the immediate future. The thesis covers three areas of work on this topic. The first section involves optimizing a neural network for Chicago O'Hare Airport. The second is related to gathering data on the performance of this network in different scenarios. These data can be used to determine if the different days/runways have different characteristics. The final phase of this document describes how to generalize the process used to build an optimized neural network for Chicago O'Hare airport in order to provide the capability to easily recreate the process for another airport. by Geoffrey T. Cooney. M.Eng. 2005-09-26T20:09:07Z 2005-09-26T20:09:07Z 2004 2004 Thesis http://hdl.handle.net/1721.1/28382 56960613 en_US M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 163 leaves 7390393 bytes 7412147 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Cooney, Geoffrey T. (Geoffrey Thomas), 1980- Optimizing neural networks for enhancing air traffic security |
| title | Optimizing neural networks for enhancing air traffic security |
| title_full | Optimizing neural networks for enhancing air traffic security |
| title_fullStr | Optimizing neural networks for enhancing air traffic security |
| title_full_unstemmed | Optimizing neural networks for enhancing air traffic security |
| title_short | Optimizing neural networks for enhancing air traffic security |
| title_sort | optimizing neural networks for enhancing air traffic security |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/28382 |
| work_keys_str_mv | AT cooneygeoffreytgeoffreythomas1980 optimizingneuralnetworksforenhancingairtrafficsecurity AT cooneygeoffreytgeoffreythomas1980 buildinganoptimizedneuralnetworkforenhancingairsafety |