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Convolutional Neural Network Architecture Study For Aerial Visual Localization, Jedediah M. Berhold
Convolutional Neural Network Architecture Study For Aerial Visual Localization, Jedediah M. Berhold
Theses and Dissertations
In unmanned aerial navigation the ability to determine the aircraft's location is essential for safe flight. The Global Positioning System (GPS) is the default modern application used for geospatial location determination. GPS is extremely robust, very accurate, and has essentially solved aerial localization. Unfortunately, the signals from all Global Navigation Satellite Systems (GNSS) to include GPS can be jammed or spoofed. To this response it is essential to develop alternative systems that could be used to supplement navigation systems, in the event of a lost GNSS signal. Public and governmental satellites have provided large amounts of high-resolution satellite imagery. These …
Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon
Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon
Theses and Dissertations
In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …