Neural Network Classification Techniques for UXO Applications

Autor:	John E. Foley, Matthew Gifford
Rok vydání:	1996
Předmět:	Unexploded ordnance Engineering Artificial neural network Operations research business.industry Field data Neural network architecture Data mining Survey instrument business Neural network classification computer.software_genre computer
Zdroj:	Symposium on the Application of Geophysics to Engineering and Environmental Problems 1996.
DOI:	10.4133/1.2922335
Popis:	A neural network was used to predict the mass of shallow subsurface conductive objects. The field data was from an unexploded ordnance (UXO) survey conducted at Camp Simms, Washington DC. The survey instrument was the Geonics EM-6 1 pulsed induction sensor. The purpose of the study was to develop a neural network architecture that could be fielded at numerous sites under the US Army Corps of Engineer’s Ordnance and Explosives Knowledgebase (OE-KB) program. The neural network was successful in predicting masses and depths. This paper presents the results of a single training session and is also intended as a short tutorial in how to prepare and present geophysical field data for analysis by a neural network.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41d64b18cb6a3bd133ac815dd855f881 https://doi.org/10.4133/1.2922335 Zobrazit plný text záznamu