Programmer's Guide to UnMES Construction and Spatial Application Documentation for UnMES Transition Package to NRL-SSC Final Report- Part B

Abstract

This document describes the software required to build, train and use the Underwater Munitions Expert System (UnMES), a probabilistic management tool designed to predict underwater migration and burial of abandoned munitions. UnMES is based on a Bayesian Network framework that relates the geological and hydrodynamic site characteristics to the processes governing munitions' interaction with sediments, waves and currents. Simple deterministic models of causal forces acting on the underwater munitions and the sediment responses have been developed to predict scour burial, onset of motion, and the potential for burial or re-exposure due to bedform migration [Rennie, Brandt and Ligo, 2019, hereafter RBL2019]. These models were implemented in Matlab [Mathworks, 2022] at JHU/APL. Other preliminary models for the estimation of liquefaction burial and total distance migrated are presented, despite lack of full validation. These deterministic models are used to build a data set to train the conditional probability tables forming the core of UnMES.

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Document Details

Document Type
Technical Report
Publication Date
May 31, 2022
Accession Number
AD1187739

Entities

People

  • Sarah Rennie

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Computer Program Documentation
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Sets
  • Engineering
  • Expert Systems
  • Graphical User Interface
  • Grids
  • Java Programming Language
  • Machine Learning
  • Monte Carlo Method
  • Munitions
  • Operating Systems
  • Physics Laboratories
  • Probability
  • Probability Distributions
  • Research Facilities
  • Two Dimensional
  • Unexploded Ammunition

Readers

  • Coastal Oceanography
  • Database Systems and Applications
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference