Improved Conceptual Models Methodology (ICoMM) for Validation of Non-Observable Systems

Abstract

This dissertation expands the current view of development and validation of conceptual models (CoM) of non-observable systems (NOSs) by using systems engineering (SE) and systems architecture (SA) methods during the model development process (MDP). A MDP is used to ensure that the models are validated and represent the real world as accurately as possible. There are several varieties of MDPs presented in literature, but all share the importance of the CoM. The improved conceptual model methodology (ICoMM) is developed in support of improving the structure of the CoM for both face and traces validation. The utility of ICoMM is demonstrated through the building of functional, physical, and allocated architecture products that improve the structure of the CoM for traces validation. ICoMM also incorporates a value model to ensure subject matter experts' (SMEs') values are documented early in the MDP for face validation. A well-constructed CoM supports model exploration of NOS when operational validation is not feasible. This dissertation uses a humanitarian assistance/disaster relief (HA/DR) scenario to demonstrate ICoMMs ability to ensure documentation of SMEs' values and that the structure of the COM links SMEs' values to the fundamental objective.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2015
Accession Number
AD1009290

Entities

People

  • Sang M. Sok

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Command And Control
  • Complex Systems
  • Computer Science
  • Computers
  • Disasters
  • Engineers
  • Humanitarian Assistance
  • Landing Craft
  • Lessons Learned
  • Military Operations
  • Operations Research
  • Personnel Management
  • Software Development
  • System Of Systems
  • Systems Engineering
  • United States

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Defense Technology Research and Development.