Paper
15 July 2002 MRMAide: a mixed resolution modeling aide
Author Affiliations +
Abstract
The Mixed Resolution Modeling Aide (MRMAide) technology is an effort to semi-automate the implementation of Mixed Resolution Modeling (MRM). MRMAide suggests ways of resolving differences in fidelity and resolution across diverse modeling paradigms. The goal of MRMAide is to provide a technology that will allow developers to incorporate model components into scenarios other than those for which they were designed. Currently, MRM is implemented by hand. This is a tedious, error-prone, and non-portable process. MRMAide, in contrast, will automatically suggest to a developer where and how to connect different components and/or simulations. MRMAide has three phases of operation: pre-processing, data abstraction, and validation. During pre-processing the components to be linked together are evaluated in order to identify appropriate mapping points. During data abstraction those mapping points are linked via data abstraction algorithms. During validation developers receive feedback regarding their newly created models relative to existing baselined models. The current work presents an overview of the various problems encountered during MRM and the various technologies utilized by MRMAide to overcome those problems.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Allyn Treshansky and Robert M. McGraw "MRMAide: a mixed resolution modeling aide", Proc. SPIE 4716, Enabling Technologies for Simulation Science VI, (15 July 2002); https://doi.org/10.1117/12.474913
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling

Associative arrays

Detection and tracking algorithms

Performance modeling

Algorithm development

Computer simulations

3D modeling

RELATED CONTENT

Research on recommendation method based on tensor similarity
Proceedings of SPIE (December 08 2022)
Leverage estimation for multi-output neural networks
Proceedings of SPIE (September 28 2016)
The ATR Center and ATRpedia
Proceedings of SPIE (March 25 2008)

Back to Top