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A Statistical Approach for Validation of Task Simulation Models with Intricate Temporal Execution Dependencies

Fulltext:


Publication Type:

Conference/Workshop Paper

Venue:

Proceedings of the Work-In-Progress (WIP) track of the 16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS10)


Abstract

This paper presents a novel approach to validation of temporal simulation models extracted from real industrial control systems containing intricate task execution dependencies, by introducing existing mature statistical methods to the context. The proposed approach firstly collects sampling distributions of response time data of tasks in both the modeled system and the model, in terms of simple random samples (SRS). The second step of the approach is to compare the sampling distributions using a non-parametric Kolmogorov-Smirnov test. After evaluating a fictive system model inspired by a real robotic control system, the proposed algorithm shows the possibility of identifying the temporal differences between a target system and its extracted model, i.e., whether the model is a sufficiently accurate approximation of the target system. The approach makes few assumptions on the system design and scales to very large and complex systems.

Bibtex

@inproceedings{Lu1778,
author = {Yue Lu and Johan Kraft and Thomas Nolte and Christer Norstr{\"o}m},
title = {A Statistical Approach for Validation of Task Simulation Models with Intricate Temporal Execution Dependencies},
pages = {5--8},
month = {April},
year = {2010},
booktitle = {Proceedings of the Work-In-Progress (WIP) track of the 16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS10)},
url = {http://www.es.mdu.se/publications/1778-}
}