Sloper, John Erik (2010) Error management in ATLAS TDAQ : an intelligent systems approach. PhD thesis, University of Warwick.

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Abstract

This thesis is concerned with the use of intelligent system techniques (IST) within
a large distributed software system, specifically the ATLAS TDAQ system which
has been developed and is currently in use at the European Laboratory for Particle
Physics(CERN). The overall aim is to investigate and evaluate a range of ITS
techniques in order to improve the error management system (EMS) currently used
within the TDAQ system via error detection and classification. The thesis work
will provide a reference for future research and development of such methods in the
TDAQ system.
The thesis begins by describing the TDAQ system and the existing EMS, with a
focus on the underlying expert system approach, in order to identify areas where
improvements can be made using IST techniques. It then discusses measures of
evaluating error detection and classification techniques and the factors specific to
the TDAQ system.
Error conditions are then simulated in a controlled manner using an experimental
setup and datasets were gathered from two different sources. Analysis and processing
of the datasets using statistical and ITS techniques shows that clusters exists in
the data corresponding to the different simulated errors.
Different ITS techniques are applied to the gathered datasets in order to realise an
error detection model. These techniques include Artificial Neural Networks (ANNs),
Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and
a comparison of the respective advantages and disadvantages is made.
The principle conclusions from this work are that IST can be successfully used to
detect errors in the ATLAS TDAQ system and thus can provide a tool to improve
the overall error management system. It is of particular importance that the IST can
be used without having a detailed knowledge of the system, as the ATLAS TDAQ
is too complex for a single person to have complete understanding of. The results
of this research will benefit researchers developing and evaluating IST techniques in
similar large scale distributed systems.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH):	Artificial intelligence, Error analysis (Mathematics), Errors
Official Date:	November 2010
Dates:	Date Event November 2010 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Hines, Evor
Extent:	xxv, 240 leaves : ill., charts
Language:	eng

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