Created by W.Langdon from gp-bibliography.bib Revision:1.7185
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.",
Genetic Programming entries for John Erik Sloper