Error Management in ATLAS TDAQ: An Intelligent Systems approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "John Erik Sloper",
  title =        "Error Management in {ATLAS} {TDAQ}: An Intelligent
                 Systems approach",
  year =         "2010",
  school =       "School of Engineering, Warwick University",
  address =      "UK",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, high energy physics, CERN, ANN,
  URL =          "",
  size =         "300 pages",
  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

                 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
  notes =        "Livio Mapelli",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  oai =          "",

Genetic Programming entries for John Erik Sloper