Browsing by Author "Price, Christopher"

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  • An Effective Practical Model-Based Detectability Tool 
    Snooke, Neal; Price, Christopher (2011-12-12)
    This paper builds on the ability to produce a comprehensive automated set of component fault¿ observation relations for vehicle on-board systems using qualitative model based reasoning techniques. Observations are typically ...
  • An Effective Practical Model-Based Detectability Tool 
    Snooke, Neal; Price, Christopher (2011-12-12)
    This paper builds on the ability to produce a comprehensive automated set of component fault– observation relations for vehicle on-board systems using qualitative model based reasoning techniques. Observations are typically ...
  • Feature Grouping for Intrusion Detection Based on Mutual Information 
    Song, Jingping; Zhu, Zhiliang; Price, Christopher (2014)
    Intrusion detection is an important task for network operators in today’s Internet. Traditional network intrusion detection systems rely on either specialized signatures of previously seen attacks, or on labeled traffic ...
  • Modified Mutual Information-based Feature Selection for Intrusion Detection Systems in Decision Tree Learning 
    Song, Jingping; Zhu, Zhiliang; Scully, Peter; Price, Christopher (2014-07)
    As network-based technologies become omnipresent, intrusion detection and prevention for these systems become increasingly important. This paper proposed a modified mutual information-based feature selection algorithm ...
  • Object tracking based on incremental Bi-2DPCA learning with sparse structure 
    Bai, Bendu; Li, Ying; Fan, Jiulun; Price, Christopher; Shen, Qiang (2015-04-01)
    In this paper, we propose a novel object tracking method that can work well in challenging scenarios such as appearance changes, motion blurs, and especially partial occlusions and noise. Our method applies bilateral ...
  • Rough-fuzzy rule interpolation 
    Chen, Chengyuan; MacParthalain, Neil; Li, Ying; Price, Christopher; Quek, Chai; Shen, Qiang (2016-07-10)
    Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy ...