Browsing by Author "Department of Computer Science"

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  • Hülse, Martin; Lee, Mark (Springer Nature, 2010-08)
    The engineering of humanoid or similar robot systems requires frameworks and architectures that support the integration of a variety of sensorimotor modalities. Within our computational framework for visually guided reaching ...
  • Meng, Qinggang; Lee, Mark (2003-07)
    In this paper, we describe an approach to adapting home service robot behaviors by reusing the experience learned and interacting with humans. Experience is situation-based and is integrated into each behavior in a distributed ...
  • Rowland, Jeremy John; Taylor, Janet (2002)
    This paper relates to supervised interpretation of the infrared analytical spectra of complex biological samples. The aim is to produce a model that can predict the value of a measurand of interest, such as the concentration ...
  • Yang, Longzhi; Shen, Qiang (2011-12-12)
    Fuzzy interpolative reasoning strengthens the power of fuzzy inference by the enhancement of the robustness of fuzzy systems and the reduction of the systems’ complexity. However, after a series of interpolations, it is ...
  • Shen, Qiang; Yang, Longzhi (2010-07-27)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process [11]. This ...
  • Shen, Qiang; Yang, Longzhi (2010-07)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning owning to its efficient identification and correction of defective interpolated rules during the interpolation process [11]. This ...
  • Shen, Qiang; Yang, Longzhi (2011-09-26)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all ...
  • Shen, Qiang; Yang, Longzhi (IEEE Press, 2011-09-26)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It first identifies all possible sets of faulty fuzzy reasoning components, termed the candidates, each of which may have led to all ...
  • Shen, Qiang; Yang, Longzhi (2011-09-26)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components ...
  • Shen, Qiang; Yang, Longzhi (IEEE Press, 2011-09-26)
    Adaptive fuzzy interpolation strengthens the potential of fuzzy interpolative reasoning. It views interpolation procedures as artificially created system components, and identifies all possible sets of faulty components ...
  • Mario; Frank; Martin (IGI Global, 2008-01)
    Neurodynamics is the application of dynamical systems theory (DST) to the analysis of the structure and function of recurrent neural networks (RNNs). In this chapter, we present RNNs artificially evolved for the control ...
  • Giagkos, Alexandros; Tuci, Elio; Wilson, Myra (Springer Nature, 2015-08-30)
    In this paper we present advances to our previously proposed coordination system for groups of unmanned aerial vehicles that provide a network backbone over mobile ground-based vehicles. Evolutionary algorithms are employed ...
  • Wilson, James; Geng, Tao; Sheldon, Michael; Hülse, Martin; Lee, Mark (2010-11)
    This paper describes a prototype robot grasping system that uses human grasping synergies and a self-organizing map to learn object affordances. The bio-inspired design of the system is presented as well as some of the ...
  • Shen, Qiang; Shang, Changjing (2006)
    This paper presents an application of supervised machine learning approaches to the classification of the yeast S. cerevisiae gene expression data. Established feature selection techniques based on information gain ranking ...
  • Shang, Changjing; Shen, Qiang (2008-07-03)
    This paper presents a methodological approach for developing image classifiers that work by exploiting the technical potential of both fuzzy-rough feature selection and neural network-based classification. The use of ...
  • Shang, Changjing; Shen, Qiang (IEEE Press, 2008)
    This paper presents a methodological approach for developing image classifiers that work by exploiting the technical potential of both fuzzy-rough feature selection and neural network-based classification. The use of ...
  • Jin, Shangzhu; Shen, Qiang; Diao, Ren (IEEE Press, 2014)
    Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in sparse rule-based systems. However, in real-world applications of inter-connected rule bases, ...
  • Miles, Helen; Wilson, Andrew; Labrosse, Frédéric; Tiddeman, Bernie; Ritsos, Panagiotis; Mearman, Joseph; Griffiths, Seren; Edwards, Ben; Möller, Katharina; Karl, Raimund; Roberts, Jonathan (2016-02)
    By collecting images of heritage assets from members of the public and processing them to create 3D reconstructed models, the HeritageTogether project has accomplished the digital recording of nearly 80 sites across Wales, ...
  • Lee, Mark; Bell, Jonathan; Coghill, George (2001-05)
    Qualitative electrical circuit models have now been developed by the QR community to the extent that they can be found in commercial software products. Our own work, including this paper, deals with steady-state models ...
  • Witt, Carsten; Friedrich, Tobias; Hebbinghaus, Nils; He, Jun; Neumann, Frank (2009-05-28)
    Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast, the theoretical understanding of the interplay of different optimization methods is rare. In this paper, we make a first ...