Electrical and Electronic Engineering - Research Publications

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Subject: Artificial Intelligence and Image Processing × Start date: 2006 × End date: 2006 ×

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    Computational intelligence techniques
    BHARAT SUNDARAM, S. ; PALANISWAMI, M. ; SHILTON, A. ; BEGG, R. (Idea Group, 2006)
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    A Low Complexity Frequency-Domain Approach to SIMO System Identification
    WANG, S. ; MANTON, J. (IEEE - Institute of Electrical and Electronic Engineers, 2006)
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    Very high speed, close field, object positioning using tri-linear CCDs
    Jahshan, D ; Bredenfeld, A ; Jacoff, A ; Noda, I ; Takahashi, Y (SPRINGER-VERLAG BERLIN, 2006)
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    PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION
    Gubbi, J ; Lai, DTH ; Palaniswami, M ; Parker, M (WORLD SCIENTIFIC PUBL CO PTE LTD, 2006-12)
    Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .
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    Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning
    Sun, M ; Ge, SS ; Mareels, IMY (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2006-06)
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    Stability and motor adaptation in human arm movements
    Burdet, E ; Tee, KP ; Mareels, I ; Milner, TE ; Chew, CM ; Franklin, DW ; Osu, R ; Kawato, M (SPRINGER, 2006-01)
    In control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.
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