Abstract-The paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in prediction of spatial phenomena by using a wireless sensor network. The spatial field is modelled by Gaussian Markov random fields (GMRF), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information between random variables at a selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF based optimality criterion is then proved to be very computationally efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the mutual information set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within (1 − 1/e) of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.
In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented for two actuated and unactuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are intractable to be determined. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
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