This study is concerned with the problem to solve the continuous coupled Riccati matrix equations in It?Markov jump systems.A new iterative algorithm is developed by using the latest estimation information and introdu...
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This study is concerned with the problem to solve the continuous coupled Riccati matrix equations in It?Markov jump systems.A new iterative algorithm is developed by using the latest estimation information and introducing a tuning *** iterative solution obtained by the proposed algorithm with zero initial conditions converges to the unique positive definite solution of the considered *** convergence rate of the algorithm is dependent on the adjustable ***,a numerical example is provided to show the effectiveness of the presented algorithms.
Blind deconvolution and blind equalization have been important interesting topics in diverse field including data communication, image processing, and geophysical data processing. Recently, Inouye and Habe introduced ...
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Blind deconvolution and blind equalization have been important interesting topics in diverse field including data communication, image processing, and geophysical data processing. Recently, Inouye and Habe introduced a multistage criterion for attaining blind deconvolution of multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. In this correspondence, based on their criterion, we present iterative algorithms for solving the blind deconvolution problem of MIMO LTI systems. However, their criterion should be subjected to several constraints of equations. Therefore, they proposed a new constraint-free multistage criterion for accomplishing the blind deconvolution of MIMO LTI systems. Based on their unconstrained criterion, we show iterative algorithms for solving the blind deconvolution of multichannel LTI systems.
The generalized t-distribution (GT) is well-known because of its flexibility in transforming into many popular distributions. However, implementation of data reconciliation (DR) estimator using GT noise is somehow dif...
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The generalized t-distribution (GT) is well-known because of its flexibility in transforming into many popular distributions. However, implementation of data reconciliation (DR) estimator using GT noise is somehow difficult due to its complex structure. This work proposes two iterative algorithms to ease the complexity of the GT DR estimator, hence making it easy to implement even in a large-scale problem. We also point out the convergence condition for each algorithm. Some simulation examples are shown to verify the effectiveness of the proposed algorithms on computational time. The results from this work can also be applied to other data reconciliation estimators.
A reconfigurable manufacturing system (RMS), one of state-of-the-art manufacturing system technologies, is the one designed at the outset for rapid changes in its hardware and software components in order to quickly a...
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A reconfigurable manufacturing system (RMS), one of state-of-the-art manufacturing system technologies, is the one designed at the outset for rapid changes in its hardware and software components in order to quickly adjust its production capacity and functionality in response to market or system changes. In this study, we consider a cellular RMS with multiple reconfigurable machining cells (RMCs), each of which has numerical control machines, a setup station, and an automatic material handling and storage system. Each machine within the RMC has an automatic tool changer and a tool magazine of a limited capacity. Two important operational problems, part grouping and loading, are considered in this study. Part grouping is the problem of allocating parts to RMCs, and loading is the problem of allocating operations and their cutting tools to machines within the RMC. An integer programming model is suggested to represent the two problems at the same time for the objective of balancing the workloads assigned to machines. Then, due to the complexity of the problem, we suggest two iterative algorithms in which the two problems are solved repeatedly until a solution is obtained. Computational experiments were done on various test instances and the results are reported. Journal of the Operational Research Society (2012) 63, 1635-1644. doi:10.1057/jors.2012.9 Published online 29 February 2012
An iterative algorithm, here referred to as ALG-BL, has recently been proposed for computing exact aliasing probabilities in signature analysis. A new such algorithm, ALG-MK, is presented here. While ALG-BL was derive...
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An iterative algorithm, here referred to as ALG-BL, has recently been proposed for computing exact aliasing probabilities in signature analysis. A new such algorithm, ALG-MK, is presented here. While ALG-BL was derived from a Boolean expressions formulation of the problem, ALG-MK is derived from a Markov process model of signature analysis. We reformulate ALG-BL such that it may also be viewed as based on a Markov process, and compare the computational complexities of the two algorithms. The time complexity of the new algorithm ALG-MK is O(L 2k), while that of ALG-BL is O(Lf2k+f), where k is the length of the signature register, f is the number of feedback taps, and L is the test sequence length. The space complexity of ALG-MK is O(2k), whereas ALG-BL has O(2k+f) space complexity. Both ALG-MK and ALG-BL may be used to study aliasing under generalized error models and are applicable to any linear or nonlinear compaction scheme described by a Markov process.
Finite-element discretization produces linear equations in the form Ax=b, where A is large, sparse, and banded with proper ordering of the variables x. The solution of such equations on distributed-memory message-pass...
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Finite-element discretization produces linear equations in the form Ax=b, where A is large, sparse, and banded with proper ordering of the variables x. The solution of such equations on distributed-memory message-passing multiprocessors implementing the hypercube topology is addressed. iterative algorithms based on the conjugate gradient method are developed for hypercubes designed for coarse-grained parallelism. The communication requirements of different schemes for mapping finite-element meshes onto the processors of a hypercube are analyzed with respect to the effect of communication parameters of the architecture. Experimental results for a 16-node Intel 80386-based iPSC/2 hypercube are presented and discussed.
In this paper, we propose iterative algorithms for solving image restoration problems. The iterative algorithms are based on decoupling of deblurring and denoising steps in the restoration process. In the deblurring s...
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In this paper, we propose iterative algorithms for solving image restoration problems. The iterative algorithms are based on decoupling of deblurring and denoising steps in the restoration process. In the deblurring step, an efficient deblurring method using fast transforms can be employed. In the denoising step, effective methods such as the wavelet shrinkage denoising method or the total variation denoising method can be used. The main advantage of this proposal is that the resulting algorithms can be very efficient and can produce better restored images in visual quality and signal-to-noise ratio than those by the restoration methods using the combination of a data-fitting term and a regularization term. The convergence of the proposed algorithms is shown in the paper. Numerical examples are also given to demonstrate the effectiveness of these algorithms.
algorithms are presented which solve the planar convex hull problem on a variety of mesh-connected arrays of processors without using recursion or divide-and-conquer techniques. The algorithms for one-way iterative ar...
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algorithms are presented which solve the planar convex hull problem on a variety of mesh-connected arrays of processors without using recursion or divide-and-conquer techniques. The algorithms for one-way iterative arrays, one-way linear cellular arrays, and two-way linear cellular arrays all operate in time O(n). The algorithm for a two-way d-dimensional cellular array operates in time O(n1/d). These algorithms are optimal for their arrays. The last algorithm can be used on an O(n) processor hypercube with a time complexity of O(log2 n). We also show how these algorithms can be adapted to fully dynamic implementations with optimal throughput and turn-around. We believe that these algorithms may have performance advantages over existing parallel divide-and-conquer algorithms for planar convex hull.
This paper deals with a method for approximating a solution of the fixed point problem: find (x) over tilde is an element of H;(x) over tilde = (proj(F(T)) .S)(x) over tilde, where H is a Hilbert space, S is some nonl...
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This paper deals with a method for approximating a solution of the fixed point problem: find (x) over tilde is an element of H;(x) over tilde = (proj(F(T)) .S)(x) over tilde, where H is a Hilbert space, S is some nonlinear operator and T is a nonexpansive mapping on a closed convex subset C and proj(F(T)) denotes the metric projection on the set of fixed points of T. First, we prove a strong convergence theorem by using a projection method which solves some variational inequality. As a special case, this projection method also solves some minimization problems. Secondly, under different restrictions on parameters, we obtain another strong convergence result which solves the above fixed point problem. (C) 2010 Elsevier Ltd. All rights reserved.
The authors study an iterative algorithm for learning a linear Gaussian observation model with an exponential power scale mixture prior (EPSM). This is a generalisation of previous study based on the Gaussian scale mi...
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The authors study an iterative algorithm for learning a linear Gaussian observation model with an exponential power scale mixture prior (EPSM). This is a generalisation of previous study based on the Gaussian scale mixture prior. The authors use the principle of majorisation minimisation to derive the general iterative algorithm which is related to a reweighted l(p)-minimisation algorithm. The authors then show that the Gaussian and Laplacian scale mixtures are two special cases of the EPSM and the corresponding learning algorithms are related to the reweighted l(2)- and l(1)-minimisation algorithms, respectively. The authors also study a particular case of the EPSM which is a Pareto distribution and discuss Bayesian methods for parameter estimation.
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