The theoretical model of direct diffraction phase-contrast imaging with partially coherent x-ray source is expressed by an operator of multiple integral. It is presented that the integral operator is linear. The probl...
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The theoretical model of direct diffraction phase-contrast imaging with partially coherent x-ray source is expressed by an operator of multiple integral. It is presented that the integral operator is linear. The problem of its phase retrieval is described by solving an operator equation of multiple integral. It is demonstrated that the solution of the phase retrieval is unstable. The numerical simulation is performed and the result validates that the solution of the phase retrieval is unstable.
This paper addresses an optimization model for assembly line-balancing problem in order to improve the line balance of a production line under a human-centric and dynamic apparel assembly process. As the variance of o...
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This paper addresses an optimization model for assembly line-balancing problem in order to improve the line balance of a production line under a human-centric and dynamic apparel assembly process. As the variance of operator efficiency is vital to line imbalance in labor intensive industry, an approach is proposed to balance production line through optimal operator allocation with the consideration of operator efficiency. Two recursive algorithms are developed to generate all feasible solutions for operator allocation. Three objectives, namely, the lowest standard deviation of operation efficiency, the highest production line efficiency and the least total operation efficiency waste, are devised to find out the optimal solution of operator allocation. The method in this paper improves the flexibility of the operator allocation on different sizes of data set of operations and operators, and enhances the efficiency of searching for the optimal solution of big size data set. The results of experiments are reported. The performance comparison demonstrates that the proposed optimization method outperforms the industry practice. (c) 2006 Elsevier Ltd. All rights reserved.
A way to efficiently compute helicity amplitudes for arbitrary tree-level scattering processes in QCD is presented. The scattering amplitude is evaluated recursively through a set of Dyson-Schwinger equations. The com...
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A way to efficiently compute helicity amplitudes for arbitrary tree-level scattering processes in QCD is presented. The scattering amplitude is evaluated recursively through a set of Dyson-Schwinger equations. The computational cost of this algorithm grows asymptotically as 3(n), where n is the number of particles involved in the process, compared to n! in the traditional Feynman graphs approach. Unitary gauge is used and mass effects are available as well. Additionally, the color and helicity structures are appropriately transformed so the usual summation is replaced by the Monte Carlo techniques. (c) 2005 Elsevier B.V. All rights reserved.
The paper discusses two algorithms for solving the Zakai equation in the time-homogeneous diffusion filtering model with possible correlation between the state process and the observation noise. Both algorithms rely o...
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The paper discusses two algorithms for solving the Zakai equation in the time-homogeneous diffusion filtering model with possible correlation between the state process and the observation noise. Both algorithms rely on the Cameron-Martin version of the Wiener chaos expansion, so that the approximate filter is a finite linear combination of the chaos elements generated by the observation process. The coefficients in the expansion depend only on the deterministic dynamics of the state and observation processes. For real-time applications, computing the coefficients in advance improves the performance of the algorithms in comparison with most other existing methods of nonlinear filtering. The paper summarizes the main existing results about these Wiener chaos algorithms and resolves some open questions concerning the convergence of the algorithms in the noise-correlated setting. The presentation includes the necessary background on the Wiener chaos and optimal nonlinear filtering.
We present a novel method to online generation of time-optimal acceleration, velocity, and position trajectory values for servo-control systems using low-cost fixed-point processors with no on-chip multipliers. As in ...
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We present a novel method to online generation of time-optimal acceleration, velocity, and position trajectory values for servo-control systems using low-cost fixed-point processors with no on-chip multipliers. As in the case of many product designs, reducing system cost is a key constraint in our method. We address this constraint by optimizing required memory size, computational time, and introducing multiplication-free recursive algorithms most suitable for real-time implementation on low-cost microcontrollers. An efficient search algorithm is presented optimizing the trajectories with respect to total move time for any distance subject to physical constraints of the system. We also introduce a normalized set of first-order difference equations using only integer arithmetic and additions to compute the trajectory values. Complexity of the proposed method is compared with multiplication-based and lookup table methods using three popular microcontrollers with and without an on-chip hardware multiplier.
The generalized multi-state k-out-of-n:G system model defined by Huang et al. [5] provides more flexibilities for modeling of multi-state systems. However, the performance evaluation algorithm they proposed for such s...
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The generalized multi-state k-out-of-n:G system model defined by Huang et al. [5] provides more flexibilities for modeling of multi-state systems. However, the performance evaluation algorithm they proposed for such systems is not efficient, and it is applicable only when the k(i) values follow a monotonic pattern. In this paper, we defined the concept of generalized multi-state k-out-of-n:F systems. There is an equivalent generalized multi-state k-out-of-n:G system with respect to each generalized multi-state k-out-of-n:F system, and vice versa. The form of minimal cut vector for generalized multi-state k-out-of-n:F systems is presented. An efficient recursive algorithm based on minimal cut vectors is developed to evaluate the state distributions of a generalized multi-state k-out-of-n:F system. Thus, a generalized multi-state k-out-of-n:G system can first be transformed to the equivalent generalized multi-state k-out-of-n:F system, and then be evaluated using the proposed recursive algorithm. Numerical examples are given to illustrate the effectiveness and efficiencies of the proposed recursive algorithms.
Tchebichef moment is a novel set of orthogonal moment applied in the fields of image analysis and pattern recognition. Less work has been made for the computation of Tchebichef moment and its inverse moment transform....
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Tchebichef moment is a novel set of orthogonal moment applied in the fields of image analysis and pattern recognition. Less work has been made for the computation of Tchebichef moment and its inverse moment transform. In this paper, both a direct recursive algorithm and a compact algorithm are developed for the computation of Tchebichef moment. The effective recursive algorithm for inverse Tchebichef moment transform is also presented. Clenshaw's recurrence formula was used in this paper to transform kernels of the forward and inverse Tchebichef moment transform. There is no need for the proposed algorithms to compute the Tchebichef polynomial values. The approaches presented are more efficient compared with the straightforward methods, and particularly suitable for parallel VLSI implementation due to their regular and simple filter structures. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
作者：
Kwon, S. J.HAU
Sch Aerosp & Mech Engn Goyang 412791 South Korea
A robust Kalman filtering method is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state equation of a physical system. The predictor and ...
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A robust Kalman filtering method is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state equation of a physical system. The predictor and corrector of the discrete Kalman filter are reformulated with the perturbation estimator. In succession, the state and perturbation estimation error dynamics and the covariance propagation equations are derived. In the sequel, the recursive algorithm of combined discrete Kalman filter and perturbation estimator is obtained. The proposed robust Kalman filter has the property of integrating innovations and the adaptation capability to the time-varying uncertainties. An example of application to a mobile robot is shown to validate the performance of the proposed scheme.
An efficient numerical procedure for computing the scattering coefficients of a radially stratified tilted cylinder is discussed. Compared with the previous algorithms, computations for scattering field in our code ar...
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An efficient numerical procedure for computing the scattering coefficients of a radially stratified tilted cylinder is discussed. Compared with the previous algorithms, computations for scattering field in our code are extended to fairly large parameters, up to more than 10,000 and a few millions in number of layers, and computational time is only a few seconds. The capabilities of our code depend also on the memory of computer. The algorithm can also be used for absorbing or nonabsorbing cylinders in different electromagnetic wave bands. Compared with the known results, the algorithm is validated. At last, the algorithm is used to simulate the intensity distributions of two-layered cylinders with large size parameter and of graded-index polymer optical fiber (GI-POF) at tilted incidence, which supplies information on non-intrusive measurement on-line of refractive index profile by light scattering. (c) 2006 Elsevier B.V. All rights reserved.
In this paper we consider stochastic recursive equations of sum type, X =(D) Sigma(K)(i-1) A(i)X(i) + b, and of max type, X =(D) max{A(i)X(i) + b(i) : l <= i <= k}, where A(i), b(i), and b are random, (X-i) are ...
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In this paper we consider stochastic recursive equations of sum type, X =(D) Sigma(K)(i-1) A(i)X(i) + b, and of max type, X =(D) max{A(i)X(i) + b(i) : l <= i <= k}, where A(i), b(i), and b are random, (X-i) are independent, identically distributed copies of X, and '=(D)' denotes equality in distribution. Equations of these types typically characterize limits in the probabilistic analysis of algorithms, in combinatorial optimization problems, and in many other problems having a recursive structure. We develop some new contraction properties of minimal L-s-metrics which allow us to establish general existence and uniqueness results for solutions without imposing any moment conditions. As an application we obtain a one-to-one relationship between the set of solutions to the homogeneous equation and the set of solutions to the inhomogeneous equation, for sum- and max-type equations. We also give a stochastic interpretation of a recent transfer principle of Rosler from nonnegative solutions of sum type to those of max type, by means of random scaled Weibull distributions.
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