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We consider the problem of planning machine loading and tool allocation in a flexible manufacturing system. The problem is modelled as a discrete generalized network with simple side constraints. An algorithm is described to yield a solution to this problem. An important aspect of the modelling process presented here is its ease of application to other planning problems in flexible manufacturing systems. [ABSTRACT FROM AUTHOR]

We propose matrix decomposition algorithms for the efficient solution of the linear systems arising from Kansa radial basis function discretizations of elliptic boundary value problems in regular polygonal domains. These algorithms exploit the symmetry of the domains of the problems under consideration which lead to coefficient matrices possessing block circulant structures. In particular, we consider the Poisson equation, the inhomogeneous biharmonic equation, and the inhomogeneous Cauchy-Navier equations of elasticity. Numerical examples demonstrating the applicability of the proposed algorithms are presented. [ABSTRACT FROM AUTHOR]

A nonlinear, adaptive method to remove the harmonic noise that commonly resides in geophysical data is proposed in this study. This filtering method is based on the ensemble empirical mode decomposition algorithm in conjunction with the logarithmic transform. We present a synthetic model study to investigate the capability of signal reconstruction from the decomposed data, and compare the results with those derived from other 2-D adaptive filters. Applications to the real seismic data acquired by using an ocean bottom seismograph and to a shot gather of the ground penetrating radar demonstrate the robustness of this method. Our work proposes a concept that instead of Fourier-based approaches, the harmonic noise removal in geophysical data can be achieved effectively by using an alternative nonlinear adaptive data analysis method, which has been applied extensively in other scientific studies. [ABSTRACT FROM AUTHOR]

Recently, particular solutions using radial basis functions have been used as a basis for solving inhomogeneous partial differential equations as a one-stage approach without the need of finding homogeneous solutions. In this paper, we adopt a newly developed adaptive greedy algorithm to enhance the performance of the one-stage method and alleviate the difficulty of ill-conditioning of the resultant matrix. To demonstrate the effectiveness of coupling these two methods, we give two 3D examples with excellent numerical results. [ABSTRACT FROM AUTHOR]

The immune algorithm (IA) is proposed to derive the rephasing strategy arrangement of laterals and distribution transformers to enhance three-phase balancing of distribution systems. The multi-objective function is formulated by considering the unbalance of phasing currents, the customer service interruption cost (CIC) and labour cost to perform the optimal rephasing strategy. For each feasible rephasing strategy, the number of customers affected with total interruption load demand and outage duration time are used to calculate the impact of system reliability because of rephasing engineering works. To demonstrate the effectiveness of the proposed methodology, a practical distribution feeder in Taipower with 271 customers is selected for computer simulation. By minimising the objective function subjected to the operation constraints, the rephasing strategy has been derived by selecting the laterals and distribution transformers for phasing adjustment. It is found that the neutral current of test feeder has been reduced to be less than the neutral overcurrent limit by executing the rephasing of laterals and distribution transformers. [ABSTRACT FROM AUTHOR]

The design of an adaptive load-shedding strategy by executing an artificial neural network (ANN) and transient stability analysis for an electric utility system is presented. To prepare the training data set for an ANN, the transient stability analysis of an actual power system has been performed to solve for minimum load shedding with various operation scenarios without causing the tripping problem of generators. The Levenberg-Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feedforward neural networks. By selecting the total power generation, total load demand and frequency decay rate as the input neurons of the ANN, the minimum amount of load shedding is determined to maintain the stability of power systems. To demonstrate the effectiveness of the proposed ANN minimum load-shedding scheme, a utility power system has been selected for computer simulation and the amount of load shedding is verified by stability analysis. [ABSTRACT FROM AUTHOR]