A robust quantitative method has been developed for measuring the size of hemispherical surface grains as observed in SPM topographs. This method uses a modified form of a Savitzky-Golay filter, by fitting successive ...
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A robust quantitative method has been developed for measuring the size of hemispherical surface grains as observed in SPM topographs. This method uses a modified form of a Savitzky-Golay filter, by fitting successive overlapping search areas within an image to a two-dimensional and using the second derivative test to find the crest of the grains. Methods for characterizing surface grains vary from analyzing X-ray diffraction patterns by using the Scherrer equation to performing random cross-sectional analyzes of SPM images. Multiple maxima are often often found for one grain during the first iteration because of the overlap between search areas. The results showed that it is important to choose the proper search area size and polynomial order.
An antenna array composed of one transmitter and multi-receivers and dedicated to measuring terminal trajectory of the target of interest, which is supposed to be in uniform rectilinear motion, is set up. On the basis...
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ISBN:
(纸本)9783540741701
An antenna array composed of one transmitter and multi-receivers and dedicated to measuring terminal trajectory of the target of interest, which is supposed to be in uniform rectilinear motion, is set up. On the basis of the model, the Vector parameter that can uniquely determine the terminal trajectory of target is introduced, and the measurement equations which describe the respective relationships between the Vector parameter and the instantaneous Doppler frequency and the phase differences extracted from echoes are established. Taking advantage of the measurement equations, we propose an algorithm of estimating the Vector parameter without resolving the phase difference ambiguity;furthermore, the detailed steps in estimating the Vector parameter using numerical optimization techniques are put forward. The Monte Carlo simulation results demonstrate the effectiveness and reliability of our numerical algorithm compared with the traditional method.
It shows how the high pressure electric gas discharge plasma near to the local thermodynamic equilibrium point can be modeled. Partial differential equations with variable coefficients and non-linear source terms are ...
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It shows how the high pressure electric gas discharge plasma near to the local thermodynamic equilibrium point can be modeled. Partial differential equations with variable coefficients and non-linear source terms are solved using the finite element method.
In this paper, a new numerical algorithm for medium-voltage overhead lines, autoreclosure, is described. The subfunction of the autoreclosure scheme that would inhibit the first shot after detecting a solid fault (as ...
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In this paper, a new numerical algorithm for medium-voltage overhead lines, autoreclosure, is described. The subfunction of the autoreclosure scheme that would inhibit the first shot after detecting a solid fault (as compared with an arc fault) is evaluated and presented. It is based on one terminal data processing and it is derived in the time domain. In the algorithm the fault nature (arcing or arcless fault) is estimated using linear least error squares estimation technique. The arc, occurring on the fault point during arcing faults on overhead lines, is included in the problem consideration. In addition, by introducing the prefault load current in the existing model, better algorithm performances and a more reliable adaptive algorithm for autoreclosure are achieved. The algorithm is derived for the case of three-phase symmetrical fault. The results of the algorithm testing through computer simulation are presented. Particularly the algorithm sensitivity to arc elongation effects, supplying network parameters, and processing of the signals in the presence of harmonics are tested and analyzed.
A model based on propagation-rate equations with consideration of transverse gain distribution is built up to describe the transverse mode competition in strongly pumped multimode fiber lasers and amplifiers. An appro...
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A model based on propagation-rate equations with consideration of transverse gain distribution is built up to describe the transverse mode competition in strongly pumped multimode fiber lasers and amplifiers. An approximate practical numerical algorithm by multilayer method is presented. Based on the model and the numerical algorithm, the behaviors of multitransverse mode competition are demonstrated and individual transverse modes power distributions of output are simulated numerically for both fiber lasers and amplifiers under various conditions. (c) 2007 Optical Society of America.
This work presents a numerical approximation of optimal control problems for non-linear distributed Hopfield Neural Network equations with diffusion term. For one spatial dimensional case, a semi-discrete numerical al...
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This work presents a numerical approximation of optimal control problems for non-linear distributed Hopfield Neural Network equations with diffusion term. For one spatial dimensional case, a semi-discrete numerical algorithm was constructed to find optimal control variable using finite element discretization, updated conjecture gradient iteration method. Furthermore, experiments demonstration will be implemented to show the effectiveness and stability through 3D graphics simulations. Copyright (c) 2006 John Wiley & Sons, Ltd.
In this paper we summarize the elements of a numerical integration scheme for elasto-plastic response of single crystals. This is intended to be compatible with large-scale explicit finite element codes and therefore ...
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In this paper we summarize the elements of a numerical integration scheme for elasto-plastic response of single crystals. This is intended to be compatible with large-scale explicit finite element codes and therefore can be used for problems involving multiple crystals and also overall behavior of polycrystalline materials. The steps described here are general for anisotropic elastic and plastic response of crystals. The crystallographic axes of the lattice are explicitly stored and updated at each time step. A plastic predict or-elastic corrector scheme is used to calculate the plastic strain rates on all active slip systems based on a rate-dependent physics-based constitutive model without the need of further auxiliary assumptions. Finally we present the results of numerous calculations using a physics-based rate- and temperature-dependent model of copper and the effect of elastic unloading, elastic crystal anisotropy, and deformation-induced lattice rotation are emphasized. (C) 2007 Elsevier Ltd. All rights reserved.
We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which cas...
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We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states;(ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step;and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in O(N) operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, hi
This paper considers the problem of factorizing a matrix with missing components into a product of two smaller matrices, also known as principal component analysis with missing data (PCAMD). The Wiberg algorithm is a ...
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This paper considers the problem of factorizing a matrix with missing components into a product of two smaller matrices, also known as principal component analysis with missing data (PCAMD). The Wiberg algorithm is a numerical algorithm developed for the problem in the community of applied mathematics. We argue that the algorithm has not been correctly understood in the computer vision community. Although there are many studies in our community, almost every one of which refers to the Wiberg study, as far as we know, there is no literature in which the performance of the Wiberg algorithm is investigated or the detail of the algorithm is presented. In this paper, we present derivation of the algorithm along with a problem in its implementation that needs to be carefully considered, and then examine its performance. The experimental results demonstrate that the Wiberg algorithm shows a considerably good performance, which should contradict the conventional view in our community, namely that minimization-based algorithms tend to fail to converge to a global minimum relatively frequently. The performance of the Wiberg algorithm is such that even starting with random initial values, it converges in most cases to a correct solution, even when the matrix has many missing components and the data are contaminated with very strong noise. Our conclusion is that the Wiberg algorithm can also be used as a standard algorithm for the problems of computer vision.
Bounded-influence estimation is a well developed and useful theory. It provides fairly efficient estimators which are robust to outliers and local model departures. However, its use has been limited thus far, mainly b...
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Bounded-influence estimation is a well developed and useful theory. It provides fairly efficient estimators which are robust to outliers and local model departures. However, its use has been limited thus far, mainly because of computational difficulties. A careful implementation in modern statistical software can effectively overcome the numerical problems of bounded-influence estimators. The proposed approach is based on general methods for solving estimating equations, together with suitable methods developed in the statistical literature, such as the delta algorithm and nested iterations. The focus is on Mallows estimation in generalized linear models and on optimal bias-robust estimation in models for independent data, such as regression models with asymmetrically distributed errors. (c) 2006 Elsevier B.V. All rights reserved.
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