Error function approximations of interest in electrochemistry.

The error function is present in several equations describing eletrode processes. But, only approximations of this function are used. In this work, these and other approximations are studied and evaluated according to precision.

Bayesian error bars for regression

Regression problems are concerned with predicting the values of one or more continuous quantities, given the values of a number of input variables. For virtually every application of regression, however, it is also important to have an indication of the uncertainty in the predictions. Such uncertainties are expressed in terms of the error bars, which specify the standard deviation of the distribution of predictions about the mean. Accurate estimate of error bars is of practical importance especially when safety and reliability is an issue. The Bayesian view of regression leads naturally to two contributions to the error bars. The first arises from the intrinsic noise on the target data, while the second comes from the uncertainty in the values of the model parameters which manifests itself in the finite width of the posterior distribution over the space of these parameters. The Hessian matrix which involves the second derivatives of the error function with respect to the weights is needed for implementing the Bayesian formalism in general and estimating the error bars in particular. A study of different methods for evaluating this matrix is given with special emphasis on the outer product approximation method. The contribution of the uncertainty in model parameters to the error bars is a finite data size effect, which becomes negligible as the number of data points in the training set increases. A study of this contribution is given in relation to the distribution of data in input space. It is shown that the addition of data points to the training set can only reduce the local magnitude of the error bars or leave it unchanged. Using the asymptotic limit of an infinite data set, it is shown that the error bars have an approximate relation to the density of data in input space.

Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism

A magneto-rheological (MR) fluid damper is a semi-active control device that has recently begun to receive more attention in the vibration control community. However, the inherent nonlinear nature of the MR fluid damper makes it challenging to use this device to achieve high damping control system performance. Therefore the development of an accurate modeling method for a MR fluid damper is necessary to take advantage of its unique characteristics. Our goal was to develop an alternative method for modeling a MR fluid damper by using a self tuning fuzzy (STF) method based on neural technique. The behavior of the researched damper is directly estimated through a fuzzy mapping system. In order to improve the accuracy of the STF model, a back propagation and a gradient descent method are used to train online the fuzzy parameters to minimize the model error function. A series of simulations had been done to validate the effectiveness of the suggested modeling method when compared with the data measured from experiments on a test rig with a researched MR fluid damper. Finally, modeling results show that the proposed STF interference system trained online by using neural technique could describe well the behavior of the MR fluid damper without need of calculation time for generating the model parameters.

Diffraction of a cylindrical pulse by a half plane under mixed boundary conditions

The general time dependent source problem has been solved by the method of transforms (Laplace, Lebedev–Kontorovich in succession) and the solution is obtained in the form of an infinite series involving Legendre functions. The solutions in the case of harmonic time dependence and the incident plane wave have been derived from the above solution and are presented in the form of an infinite series. In the case of an incident plane wave, the series has been summed and the final solution involves an improper integral which behaves like a complementary error function for large values of the argument. Finally, the far field evaluation has been shown. The results are compared with those of Sommerfeld's half-plane diffraction problem with unmixed boundary conditions.

Exploring isospectral spring-mass systems with firefly algorithm

This paper investigates in-line spring-mass systems (An), fixed at one end and free at the other, with n-degrees of freedom (d.f.). The objective is to find feasible in-line systems (B(n)) that are isospectral to a given system. The spring-mass systems, A(n) and B(n), are represented by Jacobi matrices. An error function is developed with the help of the Jacobi matrices A(n) and B(n). The problem of finding the isospectral systems is posed as an optimization problem with the aim of minimizing the error function. The approach for creating isospectral systems uses the fact that the trace of two isospectral Jacobi matrices A(n) and B(n) should be identical. A modification is made to the diagonal elements of the given Jacobi matrix (A(n)), to create the isospectral systems. The optimization problem is solved using the firefly algorithm augmented by a local search procedure. Numerical results are obtained and resulting isospectral systems are shown for 4 d.f. and 10 d.f. systems.

Improved parametric models and DPCM techniques for EGG data compiression

This paper presents a novel algorithm for compression of single lead Electrocardiogram (ECG) signals. The method is based on Pole-Zero modelling of the Discrete Cosine Transformed (DCT) signal. An extension is proposed to the well known Steiglitz-Hcbride algorithm, to model the higher frequency components of the input signal more accurately. This is achieved by weighting the error function minimized by the algorithm to estimate the model parameters. The data compression achieved by the parametric model is further enhanced by Differential Pulse Code Modulation (DPCM) of the model parameters. The method accomplishes a compression ratio in the range of 1:20 to 1:40, which far exceeds those achieved by most of the current methods.

On the relation between K-means and PLSA

Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special case of NMF with matrix L2 norm based error function. In this paper our objective is to analyze the relation between K-means clustering and PLSA by examining the KL-divergence function and matrix L2 norm based error function.

Reaction dynamics under confinement: an exact path integral treatment of a two-stage model of stochastic gene expression

Gene expression in living systems is inherently stochastic, and tends to produce varying numbers of proteins over repeated cycles of transcription and translation. In this paper, an expression is derived for the steady-state protein number distribution starting from a two-stage kinetic model of the gene expression process involving p proteins and r mRNAs. The derivation is based on an exact path integral evaluation of the joint distribution, P(p, r, t), of p and r at time t, which can be expressed in terms of the coupled Langevin equations for p and r that represent the two-stage model in continuum form. The steady-state distribution of p alone, P(p), is obtained from P(p, r, t) (a bivariate Gaussian) by integrating out the r degrees of freedom and taking the limit t -> infinity. P(p) is found to be proportional to the product of a Gaussian and a complementary error function. It provides a generally satisfactory fit to simulation data on the same two-stage process when the translational efficiency (a measure of intrinsic noise levels in the system) is relatively low; it is less successful as a model of the data when the translational efficiency (and noise levels) are high.

Exploring isospectral cantilever beams using electromagnetism inspired optimization technique

Isospectral beams have identical free vibration frequency spectrum for a specific boundary condition. The problem of finding non-uniform beams which are isospectral to a given uniform beam, with fixed-free boundary condition, leads to a multimodal optimization problem. The first Q natural frequencies of the given uniform Euler-Bernoulli beam are determined using analytical solution. The first Q natural frequencies of a non-uniform beam are obtained with the help of finite element modeling. In order to obtain the non-uniform beams isospectral to a given uniform beam, an error function is designed, which calculates the difference between the spectra of the given uniform beam and the non-uniform beam. In our study, this error function is minimized using electromagnetism inspired optimization technique, a population based iterative algorithm inspired by the attraction-repulsion physics of electromagnetism. Numerical results show the existence of the isospectral non-uniform beams for a given uniform beam, which occur as local minima. Non-uniform beams isospectral to a damaged beam, are also explored using the proposed methodology to illustrate the fact that accurate structural damage identification is difficult by just frequency measurements. (C) 2012 Elsevier B.V. All rights reserved.

Digital holographic aberration compensation in electron holography

This paper proposes a new digital method to compensate for the aberration of an electron objective lens in electron holography. In this method, the object wavefront in the exit pupil plane is numerically reconstructed from a digitized electron hologram, and is corrected by multiplying it with the conjugated phase-error function. Then, an aberration-free image can be obtained by calculating the Fresnel integral of this corrected wavefront. In comparison with traditional methods, this method is much more convenient and accurate. Some verifying experiments are also presented in this paper. (C) 2003 Society of Photo-optical Instrumentation Engineers.

光栅位移测量系统的误差自修正方法研究

文中介绍的误差自修正方法是通过光栅位移测量系统中单片机对光栅传感器的多个零位信号进行计数,并根据测量值和系统设定值得到的误差函数自动进行误差修正。实验结果表明,该方法对光栅位移测量系统的误差既可自动进行有效的修正,又可提高系统的测量精度。

Identificação de danos estruturais a partir do modelo de superfície de resposta

A identificação de danos estruturais é uma questão de fundamental importância na engenharia, visto que uma estrutura está sujeita a processos de deterioração e a ocorrência de danos durante a sua vida útil. A presença de danos compromete o desempenho e a integridade estrutural, podendo colocar vidas humanas em risco e resultam em perdas econômicas consideráveis. Técnicas de identificação de danos estruturais e monitoramento de estruturas fundamentadas no ajuste de um Modelo de Elementos Finitos (MEF) são constantes na literatura especializada. No entanto, a obtenção de um problema geralmente mal posto e o elevado custo computacional, inerente a essas técnicas, limitam ou até mesmo inviabilizam a sua aplicabilidade em estruturas que demandam um modelo de ordem elevada. Para contornar essas dificuldades, na formulação do problema de identificação de danos, pode-se utilizar o Modelo de Superfície de Reposta (MSR) em substituição a um MEF da estrutura. No presente trabalho, a identificação de danos estruturais considera o ajuste de um MSR da estrutura, objetivando-se a minimização de uma função de erro definida a partir das frequências naturais experimentais e das correspondentes frequências previstas pelo MSR. Estuda-se o problema de identificação de danos estruturais em uma viga de Euler-Bernoulli simplesmente apoiada, considerando as frequências naturais na formulação do problema inverso. O comportamento de uma viga de Euler-Bernoulli simplesmente apoiada na presença de danos é analisado, com intuito de se verificar as regiões onde a identificação dos mesmos pode apresentar maior dificuldade. No processo de identificação de danos, do presente trabalho, são avaliados os tipos de superfícies de resposta, após uma escolha apropriada do tipo de superfície de resposta a ser utilizado, determina-se a superfície de resposta considerando os dados experimentais selecionados a partir do projeto ótimo de experimentos. A utilização do método Evolução Diferencial (ED) no problema inverso de identificação de danos é considerado inerente aos resultados numéricos obtidos, a estratégia adotada mostrou-se capaz de localizar e quantificar os danos com elevada acurácia, mostrando a potencialidade do modelo de identificação de danos proposto.

Effects of rapid thermal annealing on the optical properties of GaNxAs1-x/GaAs single quantum well structure grown by molecular beam epitaxy

The effect of rapid thermal annealing (RTA) on the optical properties of GaNxAs1-x/GaAs strained single quantum well (SQW) was studied by low-temperature photoluminescence (PL). The GaNxAs1-x/GaAs SQW structures were prepared by dc active nitrogen plasma assisted molecular beam epitaxy. PL measurements on a series of samples with different well widths and nitrogen compositions were used to evaluate the effects of RTA. The annealing temperature and time were varied from 650 to 850 degrees C and 30 s to 15 min, respectively. Remarkable improvements of the optical properties of the samples were observed after RTA under optimum conditions. The interdiffusion constants have been calculated by taking into account error function diffusion and solving the Schrodinger equation. The estimated interdiffusion constants D are 10(-17)-10(-16) cm(2)/s for the earlier annealing conditions. Activation energies of 6-7 eV are obtained by fitting the temperature dependence of the interdiffusion constants. (C) 2000 American Institute of Physics. [S0021-8979(00)10401-3].

Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.