Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocaw, SW Poland. Seven UHI cases, measured during various meteorological ...
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Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocaw, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran's I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR-MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging;GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.
This paper presents a collaborative fault diagnosis system for compensation capacitor in track circuit using adaptive optimal kernel time-frequency representations (AOK-TFRs) and adaptive genetic algorithm (AGA) based...
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This paper presents a collaborative fault diagnosis system for compensation capacitor in track circuit using adaptive optimal kernel time-frequency representations (AOK-TFRs) and adaptive genetic algorithm (AGA) based on train operational data recorded in cab signal. First, AOK-TFRs technique is applied for single compensation capacitor fault diagnosis. To deal with multiple faults, the AGA based method is introduced, however, with a relatively low efficiency of diagnosis. According to the characteristics of both methods, a collaborative fault diagnosis system is established. Experiments show that the system greatly improves the diagnosis efficiency and can be used successfully. (c) 2012 Elsevier Ltd. All rights reserved.
作者：
Wang, F.Li, Y. T.Wuhan Univ
State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Hubei Province Peoples R China
The Fourier decomposition is employed to derive the error amplification matrix for the SIMPLE algorithm implemented on a collocated grid with the Rhie-Chow interpolation method incorporated, and an approaching method ...
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The Fourier decomposition is employed to derive the error amplification matrix for the SIMPLE algorithm implemented on a collocated grid with the Rhie-Chow interpolation method incorporated, and an approaching method is developed to study the effect of relaxation factors on the convergence behaviors of SIMPLE based on characteristic analysis of the lowest and highest frequency errors. The study indicates that convergence can be guaranteed by damping out the highest and lowest frequencies with conditional combinations of both factors. In the case of convergence, the optimum convergence rate almost depends on the velocity relaxation factor, and a relatively larger velocity factor can accelerate the optimal rate.
This paper presents a provably convergent multifidelity optimization algorithm for unconstrained problems that does not require high-fidelity gradients. The method uses a radial basis function interpolation to capture...
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This paper presents a provably convergent multifidelity optimization algorithm for unconstrained problems that does not require high-fidelity gradients. The method uses a radial basis function interpolation to capture the error between a high-fidelity function and a low-fidelity function. The error interpolation is added to the low-fidelity function to create a surrogate model of the high-fidelity function in the neighborhood of a trust region. When appropriately distributed spatial calibration points are used, the low-fidelity function and radial basis function interpolation generate a fully linear model. This condition is sufficient to prove convergence in a trust region framework. In the case when there are multiple lower-fidelity models, the predictions of all calibrated lower-fidelity models can be combined with a maximum likelihood estimator constructed using kriging variance estimates from the radial basis function models. This procedure allows for flexibility in sampling lower-fidelity functions, does not alter the convergence proof of the optimization algorithm, and is shown to be robust to poor low-fidelity information. The algorithm is compared with a single-fidelity quasi-Newton algorithm and two first-order consistent multifidelity trust region algorithms. For simple functions the quasi-Newton algorithm uses slightly fewer high-fidelity function evaluations;however, for more complex supersonic airfoil design problems it uses significantly more. In all cases tested, our radial basis function calibration approach uses fewer high-fidelity function evaluations when compared with first-order consistent trust region schemes.
The theoretical symmetry in Hough transform (HT) butterflies is addressed. The reason the symmetry is damaged in real HT butterflies is analysed and employed to propose a high accuracy HT. Experiment demonstrates that...
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The theoretical symmetry in Hough transform (HT) butterflies is addressed. The reason the symmetry is damaged in real HT butterflies is analysed and employed to propose a high accuracy HT. Experiment demonstrates that the proposed method obtains high accuracy straight line parameters without the exact position of the HT peak.
A novel sampling scheme for coarse-fine time-to-digital converters (TDCs), whose architecture commonly comprises a synchronous counter as a coarse part and delay-line based phase interpolator as a fine part, is propos...
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A novel sampling scheme for coarse-fine time-to-digital converters (TDCs), whose architecture commonly comprises a synchronous counter as a coarse part and delay-line based phase interpolator as a fine part, is proposed. The presented approach eliminates any error in the coarse counter and is superior in terms of mean time between failures as well as complexity, area and power consumption in comparison to previously reported solutions.
A novel method of calculating nuclear statistical equilibrium (NSE) is presented. Basic equations are carefully solved using arbitrary precision arithmetic. A special interpolation procedure is then used to retrieve a...
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A novel method of calculating nuclear statistical equilibrium (NSE) is presented. Basic equations are carefully solved using arbitrary precision arithmetic. A special interpolation procedure is then used to retrieve all abundances using tabulated results for neutrons and protons, together with basic nuclear data. Proton and neutron abundance tables, basic nuclear data, and partition functions for nuclides used in the calculations are provided. A simple interpolation algorithm using pre-calculated p and n abundances tabulated as functions of kT, rho and Y-e is outlined. Unique properties of this method are: (1) ability to pick up out of NSE selected nuclei only, (2) computational time scaling linearly with number of re-calculated abundances, (3) relatively small amount of stored data: only two large tables, (4) slightly faster than solving the NSE equations using traditional Newton-Raphson methods for small networks (few tens of species): superior for huge (800-3000) networks, (5) does not require initial guess: works well on random input, (6) can be tailored to specific application, (7) ability to use third-party NSE solvers to obtain fully compatible tables, and (8) encapsulation of the NSE code for bug-free calculations. A range of applications for this approach is possible: covering tests of traditional NSE Newton-Raphson codes, generating starting values, code-to-code verification, and possible replacement of the old legacy procedures in supernova simulations. (C) 2012 Elsevier Inc. All rights reserved.
Abstract Nowadays, in many sports discrete data describing actions of athletes are collected in order to analyze performance. Performance analysis using these data usually is based on creating categories by which the ...
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Abstract Nowadays, in many sports discrete data describing actions of athletes are collected in order to analyze performance. Performance analysis using these data usually is based on creating categories by which the quality of the performance of athletes in a certain sport can be described. The definitions of these categories often only take a few circumstances or just the most important one into account which influence the performance of an athlete. By calculating continuous topographies of representative performances of the field, respectively all participating athletes, this method of classifying performances can be broken up and performances are put in context of the place where they occurred, in context of the internal and external circumstances which influence the athlete's performance, with respect to the performance of other athletes in that area. Based on the collected data such topographies are calculated using an exponential smoothing algorithm and by means of spline interpolation. The resulting three dimensional and two dimensional visualizations of such a topography show that artificial, discrete categories, which are usually shaped like geometrical figures, often circles or rectangles, are not appropriate for performance analysis because the quality of performance even within these categories is mostly varying too much. Furthermore, the results reveal that topographies of representative performance of the field are usually not shaped like geometric figures, but rather are more chaotically shaped depending on the field's real performance. Thus, continuous topographies of performance provide the opportunity to analyze performance with respect to a real representative performance in any area.
A new local algorithm for bivariate interpolation of large sets of scattered and track data is presented. The method, which changes partially depending on the kind of data, is based on the partition of the interpolati...
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A new local algorithm for bivariate interpolation of large sets of scattered and track data is presented. The method, which changes partially depending on the kind of data, is based on the partition of the interpolation domain in a suitable number of parallel strips, and, starting from these, on the construction for any data point of a square neighbourhood containing a convenient number of data points. Then, the well-known modified Shepard's formula for surface interpolation is applied with some effective improvements. The proposed algorithm is very fast, owing to the optimal nearest neighbour searching, and achieves good accuracy. Computational cost and storage requirements are analyzed. Moreover, the efficiency and reliability of the algorithm are shown by several numerical tests, also performed by Renka's algorithm for a comparison. (C) 2010 Elsevier Inc. All rights reserved.
This paper presents an image interpolation model with nonlocal p-Laplacian regularization. The nonlocal p-Laplacian regularization overcomes the drawback of the partial differential equation (PDE) proposed by Belahmid...
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This paper presents an image interpolation model with nonlocal p-Laplacian regularization. The nonlocal p-Laplacian regularization overcomes the drawback of the partial differential equation (PDE) proposed by Belahmidi and Guichard (2004) that image density diffuses in the directions pointed by local gradient. The grey values of images diffuse along image feature direction not gradient direction under the control of the proposed model, that is, minimal smoothing in the directions across the image features and maximal smoothing in the directions along the image features. The total regularizer combines the advantages of nonlocal p-Laplacian regularization and total variation (TV) regularization (preserving discontinuities and 1D image structures). The derived model efficiently reconstructs the real image, leading to a natural interpolation, with reduced blurring and staircase artifacts. We present experimental results that prove the potential and efficacy of the method.
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