Search

Abstract: This paper investigates a global optimization algorithm for the calibration of stochastic volatility models. Two GARCH models are considered, namely the Leverage and the Heston–Nandi model. Empirical information on option prices is used to minimize a loss function that reflects the option pricing error. It is shown that commonly used gradient based optimization procedures may not lead to a good solution and often converge to a local optimum. A concurrent approach where several optimizers (“particles”) execute an accelerated random search (ARS) procedure has been introduced to thoroughly explore the whole parameter domain. The number of particles influences the solution quality and computation time, leading to a trade-off between these two factors. In order to speed up the computation, distributed computing and variance reduction techniques are employed. Tests show that the concurrent ARS approach clearly outperforms the standard gradient based method. [Copyright &y& Elsevier]

[Display omitted] • The calibration has a significant impact on the accuracy of the navigation. • A calibration method based on particle swarm optimization algorithm. • Converting the Doppler velocity log calibration problem to a Wahba problem. • The proposed calibration method converges quickly and accurately. • The accuracy of integrated navigation is significantly improved. In the underwater SINS/DVL integrated navigation system, the calibration problem between SINS and DVL sensors is one of the critical factors affecting the accuracy of integrated navigation. Traditional calibration methods usually require long-term filter estimation or require the vehicle to operate specific maneuvers, which have limitations. In this paper, a DVL calibration algorithm using a particle swarm optimization (PSO) algorithm is proposed. The SINS/GPS integrated navigation velocity and DVL output velocity are constructed into two point sets, thereby converting the calibration problem into a Wahba problem and using the particle swarm optimization algorithm to solve the optimal rotation matrix. Simulation experiments and semi-physical experiments show that the proposed calibration method can quickly and accurately estimate the scale factor and installation misalignment angle between SINS and DVL sensors, and the integrated navigation accuracy after calibration is significantly improved. Compared with the conventional calibration approaches, the calibration performance of the proposed method is better. [ABSTRACT FROM AUTHOR]

This paper is concerned with the maximum-likelihood (ML) calibration methods tailored to the antenna arrays whose spatial responses are perturbed by mutual coupling effects and unknown sensor gain/phase responses. Unlike the existing work, the proposed methods are capable of jointly exploiting the spatial-domain information and time-domain waveform information of the calibration sources. Two kinds of numerical optimization algorithm are devised dependent on different array geometries. One is suitable for arbitrary irregular array manifold, while the other applies to some particular uniform arrays. Additionally, based on the maximum a posteriori probability (MAP) criterion, we extend the two algorithms to the scenario where the true values of the calibration source azimuths deviate slightly from the nominal ones with a priori known Gaussian distribution. The Cramér-Rao bound (CRB) expressions for the unknowns are derived in the absence and presence of the azimuth deviations, respectively. Simulation results support that the performances of the proposed algorithms are preferable to the ones which merely employs the spatial-domain information of the calibration sources, and are able to attain the corresponding CRB. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic optimization algorithm is proposed to calibrate the three-axis magnetometer onboard. The sensor errors, namely, hard iron, soft iron, nonorthogonality, scale factors, and bias, are taken into account. Particle swarm optimization (PSO) strategy is used to do the calibration, enhanced by the stretching technique to improve the accuracy and robustness. The performance of this algorithm is evaluated with a series of laboratory experiments and a field experiment of autonomous underwater vehicle. Comparisons with other analytical calibration methods are made. The results demonstrate that both the PSO and the stretched PSO algorithm can significantly compensate the magnetometer readings, and the latter algorithm has higher accuracy and more robustness. [ABSTRACT FROM PUBLISHER]

Optimization is often used to perform model calibration, the process of inferring the values of model parameters so that the results of the simulations best match observed behavior. It can both improve the predictive capability of the model and curtail the loss of information caused by using a numerical model instead of the actual system. At its heart is the comparison of experimental data and simulation results. Complicating this comparison is the fact that both data sets contain uncertainties which must be quantified in order to make reasonable comparisons. Therefore, uncertainty quantification (UQ) techniques can be applied to identify, characterize, reduce, and, if possible, eliminate uncertainties. Incorporation of UQ into the calibration process can drastically improve the usefulness of computational models. Current approaches are serial approaches in that first, the calibration parameters are identified and then, a series of runs dedicated to UQ analysis is completed. Although this approach can be effective,it can be computationally expensive or produce incomplete results. Model analysis that takes advantage of intermediate optimization iterates can reduce the expense, but the sampling done by the optimization algorithms is not ideal. In this paper, we will review serial approaches and propose a joint calibration and UQ approach that combines Bayesian statistical models and derivative-free optimization in order to monitor sensitivity information throughout the calibration process. [ABSTRACT FROM AUTHOR]

This paper presents a method for determination of the finite-element method model electrothermal parameters in a medium-voltage switchgear cell. The research includes a thermal calculation that is considered as a consequence of the eddy currents phenomena, which is a coupled numerical problem. Calibration of the numerical model is based on the multiobjective optimization procedure, which is carried out with the differential evolution algorithm (DE). The DE provides the best possible agreement of the measured temperature values on the test model and calculated values with the equivalent numerical model. The presented results are obtained for the linear and nonlinear case of eddy currents phenomena. The goal of the calibration is to determine the correct electrothermal parameters. [ABSTRACT FROM PUBLISHER]

Abstract: A new approach to 3D scanners calibration, based on Fourier Transform Profilometry method, is proposed and verified by experiments. Commonly used calibration techniques require that a calibration plane is placed in different and known positions in the scanner measuring volume; an optimization procedure iteratively change the value of some calibration parameters in order to minimize the discrepancy between the height distributions of the measured planes and the reference ones. The main limit of this type of calibration techniques is the necessity to guarantee a high accuracy in the placement of the planes used as calibration references. The innovation of the method proposed in this paper is the complete removal of any device for the accurate plane placement by assigning the task of the plane positions estimation to the camera, which is part of the scanner. The well known camera calibration algorithm proposed by Zhang permits at the same time to calibrate the camera and to estimate the positions and orientations of the plane used for the calibration itself. The knowledge of the plane position allows to use them as a reference of the scanner calibration. The obtained results show that this method provides accuracy values for the scanner parameters estimation comparable with other calibration techniques, but the advantage is that no particular device is needed. [ABSTRACT FROM AUTHOR]

Abstract: Because of noise and reverberation, accuracy of speech recognition systems decreases when the distance between talker and microphone increases. By the using of microphone arrays and appropriate filtering of received signals, the accuracy of recognizer can be increased. Many different methods for using microphone arrays have been proposed that can be classified into two main approaches: systems that perform in two independent stages of array processing and then recognition and systems that use array processing to generate a sequence of features which maximize the likelihood of generating the correct hypothesis in recognition phase. Following second approach, in this paper a new method for microphone array processing is proposed in which the parameters of array processing are adjusted in calibration phase based on phones used in language and maximum likelihood method. Optimized filter parameters are stored and used during recognition phase. A new modified Viterbi algorithm using optimal phone-based filter parameters is used for recognition phase. The proposed algorithm is analytically formulated and Persian language is used to find any improvement in speech recognition accuracy compared with results of delay and sum and utterance-based filter and sum algorithms. The results show 12.2% improvement in accuracy compared to utterance-based algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a calibration framework for a precipitation-runoff model for flood prediction in a mesoscale Alpine basin with discharges strongly influenced by hydraulic works. The developed methodology addresses two classical hydrological calibration challenges: computational limitations to run optimization algorithms for distributed hourly models and the absence of concomitant meteorological and natural discharge time series. The presented processes-oriented, multi-signal approach is based on hydrological data from a variety of sources and for different periods, corresponding to various spatial scales. The model parameters are calibrated by sequentially minimizing differences between observed and simulated values for different hydrological signals and signatures such as: (a) the phase of precipitations, (b) the time evolution of point-scale snow heights, (c) the mean inter-annual cycle of daily discharges, and (d) timing of snowmelt-induced spring runoff. We compare the model performance to a benchmark model obtained by simply using the globally optimal parameter values from the nearest gauged and non perturbed catchment. For prediction of flow seasonality and also extreme events, the calibration methodology outperforms the benchmark. Citation Hingray, B., Schaefli, B., Mezghani, A. & Hamdi, Y. (2010) Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments. Hydrol. Sci. J. 55(6), 1002-1016. [ABSTRACT FROM AUTHOR]

Summary: The automatic calibration of hydrological models has traditionally been performed using gauged data. However, inaccessibility to remote areas and lack of financial support cause data to be lacking in large tropical basins, such as the Amazon basin. Advances in the acquisition, processing and availability of spatially distributed remotely sensed data move the evaluation of computational models easier and more practical. This paper presents the pioneering integration of spatial altimetry data into the automatic calibration of a hydrological model. The study area is the Branco River basin, located in the Northern Amazon basin. An empirical stage×discharge relation is obtained for the Negro River and transposed to the Branco River, which enables the correlation of spatial altimetry data with water discharge derived from the MGB-IPH hydrological model. Six scenarios are created combining two sets of objective functions with three different datasets. Two of them are composed of ENVISAT altimetric data, and the third one is derived from daily gauged discharges. The MOCOM-UA multi-criteria global optimization algorithm is used to optimize the model parameters. The calibration process is validated with gauged discharge at three gauge stations located along the Branco River and two tributaries. Results demonstrate that the combination of virtual stations along the river can provide reasonable parameters. Further, the considerably reduced number of observations provided by the satellite is not a restriction to the automatic calibration, deriving performance coefficients similar to those obtained with the process using daily gauged data. [Copyright &y& Elsevier]

The suitability of genetic algorithms (GAs) for the optimization of water distribution systems (WDSs) has been demonstrated extensively. However, despite many years of application in many different fields, the selection of the GA parameters remains a difficult and time consuming task. In this paper, two methodologies that do not require trial-and-error GA parameter calibration have been tested on a WDS optimization problem to determine their suitability for application in the water resources field and to assess their ability in locating near-optimal solutions. The results indicate that both approaches located solutions that were significantly better than a GA using typical parameter values, while the methodology based on convergence of the GA population located the best solutions overall. This method can be easily applied to assist GA users in identifying suitable GA parameters without requiring a time consuming trial-and-error approach. [ABSTRACT FROM AUTHOR]

Abstract: Camera calibration is an indispensable step in retrieving 3D metric information from 2D images. One classical self-calibration method is based on Kruppa equation derived from pairwise image correspondences. However, the calibration constraints derived from Kruppa equation are quadratic, which are computationally intensive and difficult to obtain initial values. In this paper, we propose a new initialization algorithm to estimate the unknown scalar in the equation, thus the camera parameters can be initialized linearly in a closed form and then optimized iteratively via the Kruppa constraints. We prove that the scalar can be uniquely recovered from the infinite homography and propose a practical method to estimate the homography from a physical or virtual plane located at far distance to the camera. Extensive experiments on synthetic and real images validate the effectiveness of the proposed method. [Copyright &y& Elsevier]

This paper evaluated the MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) product (MOD17) by using estimated GPP from eddy-covariance flux measurements over an irrigated winter wheat and maize double-cropping field on the North China Plain in 2003–2004, and an alpine meadow on the Tibetan Plateau in 2002–2003. The mean annual GPP from MOD17 accounted for 1/2–2/3 of the surface estimated mean annual GPP for the alpine meadow, but only about 1/5–1/3 for the cropland. This underestimation was partly attributed to low estimates of leaf area index by a MODIS product (MOD15) because it is used to calculate absorbed photosynthetically active radiation in the MOD17 algorithm. The main reason is that the parameter maximum light use efficiency ( εmax) in the MOD17 algorithm was underestimated for the two biomes, especially for the cropland. Contrasted to the default, εmax was optimized using surface measurements. The optimized εmax for winter wheat, maize and meadow was 1.18, 1.81 and 0.73 g C/MJ, respectively. By using the surface measurements and optimized εmax , the MOD17 algorithm significantly improved the accuracy of GPP estimates. The optimum MOD17 algorithm explained about 82%, 68%, and 79% of GPP variance for winter wheat, maize, and meadow, respectively. These results suggest that it is necessary to adjust the MOD17 parameters for the estimation of cropland and meadow GPP, particularly over cropland. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the calibration of the non linear Lotka–Volterra model is used to compare the robustness and efficiency (CPU time) of different optimisation algorithms. Five versions of a quasi-Newton trust-region algorithm are developed and compared with a widely used quasi-Newton method. The trust-region algorithms is more robust and three of them are numerically cheaper than the more usual line search approach. Computation of the first derivatives of the objective function is cheaper with the backward differentiation (or adjoint model) technique than with the forward method as soon as the number of parameter is greater than a few ones. In the optimisation problem, the additional information about the Jacobian matrix made available by the forward method reduces the number of iterations but does not compensate for the increased numerical costs. A quasi-Newton trust-region algorithm with backward differentiation and BFGS update after both successful and unsuccessful iterations represents a robust and efficient algorithm that can be used to calibrate very demanding dynamic models. [Copyright &y& Elsevier]