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Journal articles on the topic 'Monte Carlo method Vector mesons'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Makarova, K. V., A. G. Makarov, M. A. Padalko, V. S. Strongin, and K. V. Nefedev. "Multispin Monte Carlo Method." Dal'nevostochnyi Matematicheskii Zhurnal 20, no. 2 (November 25, 2020): 212–20. http://dx.doi.org/10.47910/femj202020.

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The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means. The cluster multispin approach extends the computational capabilities of the Metropolis algorithm and allows one to find configurations of the ground and low-energy states.

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CHIN, SIU A. "HAMILTONIAN LATTICE STUDIES OF CHIRAL MESON FIELD THEORIES." International Journal of Modern Physics B 13, no. 05n06 (March 10, 1999): 721–29. http://dx.doi.org/10.1142/s0217979299000618.

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The latticization of the non-linear sigma model reduces a chiral meson field theory to an O(4) spin lattice system with quantum fluctuations. The result is an interesting marriage between quantum many-body theory and classical spin systems. By solving the resulting lattice Hamiltonian by Monte Carlo methods, the dynamics and thermodynamics of pions can be determined non-perturbatively. In a variational 16 3 lattice study, the ground state chiral phase transition is shown to be first order. Moreover, as the chiral phase transition is approached, the mass gap of pionic collective modes with quantum number of the "ω" vector meson drops toward zero.

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Rymarczyk, Tomasz, and Grzegorz Kłosowski. "SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD." Informatics Control Measurement in Economy and Environment Protection 7, no. 4 (December 21, 2017): 20–23. http://dx.doi.org/10.5604/01.3001.0010.7244.

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In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.

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Goldberg, A., and S. D. Bloom. "Monte Carlo Algorithms for Moments of Transition Arrays." International Astronomical Union Colloquium 102 (1988): 79–81. http://dx.doi.org/10.1017/s0252921100107468.

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AbstractClosed expressions for the first, second, and (in some cases) the third moment of atomic transition arrays now exist. Recently a method has been developed for getting to very high moments (up to the 12th and beyond) in cases where a “collective” state-vector (i.e. a state-vector containing the entire electric dipole strength) can be created from each eigenstate in the parent configuration. Both of these approaches give exact results. Herein we describe astatistical(or Monte Carlo) approach which requires onlyonerepresentative state-vector |RV> for the entire parent manifold to get estimates of transition moments of high order. The representation is achieved through the random amplitudes associated with each basis vector making up |RV>. This also gives rise to the dispersion characterizing the method, which has been applied to a system (in the M shell) with≈250,000 lines where we have calculated up to the 5th moment. It turns out that the dispersion in the moments decreases with the size of the manifold, making its application to very big systems statistically advantageous. A discussion of the method and these dispersion characteristics will be presented.

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Shiina, Takayuki. "Capacity Expansion Problem by Monte Carlo Sampling Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 697–703. http://dx.doi.org/10.20965/jaciii.2009.p0697.

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We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

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Koch, K. R. "Bayesian statistics and Monte Carlo methods." Journal of Geodetic Science 8, no. 1 (February 1, 2018): 18–29. http://dx.doi.org/10.1515/jogs-2018-0003.

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Abstract The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes’ theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

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Mikhailov, G. A., and I. N. Medvedev. "Vector estimators of the Monte Carlo method: Dual representation and optimization." Numerical Analysis and Applications 3, no. 4 (October 2010): 344–56. http://dx.doi.org/10.1134/s1995423910040063.

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8

Churmakov, D. Yu, V. L. Kuz'min, and I. V. Meglinskii. "Application of the vector Monte-Carlo method in polarisation optical coherence tomography." Quantum Electronics 36, no. 11 (November 30, 2006): 1009–15. http://dx.doi.org/10.1070/qe2006v036n11abeh013339.

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9

ABDALLA, AREEG, and JAMES BUCKLEY. "MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION." New Mathematics and Natural Computation 04, no. 02 (July 2008): 123–41. http://dx.doi.org/10.1142/s1793005708000982.

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We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

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Pan, Qiujing, and Daniel Dias. "An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation." Structural Safety 67 (July 2017): 85–95. http://dx.doi.org/10.1016/j.strusafe.2017.04.006.

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11

Gay, Benoit, Rodolphe Vaillon, and M. Pınar Mengüç. "Polarization imaging of multiply-scattered radiation based on integral-vector Monte Carlo method." Journal of Quantitative Spectroscopy and Radiative Transfer 111, no. 2 (January 2010): 287–94. http://dx.doi.org/10.1016/j.jqsrt.2009.06.010.

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Wan, Yi, Qi Bo Cai, and Huan Wang. "Method Study on System Reliability Calculation and Control." Advanced Materials Research 580 (October 2012): 374–77. http://dx.doi.org/10.4028/www.scientific.net/amr.580.374.

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An intelligent method of reliability analysis based on compound algorithm is presented in this paper, support vector machine and analysis of finite element combined with Monte Carlo numerical simulation is integrated to improve simulation computing precision. Mathematic model of reliability calculation on catenary system and compound algorithm model are built, reliability of location supporting seat and location pipe are calculated by the method, location supporting seat and location pipe are critical force-bearing parts of catenary system in the high-speed electrified railway, and fault rate is very high, their reliability analysis is important research subject in railway system, it is difficult to built reliability model of location supporting seat and location pipe because they work in a complex and uncertain environment. In this paper, analysis method of location installation based on support vector machine and finite element combined with monte carlo is used, and the outside parameter influence on location installation is analyzed by the model.

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Jia, Yongna, Siwei Yu, and Jianwei Ma. "Intelligent interpolation by Monte Carlo machine learning." GEOPHYSICS 83, no. 2 (March 1, 2018): V83—V97. http://dx.doi.org/10.1190/geo2017-0294.1.

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Acquisition technology advances, as well as the exploration of geologically complex areas, are pushing the quantity of data to be analyzed into the “big-data” era. In our related work, we found that a machine-learning method based on support vector regression (SVR) for seismic data intelligent interpolation can fully use large data as training data and can eliminate certain prior assumptions in the existing methods, such as linear events, sparsity, or low rank. However, immense training sets not only encompass high redundancy but also result in considerable computational costs, especially for high-dimensional seismic data. We have developed a criterion based on the Monte Carlo method for the intelligent reduction of training sets. For seismic data, pixel values in each local patch can be regarded as a set of statistical data and a variance value for the patch can be calculated. A high variance means that there are events centered around its corresponding patch or the pixel values in the patch range obviously. The patches with high variances are regarded as more representative patches. The Monte Carlo method assigns the variance as constraint and selects only the representative patches with a higher probability through a series of random positive numbers. After the training set is intelligently reduced through the Monte Carlo method, only these representative patches, constituting the new training set, are input to the SVR-based machine learning frame to construct a continuous regression model. Meanwhile, the patches with lower variances can be readily interpolated using a simple method and only present a minor influence in the construction of the regression model. Thus, the representative patches are called effective patches. Finally, the missing traces can be generated from the learned regression model. Numerical illustrations on 2D seismic data and results on 3D or 5D data show that the Monte Carlo method can intelligently select the effective patches as the new training set, which greatly decreases redundancy and also keeps the reconstruction quality.

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Pan, Xiuqiang, Yangu Zhang, and Yi Wan. "A dynamic reliability analysis method based on support vector machine and Monte Carlo simulation." Journal of Computational Methods in Sciences and Engineering 20, no. 1 (April 10, 2020): 149–55. http://dx.doi.org/10.3233/jcm-193473.

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15

Wang, Liejun, Taiyi Zhang, Zhenhong Jia, and Liang Ding. "Face detection algorithm based on hybrid Monte Carlo method and Bayesian support vector machine." Concurrency and Computation: Practice and Experience 25, no. 9 (July 5, 2012): 1064–72. http://dx.doi.org/10.1002/cpe.2874.

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Cao, Yanlong, Ting Liu, Jiangxin Yang, and Huiwen Yan. "A novel tolerance analysis method for three-dimensional assembly." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 7 (August 3, 2018): 1818–27. http://dx.doi.org/10.1177/0954405418789979.

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Three-dimensional tolerance analysis is increasingly becoming an innovative method for computer-aided tolerancing. Its aim is to support the design, manufacturing, and inspection by providing a quantitative analysis of the effects of multi-tolerances on final functional key characteristics and predict the quality level. This article proposes a novel approach for three-dimensional assembly analysis—a hybridization of vector loop and quasi-Monte Carlo method. The former is used to establish the three-dimensional assembly chain and obtain the assembly function. The latter is adopted to generate n sets of dimensional values according to the distribution of each dimension in chain. The new method is shown to inherit many of the best features of classical vector loop and quasi-Monte Carlo, combining easy-to-obtain assembly function with accurate statistical analysis. For every set of dimensional values, one sample value of a functional requirement can be computed with the Newton–Raphson iterative procedure. A crank slider mechanical assembly is shown as an example to illustrate the proposed method.

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17

Chen, Guo Qing, Guo Shao Su, and Tian Bin Li. "New Method of Reliability Analysis for Deep Tunnel." Applied Mechanics and Materials 50-51 (February 2011): 864–68. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.864.

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The performance function of deep tunnel in the reliability analysis can not be explicitly expressed and has non-linear characteristics, a new method of reliability analysis is proposed by combining intelligent methods and Monte Carlo principle. The support vector machine(SVM) model is employed to establish the nonlinear mapping relationship between basic random variables and structural response action, particle swarm optimization is used to optimize the parameters of SVM model. The learned SVM model is integrated with Monte Carlo method to calculate the failure probability and reliability. At last, the validity of the proposed method is verified by two typical engineering examples, the reliability index of wall rock along Jinping Hydropower Station diversion tunnels is calculated，the results are in good agreement with the actual situations. The proposed method could benefits other similar projects.

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18

Zhao, Jun Wei, Qin Zhang, and Guo Qiang Chen. "Study on Error Analysis of Parallel Machine Tool Based on Monte-Carlo Method." Applied Mechanics and Materials 273 (January 2013): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amm.273.148.

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Aiming at the complexity of the parallel machine tool error assessment, the distance between the ball joints on the execution platform is considered as the constraint condition to establish the forward kinematic equation, and a mathematic model of space position and attitude errors is established. The paper proposes a method to the error analysis of the parallel machine tool based on Monte-Carlo simulation according to the structure characteristic, and gives a universal computation program. The feasibility of the method is verified by comparing with the traditional motion vector equation differential method in final.

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Perez Mayesffer, E. E., E. Reynoso Lara, W. F. Guerrero Sanchez, G. Rodríguez Zurita QPD, J. Dávila Pintle, and Y. E. Bravo-García. "3D Monte Carlo analysis on photons step through turbid medium by Mie scattering." Revista Mexicana de Física 67, no. 2 Mar-Apr (July 15, 2021): 292–98. http://dx.doi.org/10.31349/revmexfis.67.292.

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Photon Scattering Profiles in a turbid media were investigated through numerical simulation based on Monte Carlo-Mie method, at this present work. Using Wolfram Mathematics in the program algorithm. Photon Scattering was treated using electromagnetic spherical harmonics waves, in three-dimensional scattering. The proposal, as an alternative to the Henyey-Greensein phase approximation, was defining an unit vector that represents a phase distribution, as an equivalent function with three vector components, within the turbid media. Associating the step component, as projection using Legendre polynomials and for the transverse plane components were defining as vector bases in terms of Legendre-Hankel functions, according to Gustav Mie theory. This composite vector was defined as a step function and was evaluated within Monte Carlo algorithm, obtaining simulations of light scattering. Backscatter profiles were compared for different geometric dimensions of the spherical particles within the turbid media, including a validation of the model with an experimental Lidar signal from low clouds, obtaining physical properties of the turbid media by the proposed theoretical model.

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20

Fredenhagen, K., and M. Marcu. "A modified heat bath method suitable for Monte Carlo simulations on vector and parallel processors." Physics Letters B 193, no. 4 (July 1987): 486–88. http://dx.doi.org/10.1016/0370-2693(87)91703-5.

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Acebrón, Juan A. "A Monte Carlo method for computing the action of a matrix exponential on a vector." Applied Mathematics and Computation 362 (December 2019): 124545. http://dx.doi.org/10.1016/j.amc.2019.06.059.

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22

Mikhailov, G. A. "Investigation and reduction of the variance of weight-vector algorithms of the Monte-Carlo method." USSR Computational Mathematics and Mathematical Physics 25, no. 6 (January 1985): 18–26. http://dx.doi.org/10.1016/0041-5553(85)90004-7.

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23

de Figueiredo, Leandro Passos, Dario Grana, Mauro Roisenberg, and Bruno B. Rodrigues. "Multimodal Markov chain Monte Carlo method for nonlinear petrophysical seismic inversion." GEOPHYSICS 84, no. 5 (September 1, 2019): M1—M13. http://dx.doi.org/10.1190/geo2018-0839.1.

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One of the main objectives in the reservoir characterization is estimating the rock properties based on seismic measurements. We have developed a stochastic sampling method for the joint prediction of facies and petrophysical properties, assuming a nonparametric mixture prior distribution and a nonlinear forward model. The proposed methodology is based on a Markov chain Monte Carlo (MCMC) method specifically designed for multimodal distributions for nonlinear problems. The vector of model parameters includes the facies sequence along the seismic trace as well as the continuous petrophysical properties, such as porosity, mineral fractions, and fluid saturations. At each location, the distribution of petrophysical properties is assumed to be multimodal and nonparametric with as many modes as the number of facies; therefore, along the seismic trace, the distribution is multimodal with the number of modes being equal to the number of facies power the number of samples. Because of the nonlinear forward model, the large number of modes and as a consequence the large dimension of the model space, the analytical computation of the full posterior distribution is not feasible. We then numerically evaluate the posterior distribution by using an MCMC method in which we iteratively sample the facies, by moving from one mode to another, and the petrophysical properties, by sampling within the same mode. The method is extended to multiple seismic traces by applying a first-order Markov chain that accounts for the lateral continuity of the model properties. We first validate the method using a synthetic 2D reservoir model and then we apply the method to a real data set acquired in a carbonate field.

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Vaillon, R., B. T. Wong, and M. P. Mengüç. "Polarized radiative transfer in a particle-laden semi-transparent medium via a vector Monte Carlo method." Journal of Quantitative Spectroscopy and Radiative Transfer 84, no. 4 (April 2004): 383–94. http://dx.doi.org/10.1016/s0022-4073(03)00257-7.

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25

Mazurek, Jiří, Radomír Perzina, Jaroslav Ramík, and David Bartl. "A Numerical Comparison of the Sensitivity of the Geometric Mean Method, Eigenvalue Method, and Best–Worst Method." Mathematics 9, no. 5 (March 5, 2021): 554. http://dx.doi.org/10.3390/math9050554.

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In this paper, we compare three methods for deriving a priority vector in the theoretical framework of pairwise comparisons—the Geometric Mean Method (GMM), Eigenvalue Method (EVM) and Best–Worst Method (BWM)—with respect to two features: sensitivity and order violation. As the research method, we apply One-Factor-At-a-Time (OFAT) sensitivity analysis via Monte Carlo simulations; the number of compared objects ranges from 3 to 8, and the comparison scale coincides with Saaty’s fundamental scale from 1 to 9 with reciprocals. Our findings suggest that the BWM is, on average, significantly more sensitive statistically (and thus less robust) and more susceptible to order violation than the GMM and EVM for every examined matrix (vector) size, even after adjustment for the different numbers of pairwise comparisons required by each method. On the other hand, differences in sensitivity and order violation between the GMM and EMM were found to be mostly statistically insignificant.

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Nagai, Kazuki, Masato Anada, Yoshinori Nakanishi-Ohno, Masato Okada, and Yusuke Wakabayashi. "Robust surface structure analysis with reliable uncertainty estimation using the exchange Monte Carlo method." Journal of Applied Crystallography 53, no. 2 (March 6, 2020): 387–92. http://dx.doi.org/10.1107/s1600576720001314.

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The exchange Monte Carlo (MC) method is implemented in a surface structure refinement software using Bayesian inference. The MC calculation successfully reproduces crystal truncation rod intensity profiles from perovskite oxide ultrathin films, which involves about 60 structure parameters, starting from a simple model structure in which the ultrathin film and substrate surface have an atomic arrangement identical to the substrate bulk crystal. This shows great tolerance of the initial model in the surface structure search. The MC software is provided on the web. One of the advantages of using the MC method is the precise estimation of uncertainty of the obtained parameters. However, the parameter uncertainty is largely underestimated when one assumes that the diffraction measurements at each scattering vector are independent. The underestimation is caused by the correlation of experimental error. A means of estimation of uncertainty based on the effective number of observations is demonstrated.

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Mohammed, M. A., A. I. N. Ibrahim, Z. Siri, and N. F. M. Noor. "Mean Monte Carlo Finite Difference Method for Random Sampling of a Nonlinear Epidemic System." Sociological Methods & Research 48, no. 1 (October 20, 2016): 34–61. http://dx.doi.org/10.1177/0049124116672683.

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In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained for each subpopulation as a vector distribution. The numerical outputs are tabulated, graphed, and compared with previous statistical estimations for 2013, 2015, and 2030, respectively. The solutions of FD and MMCFD are found to be in good agreement with small standard deviation of the means, and small measure of difference. The new MMCFD method is useful to predict intervals of random distributions for the numerical solutions of this epidemiology model with better approximation and agreement between existing statistical estimations and FD numerical solutions.

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Qian, Lin-Feng, Guo-Dong Shi, Yong Huang, and Yu-Ming Xing. "Backward and forward Monte Carlo method for vector radiative transfer in a two-dimensional graded index medium." Journal of Quantitative Spectroscopy and Radiative Transfer 200 (October 2017): 225–33. http://dx.doi.org/10.1016/j.jqsrt.2017.06.017.

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Lehikoinen, Antti. "Spectral Stochastic Finite Element Method for Electromagnetic Problems with Random Geometry." Electrical, Control and Communication Engineering 6, no. 1 (October 23, 2014): 5–12. http://dx.doi.org/10.2478/ecce-2014-0011.

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Abstract In electromagnetic problems, the problem geometry may not always be exactly known. One example of such a case is a rotating machine with random-wound windings. While spectral stochastic finite element methods have been used to solve statistical electromagnetic problems such as this, their use has been mainly limited to problems with uncertainties in material parameters only. This paper presents a simple method to solve both static and time-harmonic magnetic field problems with source currents in random positions. By using an indicator function, the geometric uncertainties are effectively reduced to material uncertainties, and the problem can be solved using the established spectral stochastic procedures. The proposed method is used to solve a demonstrative single-conductor problem, and the results are compared to the Monte Carlo method. Based on these simulations, the method appears to yield accurate mean values and variances both for the vector potential and current, converging close to the results obtained by time-consuming Monte Carlo analysis. However, further study may be needed to use the method for more complicated multi-conductor problems and to reduce the sensitivity of the method on the mesh used.

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Lee, Sangyeol, Chang Kyeom Kim, and Dongwuk Kim. "Monitoring Volatility Change for Time Series Based on Support Vector Regression." Entropy 22, no. 11 (November 17, 2020): 1312. http://dx.doi.org/10.3390/e22111312.

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This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.

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Chien, Chiang-Heng, Wei-Yen Wang, Jun Jo, and Chen-Chien Hsu. "Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence." Robotica 35, no. 7 (May 20, 2016): 1504–22. http://dx.doi.org/10.1017/s026357471600028x.

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SUMMARYIn this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.

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Wang, Shenlong, Xiaohong Ding, Daye Zhu, Huijie Yu, and Haihua Wang. "Measurement uncertainty evaluation in whiplash test model via neural network and support vector machine-based Monte Carlo method." Measurement 119 (April 2018): 229–45. http://dx.doi.org/10.1016/j.measurement.2018.01.065.

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Chen, Guoqiang, and Jianli Kang. "Frequency Spectrum Customization and Optimization by Using Monte Carlo Method for Random Space Vector Pulse Width Modulation Strategy." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 12 (December 31, 2016): 135–52. http://dx.doi.org/10.14257/ijsip.2016.9.12.14.

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BAE, S., S. H. KO, and P. D. CODDINGTON. "PARALLEL WOLFF CLUSTER ALGORITHMS." International Journal of Modern Physics C 06, no. 02 (April 1995): 197–210. http://dx.doi.org/10.1142/s0129183195000150.

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The Wolff single-cluster algorithm is the most efficient method known for Monte Carlo simulation of many spin models. Due to the irregular size, shape and position of the Wolff clusters, this method does not easily lend itself to efficient parallel implementation, so that simulations using this method have thus far been confined to workstations and vector machines. Here we present two parallel implementations of this algorithm, and show that one gives fairly good performance on a MIMD parallel computer.

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Zhao, Rubing, Xiaojian Xu, Jiale Li, Cheng Li, Jinhong Chen, Yu Liu, and Shuijin Zhu. "Rapid determination of phytic acid content in cottonseed meal via near infrared spectroscopy." Journal of Near Infrared Spectroscopy 25, no. 3 (May 12, 2017): 188–95. http://dx.doi.org/10.1177/0967033517708119.

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A near infrared calibration model with higher precision and better stability was constructed in the present study, using 280 cottonseed samples. The reference phytic acid contents were determined by high-performance ion chromatography. A combination of Savitzky–Golay smoothing, standard normal variate, and the first derivative was chosen as the spectral pre-treatment method. Monte Carlo uninformative variable elimination was proposed for spectral variable selection. The regression methods of partial least squares, least squares support vector machines, and weighted least squares support vector machines were developed for the calibration model. The optimal near infrared calibration model for phytic acid contents in the cottonseed meals was least squares support vector machines, with r2 = 0.97, RPD = 5.53, RMSECV = 0.06%, and RMSEP = 0.05%. This robust method can replace the traditional method of phytic acid determination in cottonseed meals.

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Ermakov, Sergey M., and Maxim G. Smilovitskiy. "Monte-Carlo for solving large linear systems of ordinary diﬀerential equations." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 8, no. 1 (2021): 37–48. http://dx.doi.org/10.21638/spbu01.2021.104.

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Monte-Carlo approach towards solving Cauchy problem for large systems of linear differential equations is being proposed in this paper. Firstly, a quick overlook of previously obtained results from applying the approach towards Fredholm-type integral equations is being made. In the main part of the paper, a similar method is being applied towards a linear system of ODE. It is transformed into an equivalent system of Volterra-type integral equations, which relaxes certain limitations being present due to necessary conditions for convergence of majorant series. The following theorems are being stated. Theorem 1 provides necessary compliance conditions that need to be imposed upon initial and transition distributions of a required Markov chain, for which an equality between estimate’s expectation and a desirable vector product would hold. Theorem 2 formulates an equation that governs estimate’s variance, while theorem 3 states a form for Markov chain parameters that minimise the variance. Proofs are given, following the statements. A system of linear ODEs that describe a closed queue made up of ten virtual machines and seven virtual service hubs is then solved using the proposed approach. Solutions are being obtained both for a system with constant coefficients and time-variable coefficients, where breakdown intensity is dependent on t. Comparison is being made between Monte-Carlo and Rungge Kutta obtained solutions. The results can be found in corresponding tables.

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Hao, Xiuhong, Shuqiang Wang, Panqiang Huo, and Deng Pan. "Life prediction of heavy-load self-lubricating liners." Advances in Mechanical Engineering 13, no. 2 (February 2021): 168781402199215. http://dx.doi.org/10.1177/1687814021992155.

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To address the issues of long testing periods and small sample sizes while evaluating the service life of heavy-load self-lubricating liners, we propose a succinct method based on Monte Carlo simulation that is significantly fast and requires a small sample size. First, the support vector regression algorithm was applied to fit the degradation trajectories of the wear depth, and the first and second characteristic parameter vectors of the wear depth as well as the corresponding distribution models were obtained. Next, sample expansion was performed using Monte Carlo simulation and the inverse transform method. Finally, based on the failure criterion of the self-lubricating liner, the service lives and distribution models of the expanded samples were obtained; subsequently, the corresponding reliability life indices were provided. Our results indicate that when the expanded sample was large enough, the proposed prediction method exhibited a relatively high prediction accuracy. Therefore, these results provide theoretical support for shortening the testing cycle used to evaluate the service life of self-lubricating liners and for accelerating the research and development of self-lubricating spherical plain bearing products.

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Du, Li Ming, Feng Ying Wang, and Zi Yang Han. "Constructing Attractors via the Improved Eugenics Genetic Algorithm." Advanced Materials Research 989-994 (July 2014): 1786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1786.

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The paper introduces Monte Carlo method and Eugenics genetic algorithm, which be used to generate a great diversity of chaotic attractors firstly. By an analysis of their algorithms, a improved eugenics genetic algorithm is presented to avoid the "genetic drift" phenomenon in attractor graphics. A parameter vector distance limit is adopted to solve the problem and lots of experiments applying equivalent mappings of frieze group are finished to validate effectiveness for algorithm.

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Esmaeli-Ayan, A., A. Malekzadeh, and F. Hormozinejad. "Inferences on the regression coefficients in panel data models: parametric bootstrap approach." Mathematical Sciences 14, no. 1 (December 28, 2019): 67–73. http://dx.doi.org/10.1007/s40096-019-00316-6.

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AbstractThis article presents a parametric bootstrap approach to inference on the regression coefficients in panel data models. We aim to propose a method that is easily applicable for implement hypothesis testing and construct confidence interval of the regression coefficients vector of balanced and unbalanced panel data models. We show the results of our simulation study to compare of our parametric bootstrap approach with other approaches and approximated methods based on a Monte Carlo simulation study.

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Shi, Zhaoyin, Zhenzhou Lu, Xiaobo Zhang, and Luyi Li. "A novel adaptive support vector machine method for reliability analysis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 5 (March 14, 2021): 896–908. http://dx.doi.org/10.1177/1748006x211003371.

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For the structural reliability analysis, although many methods have been proposed, they still suffer from substantial computational cost or slow convergence rate for complex structures, the limit state function of which are highly non-linear, high dimensional, or implicit. A novel adaptive surrogate model method is proposed by combining support vector machine (SVM) and Monte Carlo simulation (MCS) to improve the computational efficiency of estimating structural failure probability in this paper. In the proposed method, a new adaptive learning method is established based on the kernel function of the SVM, and a new stop criterion is constructed by measuring the relative position between sample points and the margin of SVM. Then, MCS is employed to estimate failure probability based on the convergent SVM model instead of the actual limit state function. Due to the introduction of adaptive learning function, the effectiveness of the proposed method is significantly higher than those that employed random training set to construct the SVM model only once. Compared with the existing adaptive SVM combined with MCS, the proposed method avoids information loss caused by inconsistent distance scales and the normalization of the learning function, and the proposed convergence criterion is also more concise than that employed in the existing method. The examples in the paper show that the proposed method is more efficient and has broader applicability than other similar surrogate methods.

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Zhao, Jing, Yaoqi Duan, and Xiaojuan Liu. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method." Energies 11, no. 7 (July 20, 2018): 1900. http://dx.doi.org/10.3390/en11071900.

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Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h–1) at the prediction moment.

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Zhai, Peng-Wang, George W. Kattawar, and Ping Yang. "Impulse response solution to the three-dimensional vector radiative transfer equation in atmosphere-ocean systems I Monte Carlo method." Applied Optics 47, no. 8 (March 5, 2008): 1037. http://dx.doi.org/10.1364/ao.47.001037.

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Bagnato, Marco. "Design of an algorithm for an adaptive Value at Risk measurement through the implementation of robust methods in relation to asset cross-correlation." Risk Management Magazine 16, no. 1 (April 30, 2021): 43–57. http://dx.doi.org/10.47473/2020rmm0083.

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This study proposes an algorithmic approach for selecting among different Value at Risk (VaR) estimation methods. The proposed metaheuristic, denominated as “Commitment Machine” (CM), has a strong focus on assets cross-correlation and allows to measure adaptively the VaR, dynamically evaluating which is the most performing method through the minimization of a loss function. The CM algorithm compares five different VaR estimation techniques: the traditional historical simulation method, the filtered historical simulation (FHS) method, the Monte Carlo method with correlated assets, the Monte Carlo method with correlated assets which uses a GARCH model to simulate asset volatility and a Bayesian Vector autoregressive model. The heterogeneity of the compared methodologies and the proposed dynamic selection criteria allow us to be confident in the goodness of the estimated risk measure. The CM approach is able to consider the correlations between portfolio assets and the non-stationarity of the analysed time-series in the different models. The paper describes the techniques adopted by the CM, the logic behind model selection and it provides a market application case of the proposed metaheuristic, by simulating an equally weighted multi-asset portfolio.

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Yan, Bing, Hu Sheng Guo, and Xiang Li. "The Passive Target Motion Parameters Estimate Algorithm Based on Particle Filter." Advanced Materials Research 850-851 (December 2013): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.922.

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A new passive target motion analysis (TMA) based on method a single seismic-filed vector sensor is designed using pitching angle and multi-path delay is studied. The state equation and measurement equation are established and analyzed systemically by improvement particle filter based on the measurements of both bearings and time delay that are collected by the sensor. The Monte Carlo simulation experiment results demonstrate that the algorithm has some advantages of fast convergence, high precision and stability.

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Anop, Maxim, Evgenii Murashkin, and Marina V. Polonik. "On Zero-Order Optimization in Problem of the Pressure Computing in Finite Elastic-Creep Deformations." Key Engineering Materials 685 (February 2016): 300–304. http://dx.doi.org/10.4028/www.scientific.net/kem.685.300.

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The present study is devoted to the problem of optimal loading pressure identification by the prescribed displacements vector. The framework of finite elastocreep strains is used. The problem of deformation of the material in the vicinity of microdefect was considered. Integro-differential equations for the external pressure, irreversible deformations and displacements were derived. The simple zero-order optimization algorithm like the Monte Carlo method for considering problem was proposed. The optimal strain-stress state parameters were computed and analyzed.

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Zhao, Q. H., Y. Li, and Y. Wang. "SAR IMAGE SEGMENTATION WITH UNKNOWN NUMBER OF CLASSES COMBINED VORONOI TESSELLATION AND RJMCMC ALGORITHM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 119–24. http://dx.doi.org/10.5194/isprsannals-iii-7-119-2016.

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This paper presents a novel segmentation method for automatically determining the number of classes in Synthetic Aperture Radar (SAR) images by combining Voronoi tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy. Instead of giving the number of classes <i>a priori</i>, it is considered as a random variable and subject to a Poisson distribution. Based on Voronoi tessellation, the image is divided into homogeneous polygons. By Bayesian paradigm, a posterior distribution which characterizes the segmentation and model parameters conditional on a given SAR image can be obtained up to a normalizing constant; Then, a Revisable Jump Markov Chain Monte Carlo(RJMCMC) algorithm involving six move types is designed to simulate the posterior distribution, the move types including: splitting or merging real classes, updating parameter vector, updating label field, moving positions of generating points, birth or death of generating points and birth or death of an empty class. Experimental results with real and simulated SAR images demonstrate that the proposed method can determine the number of classes automatically and segment homogeneous regions well.

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47

Zhao, Q. H., Y. Li, and Y. Wang. "SAR IMAGE SEGMENTATION WITH UNKNOWN NUMBER OF CLASSES COMBINED VORONOI TESSELLATION AND RJMCMC ALGORITHM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 119–24. http://dx.doi.org/10.5194/isprs-annals-iii-7-119-2016.

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This paper presents a novel segmentation method for automatically determining the number of classes in Synthetic Aperture Radar (SAR) images by combining Voronoi tessellation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy. Instead of giving the number of classes <i>a priori</i>, it is considered as a random variable and subject to a Poisson distribution. Based on Voronoi tessellation, the image is divided into homogeneous polygons. By Bayesian paradigm, a posterior distribution which characterizes the segmentation and model parameters conditional on a given SAR image can be obtained up to a normalizing constant; Then, a Revisable Jump Markov Chain Monte Carlo(RJMCMC) algorithm involving six move types is designed to simulate the posterior distribution, the move types including: splitting or merging real classes, updating parameter vector, updating label field, moving positions of generating points, birth or death of generating points and birth or death of an empty class. Experimental results with real and simulated SAR images demonstrate that the proposed method can determine the number of classes automatically and segment homogeneous regions well.

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Boyarkin, D. A., D. S. Krupenev, and D. V. Iakubobsky. "Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods." E3S Web of Conferences 114 (2019): 03003. http://dx.doi.org/10.1051/e3sconf/201911403003.

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Modern electricity consumers place increasingly high demands on the level of reliability of power supply and, correspondingly, the reliability of electric power systems (EPS). These requirements should be directly addressed in the EPS development planning tasks. To this end, the evaluation of the level of EPS reliability is performed by employing software and computer systems that have various methods of reliability evaluation implemented therein. Among the variety of methods for identifying reliability indicators to evaluate resource adequacy the most appropriate one is the Monte Carlo method (the method of statistical tests): it enables to perform calculations within a reasonable time with the required accuracy, while the calculation of complex EPS-like systems by means of analytical methods proves impossible because of the large dimensionality of the problem. However, even when using the Monte Carlo method, the difficulties arise as well, namely the problem of the need to reproduce a large number of random states of the simulated EPS and the calculation of the operating mode of each of them. There are several ways to reduce the overall calculation time by either efficient random number generators and optimizers or alternative methods of the calculation of operating modes. The article deals with the issue of bringing up to date the method behind reliability calculation that makes use of the Monte Carlo method. We propose to use regression analysis methods when calculating operating modes of random states of the EPS. To this end, we adopt the support-vector machine and the random forest method. Experimental studies covered in the article attest to the efficiency of the new method, for the 24-node system IEEE RTS-96 the calculation speed has been increased by almost a factor of 4 while maintaining accuracy.

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Yu, Xiao Lin, Heng Bin Zheng, Quan Sheng Yan, and Wei Li. "A Least Square Support Vector Machine Approach Based on Uniform Design Method for Structural Reliability Analysis." Advanced Materials Research 163-167 (December 2010): 3348–53. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.3348.

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Since the performance functions of large complex structures can not be expressed explicitly in the process of reliability analysis, support vector machines (SVM) with good ability of generalization are used as the response surface function based on the small training samples. The uniform design method was adopted in selecting the training data. The least support vector machines (LS-SVM) were used to find the support vectors. The limit state function was expressed by the LS-SVM regression. Reliability analysis was then performed by the usual reliability method (e.g., the first-order reliability method, the second-order reliability method or Monte Carlo) on the response surface. The results of calculations of numerical examples and a typical cable-stayed bridge show that LS-SVM using the uniform design method can well approximate the real response of complex structures which has a good efficiency and accuracy and can be applied in complex structures.

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Cheng, Jin Fang, Fu Qian, and Nan Li. "Direction of Arrival Estimation Based on a Tensor Approach." Applied Mechanics and Materials 530-531 (February 2014): 581–85. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.581.

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In this letter, we put forward a novel tensor-based Multiple Signal Classification (TB-MUSIC) applicable to a vector hydrophone array. For this purpose, the signal subspace is derived from the higher order singular value decomposition (HOSVD) of the third order tensor of the output model. Then the proposed method is achieved by signal subspace projection weighted with the reciprocal of principal singular values multiplying by the spatial spectrum based on TB-MUSIC. The synthetic spatial spectrum shows higher resolution and robustness under no-ideal scenarios. Monte Carlo experimental results are provided to illustrate the better performance of the proposed method.

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