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1997 IEEE Conference on Intelligent Transportation Systems (ITSC 997)

作者： Lakshmanan, S Kaliyaperumal, K Kluge, K Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

ISBN: (纸本)0780342690

This paper describes LEXLUTHER (Likelihood-based EXperiments evaLUating THe Efficacy of Radar), an algorithm for defecting roads and obstacles in radar data taken from an imaging platform mounted on a stationary automobile. Such an algorithm would be useful in systems that provide all-weather driving assistance. Road boundaries are detected first. The prior shape of the road boundaries is modeled as a deformable template that describes the road edges in terms of its curvature, orientation and offset. This template is matched to the underlying gradient field of the road data using a new and novel matching criteria. The metropolis algorithm is used to deform the template so that it "best" matches the underlying gradient field. Obstacles are detected next. The radar returns from image pixels that are identified by LEXLUTHER as being part of the road are processed again and their power levels are compared to a threshold. Pixels belonging to the road that return a significant (greater than threshold) amount of incident radar power are identified as potential obstacles. The performance of LEXLUTHER on a large all-weather data set is documented The road edges and obstacles detected are consistently close to ground truth over the entire data set.

关键词： deformable templates Bayesian detection metropolis algorithm radar backscatter all weather vision

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HIERARCHICAL ANALYSIS OF SUR MODELS WITH EXTENSIONS TO CORRELATED SERIAL ERRORS AND TIME-VARYING PARAMETER MODELS

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JOURNAL OF ECONOMETRICS 1995年第2期68卷 339-360页

作者： CHIB, S GREENBERG, E WASHINGTON UNIV DEPT ECONST LOUISMO 63130

We consider the use of Markov chain Monte Carlo methods to analyze hierarchical versions of Zellner's SUR model. In this context, the questions of Bayes estimation and model adequacy checking are considered, The approach is extended to SUR model with vector autoregressive and vector moving average errors of the first order. Finally, an efficient algorithm is developed to estimate a Markov time-varying parameter SUR model. The ideas are applied to both simulated and real data.

关键词： GIBBS SAMPLING metropolis algorithm DATA AUGMENTATION MARKOV CHAIN MONTE CARLO BAYES FACTOR HIERARCHICAL MODEL VECTOR AUTOREGRESSIVE PROCESS VECTOR MOVING AVERAGE PROCESS TIME-VARYING PARAMETER MODEL STATE SPACE MODEL

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Seasonality and Markov switching in an unobserved component time series model

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Empirical Economics 2003年第2期28卷 365-386页

作者： Luginbuhl, Rob de Vos, Aart Department of Econometrics Vrije Universiteit Amsterdam HV 1081 Netherlands

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model.

关键词： Business cycle Gibbs sampler Kalman filter metropolis algorithm Simulation smoother

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Genetic algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique

Genetic Algorithm Based on Enhanced Selection and Log-Scaled...

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Future Technologies Conference (FTC)

作者： Gupta, Neeraj Patel, Nilesh Tiwari, Bhupendra Nath Khosravy, Mahdi Oakland Univ Dept Comp Sci & Engn Rochester MI 48309 USA Ist Nazl Fis Nucl Lab Nazl Frascati Via E Fermi 40 I-00044 Rome Italy Fedral Univ Juiz de Fora Dept Elect & Elect Engn Juiz De Fora Brazil

ISBN: (纸本)9783030026868;9783030026851

In this paper, we introduce the selection and mutation schemes to enhance the computational power of Genetic algorithm (GA) for global optimization of multi-modal problems. Proposed operators make the GA an efficient optimizer in comparison of other variants of GA with improved precision, consistency and diversity. Due to the presented selection and mutation schemes improved GA, as named Enhanced Selection and Log-scaled Mutation GA (ESALOGA), selects the best chromosomes from a pool of parents and children after crossover. Indeed, the proposed GA algorithm is adaptive due to the log-scaled mutation scheme, which corresponds to the fitness of current population at each stage of its execution. Our proposal is further supported via the simulation and comparative analysis with standard GA (SGA) and other variants of GA for a class of multi-variable objective functions. Additionally, comparative results with other optimizers such as Probabilistic Bee algorithm (PBA), Invasive Weed Optimizer (IWO), and Shuffled Frog Leap algorithm (SFLA) are presented on higher number of variables to show the effectiveness of ESALOGA.

关键词： Selection operator Mutation operator Log-scaled mutation Diversity preservation Genetic algorithms metropolis algorithm

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A multi-site updating Markov chain Monte Carlo algorithm for morphologically constrained Gibbs random fields

A multi-site updating Markov chain Monte Carlo algorithm for...

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Conference on Bayesian Inference for Inverse Problems

作者： Sivakumar, K Goutsias, J Texas A&M Univ Dept Elect Engn Comp Assisted Med Diagnost Imaging Lab College Stn TX 77843 USA

ISBN: (纸本)0819429147

We study the problem of simulating a class of Gibbs random field models, called morphologically constrained Gibbs random fields, using Markov chain Monte Carlo sampling techniques. Traditional single site updating Markov chain Monte Carlo sampling algorithms, like the metropolis algorithm, tend to converge extremely slowly when used to simulate these models, particularly at low temperatures and for constraints involving large geometrical shapes. Moreover, the morphologically constrained Gibbs random fields are not, in general, Markov. Hence, a Markov chain Monte Carlo sampling algorithm based on the Gibbs sampler is not possible. We propose a variant of the metropolis algorithm that, at each iteration, allows multi-site updating and converges substantially faster than the traditional single-site updating algorithm. The set of sites that are updated at a particular iteration is specified in terms of a shape parameter and a size parameter. Computation of the acceptance probability involves a "test ratio," which requires computation of the ratio of the probabilities of the current and new realizations. Because of the special structure of our energy function, this computation can be done by means of a simple local iterative procedure. Therefore, lack of Markovianity does not impose any additional computational burden for model simulation. The proposed algorithm has been used to simulate a number of image texture models, both synthetic and natural.

关键词： Bayesian image analysis Gibbs random fields Markov chain Monte Carlo mathematical morphology metropolis algorithm morphological constraints

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Monte Carlo simulation of recrystallization and grain growth in Annealed Al-4% Cu alloy: Validation through experimental microstructure comparison

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2024年第0期

作者： Rajendra, P. Nagaral, Madeva Siddaraju, C. Ramaiah Inst Technol Dept Mech Engn Bengaluru 560054 Karnataka India Hindustan Aeronaut Ltd Aircraft Res & Design Ctr Bengaluru 560037 Karnataka India

The study of recrystallization and grain growth in annealed metals has been extensively investigated using various computational approaches, with Monte Carlo (MC) simulations proving particularly effective. This research highlights the application of the MC method in simulating microstructures that closely resemble experimentally observed ones. The experimental microstructures were obtained from a 50% cold-worked Al-4% Cu (Duralumin) alloy, annealed at different conditions. The metropolis algorithm was employed to simulate the microstructures using a two-dimensional Potts model on a square lattice. The simulated results strongly correlate with the actual microstructures, validating the effectiveness of the MC method in exploring grain growth phenomena. This study underscores the potential of MC simulations as a robust tool for investigating recrystallization and grain evolution in annealed metals.

关键词： Recrystallization grain growth annealed metals Monte Carlo simulations microstructure simulation metropolis algorithm Potts model

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When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys 16

When Non-Elitism Outperforms Elitism for Crossing Fitness Va...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Oliveto, Pietro S. Paixao, Tiago Heredia, Jorge Perez Sudholt, Dirk Trubenova, Barbora Univ Sheffield Sheffield S1 4DP S Yorkshire England IST Austria Campus 1 A-3400 Klosterneuburg Austria

ISBN: (纸本)9781450342063

Crossing fitness valleys is one of the major obstacles to function optimization. In this paper we investigate how the structure of the fitness valley, namely its depth d and length l, influence the runtime of different strategies for crossing these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the metropolis algorithm. While the (1+1) EA has to jump across the valley to a point of higher fitness because it does not accept decreasing moves, the non-elitist algorithms may cross the valley by accepting worsening moves. We show that while the runtime of the (1+1) EA algorithm depends critically on the length of the valley, the runtimes of the non-elitist algorithms depend crucially only on the depth of the valley. In particular, the expected runtime of both SSWM and metropolis is polynomial in l and exponential in d while the (1+1) EA is efficient only for valleys of small length. Moreover, we show that both SSWM and metropolis can also efficiently optimize a rugged function consisting of consecutive valleys.

关键词： fitness valley valley path non-elitism runtime analysis natural evolution theory strong selection weak mutation regime metropolis algorithm simulated annealing

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Choosing the Right algorithm With Hints From Complexity Theory (Hot-off-the-Press Track at GECCO 2022) 22

Choosing the Right Algorithm With Hints From Complexity Theo...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Wang, Shouda Zheng, Weijie Doerr, Benjamin Inst Polytech Paris Ecole Polytech CNRS Lab Informat LIX Palaiseau France Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781450392686

Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established optimization heuristics. We demonstrate this approach on the recently proposed DLB benchmark, for which the only known results are O (n(3)) runtimes for several classic evolutionary algorithms and an O (n(2) log n) runtime for an estimation-of-distribution algorithm. Our finding that the unary unbiased black-box complexity is only O (n(2)) suggests the metropolis algorithm as an interesting candidate and we prove that it solves the DLB problem in quadratic time. Since we also prove that better runtimes cannot be obtained in the class of unary unbiased algorithms, we shift our attention to algorithms that use the information of more parents to generate new solutions. An artificial algorithm of this type having an O (n log n) runtime leads to the result that the significance-based compact genetic algorithm sig-cGA) can solve the DLB problem also in time O (n log n).

关键词： Runtime analysis complexity theory metropolis algorithm

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Critical exponents for extended dynamical systems with simultaneous updating:: the case of the Ising model

Critical exponents for extended dynamical systems with simul...

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7th Latin American Workshop on Nonlinear Phenomena

作者： Pérez, G Sastre, F Medina, R Inst Politecn Nacl Dept Fis Aplicada Ctr Invest & Estudios Avanzados Apartado Postal 73 Cordemex Merida 97310 Yucatan Mexico

The behavior of a simultaneously updated metropolis simulation of the two-dimensional (2D) Ising model is explored. The configurations generated by such a simulation are not constructed to be equilibrium configurations, and may therefore display a different critical behavior, assuming a phase transition is present. This is relevant to the recently posed question of how the updating scheme affects the critical exponents for chaotic extended systems that show continuous phase transitions. It is found that the simultaneously updated algorithm drives the lattice, for all non-zero temperatures, into blinking maximum energy configurations, making it of little use. A small modification, given by a reduction in the acceptance probabilities for spin flips, restores the ability of the algorithm to generate ordered and disordered states, with a well-defined phase transition between them. The critical temperature becomes a function of the acceptance probability for energy lowering spin flips. The critical exponents still fall into the 2D Ising universality class. This happens even though the simultaneously updated metropolis algorithm does not really simulate the Ising model in equilibrium. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： metropolis algorithm Ising model phase transition

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The statistical modeling of the platinum nanoparticles in the transition area from the five-fold symmetry structure to the crystal lattice

The statistical modeling of the platinum nanoparticles in th...

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Oil and gas engineering (OGE)

作者： Svalova, A., I Stishenko, P., V Omsk State Tech Univ 11 Mira Pr Omsk 644050 Russia

ISBN: (纸本)9781510809888

This paper deals with the atom energy mean value of the platinum nanoparticles of various size (from 300 to 3000 atoms that approximately corresponds to the transition threshold from the icosahedral structure to the face-centered cubic one) on the face-centered cubic lattice and on the five-fold symmetry one, with the application of the modified metropolis algorithm based on the Monte-Carlo method. When calculating the energy the quantum Sutton-Chen potential is used. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： metropolis algorithm platinum nanoparticles quantum Sutton-Chen potentials

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