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Assessment of the lognormality assumption of seismic fragility curves using non-parametric representations [article]

Bruno Sudret, Chu Mai, Katerina Konakli
2015 arXiv   pre-print
In this paper, we introduce two non-parametric approaches to establish the fragility curves without making any assumption, namely the conditional Monte Carlo simulation and the kernel density estimation  ...  Fragility curves are commonly used in civil engineering to estimate the vulnerability of structures to earthquakes.  ...  (LR: linear regression; MLE: maximum likelihood estimation; MCS: binned Monte Carlo simulation; KDE: kernel density estimation) Fragility curves by parametric and non-parametric approaches using Sa as  ... 
arXiv:1403.5481v2 fatcat:dcu2sq3lyjew5fbjlro32lpyqm

Special Issue On Response Analysis and Optimization of Dynamic Energy Harvesting Systems Under the Presence of Uncertainties

Agathoklis Giaralis, Ioannis A. Kougioumtzoglou, Pol D. Spanos
2020 ASCE-ASME J of Risk & Uncertainty in Engineering SystemsPart B: Mechanical Engineering  
assessment of energy harvesters; (2) Uncertainty quantification and modelling of energy harvesters; and (3) Inerter-based energy harvesters subject to random excitations.  ...  systems under the presence of uncertainties This special issue of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part B comprises 10 papers related to recent advances and emerging approaches  ...  (Uncertainty analysis of piezoelectric vibration energy harvesters using a finite element level-based maximum entropy approach) presented a methodology for non-parametric uncertainty quantification of  ... 
doi:10.1115/1.4049201 fatcat:lqn6wcwgdbfvbakhyxdojmrciq

Seismic fragility curves for structures using non-parametric representations

Chu Mai, Katerina Konakli, Bruno Sudret
2017 Frontiers of Structural and Civil Engineering  
In this paper, we introduce two non-parametric approaches to establish the fragility curves without employing the above assumption, namely binned Monte Carlo simulation and kernel density estimation.  ...  The curves obtained with the non-parametric approaches are compared with respective curves based on the lognormal assumption.  ...  Sanaz Rezaeian, who provided clarifications on the stochastic ground motion model used in this study, are also ac-26 knowledged.  ... 
doi:10.1007/s11709-017-0385-y fatcat:e36rg3utwbbplkwya2hi63vvc4

Seismic fragility curves for structures using non-parametric representations [article]

C. Mai, K. Konakli, B. Sudret
2017 arXiv   pre-print
In this paper, we introduce two non-parametric approaches to establish the fragility curves without employing the above assumption, namely binned Monte Carlo simulation and kernel density estimation.  ...  The curves obtained with the non-parametric approaches are compared with respective curves based on the lognormal assumption.  ...  Sanaz Rezaeian, who provided clarifications on the stochastic ground motion model used in this study, are also acknowledged.  ... 
arXiv:1704.03876v1 fatcat:vnyh6p2q4vasjiapwowqwhwkpe

Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

YongQi Liang, ChongZhao Han
2011 Science China Information Sciences  
Keywords semi-ballistic reentry vehicle, hybrid estimation, model-set design, Monte Carlo method, quasi-Monte Carlo method Citation Liang Y Q, Han C Z.  ...  Although the MM approach was widely used for hybrid estimation, it is seldom used for the reentry problem.  ...  A main stream of the hybrid estimation is the multiple-model (MM) approach, for which a set of models are designed to cover the Modeling of SBRV and analysis of its motion and mode The SBRV has an unbalanced  ... 
doi:10.1007/s11432-010-4159-6 fatcat:fnuzil5imbd5dek2crqt7kinha

2020 Index IEEE Transactions on Intelligent Vehicles Vol. 5

2020 IEEE Transactions on Intelligent Vehicles  
Hoel, C., +, TIV June 2020 294-305 Driver Behavior Modeling Using Game Engine and Real Vehicle: A Learn- ing-Based Approach.  ...  Xu, S., +, TIV June 2020 324-334 Machine learning Driver Behavior Modeling Using Game Engine and Real Vehicle: A Learn- ing-Based Approach.  ... 
doi:10.1109/tiv.2020.3048338 fatcat:4i46o4q23rftbhh2jzfmbumbey

Table of contents

2013 IEEE transactions on circuits and systems for video technology (Print)  
Lin 591 Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Tok, A. Glantz, A. Krutz, and T.  ... 
doi:10.1109/tcsvt.2013.2250717 fatcat:reayopbupzcadec6do7vfw6gdq

A decentralized probabilistic approach to articulated body tracking

Gang Hua, Ying Wu
2007 Computer Vision and Image Understanding  
The computational model of the proposed approach is based on a dynamic Markov network, a generative model which characterizes the dynamics, the image observations of each individual limb, as well as the  ...  We also present a variational maximum a posteriori (MAP) algorithm, which has a rigorous theoretic foundation, to approach to the optimal MAP estimate of the articulated motion.  ...  Instead a Monte Carlo method will be used as shown in later sections.  ... 
doi:10.1016/j.cviu.2006.11.020 fatcat:gbu5yrg7cvgj3lb2tsyu4siksi

Articulate hand motion capturing based on a Monte Carlo Nelder-Mead simplex tracker

J. Lin, Ying Wu, T.S. Huang
2004 Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.  
To take advantage of the constraints, we propose to use a nonparametric representation of the feasible configuration space and employ a Monte Carlo Nelder-Mead simplex search algorithm.  ...  Second, the direct search algorithm produces a set of more representative particles. Experiment results show that this hybrid approach is robust for tracking the hand motion.  ...  Acknowledgments The authors thank Howard Huang for his numerous insightful comments and Arjun Kulothungun for his contribution on model projection.  ... 
doi:10.1109/icpr.2004.1333936 dblp:conf/icpr/LinWH04 fatcat:ss3rt7lw3zbflpdnyf4c5wqkbm

Page 8081 of Mathematical Reviews Vol. , Issue 2004j [page]

2004 Mathematical Reviews  
Summary: “A hybrid method that combines Laplace’s approxi- mation and Monte Carlo simulations to evaluate integrals in the likelihood function is proposed for estimation of the parameters in nonlinear  ...  Summary: “A strong consistent estimate for one parameter of the multiparameter fractional Brownian motion is constructed. It is based on the Levy-Baxter theorems.  ... 

Nonstationary Random Parametric Vibration in Light Aircraft Landing Gear

D. E. Huntington, C. S. Lyrintzis
1998 Journal of Aircraft  
; and a hybrid Monte Carlo technique containing a spectral representation approach and a variant of Latin hypercube sampling.  ...  This Monte Carlo analysis will use a hybrid approach: the random parameters in the problem will be simulated by Latin hypercube sam- pling,'''?  ... 
doi:10.2514/2.2272 fatcat:ptj6ycqzjbfwxebj3yye4psydm

Bayesian inference for partially observed stochastic differential equations driven by fractional Brownian motion

A. Beskos, J. Dureau, K. Kalogeropoulos
2015 Biometrika  
We present a reparameterization framework based on the Davies and Harte method for sampling stationary Gaussian processes and use it to construct a Markov chain Monte Carlo algorithm that allows computationally  ...  The algorithm is based on a version of hybrid Monte Carlo that delivers increased efficiency when applied on the high-dimensional latent variables arising in this context.  ...  Fig. 1 . 1 Traceplots from 2 × 10 4 iterations of advanced hybrid Monte Carlo, for dataset Sim-A.  ... 
doi:10.1093/biomet/asv051 fatcat:lgbgqjujszfdvmq3fcvjcxoxva

Bayesian Inference for partially observed SDEs Driven by Fractional Brownian Motion [article]

Alexandros Beskos, Joseph Dureau, Konstantinos Kalogeropoulos
2015 arXiv   pre-print
The Markov chain Monte Carlo algorithm is based on a version of hybrid Monte Carlo that delivers increased efficiency when applied on the high-dimensional latent variables arising in this context.  ...  We present a reparameterization framework based on the Davies and Harte method for sampling stationary Gaussian processes and use this framework to construct a Markov chain Monte Carlo algorithm that allows  ...  INTRODUCTION A natural continuous-time modeling framework for processes with memory uses fractional Brownian motion as the driving noise.  ... 
arXiv:1307.0238v5 fatcat:ozbbrztmlzc4ljb5xggso5l5mi

State uncertainty propagation in the presence of parametric uncertainty and additive white noise

Umamaheswara Konda, Puneet Singla, Tarunraj Singh, Peter Scott
2010 Proceedings of the 2010 American Control Conference  
Similarly, for a fixed realization of the stochastic forcing process, the gPC approach provides an output distribution resulting from parametric uncertainty.  ...  These moment equations exploit the gPC approach to describe the propagation of a combination of uncertainties in model parameters, initial conditions and forcing terms.  ...  The propagation of these uncertainties in this linear dynamic model is evaluated using the gPC based approaches and compared with the Monte Carlo solution.  ... 
doi:10.1109/acc.2010.5531048 fatcat:2xppwoq4lbatphdn6gjntikcxu

An earthquake-source-based metric for seismic fragility analysis

Alin Radu, Mircea Grigoriu
2018 Bulletin of Earthquake Engineering  
Thus, as solutions to this issue, a bi-variate log-normal parametric model and an efficient calculation method, based on stochastic-reduced-order models, for fragility surfaces are proposed.  ...  The seismic fragility of a system is the probability that the system enters a damage state under seismic ground motions with specified characteristics.  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s10518-018-0341-9 fatcat:3wh37w2ydnau7nx7rmuksyimyq
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