Monte Carlo simulation of time-dependent, transport-limited fluorescent boundary measurements in frequency domain

2007 ◽  
Vol 34 (4) ◽  
pp. 1298-1311 ◽  
Author(s):  
Tianshu Pan ◽  
John C. Rasmussen ◽  
Jae Hoon Lee ◽  
Eva M. Sevick-Muraca
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
Time Dependent ◽  
Boundary Measurements ◽  
Dependent Transport

New approach to Monte Carlo simulation of photon transport in the frequency domain

1995 ◽  
Author(s):  
Ilya V. Yaroslavsky ◽  
Anna N. Yaroslavsky ◽  
Hans-Joachim Schwarzmaier ◽  
Garif G. Akchurin ◽  
Valery V. Tuchin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
New Approach ◽  
Photon Transport

Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Global Optimization ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Extreme Value ◽  
Time Dependent ◽  
Efficient Global Optimization ◽  
Highly Nonlinear ◽  
Adaptive Kriging

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

Long-range dependence in Indian stock market: a study of Indian sectoral indices

2014 ◽  
Vol 9 (4) ◽  
pp. 505-519 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
Long Range ◽  
Time Domain ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Simulation Experiments ◽  
Content Type

Purpose – The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments. Design/methodology/approach – The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices. Findings – The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature. Originality/value – This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

Probit-Based Time-Dependent Stochastic User Equilibrium Traffic Assignment Model

2008 ◽  
Vol 2085 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Kuilin Zhang ◽  
Hani S. Mahmassani ◽  
Chung-Cheng Lu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Spatial Correlation ◽  
Traffic Assignment ◽  
User Equilibrium ◽  
The Other ◽  
Time Dependent ◽  
Stochastic User Equilibrium ◽  
Assignment Model ◽  
Temporal And Spatial

This study presents a time-dependent stochastic user equilibrium (TDSUE) traffic assignment model within a probit-based path choice decision framework that explicitly takes into account temporal and spatial correlation (traveler interactions) in travel disutilities across a set of paths. The TDSUE problem, which aims to find time-dependent SUE path flows, is formulated as a fixed-point problem and solved by a simulation-based method of successive averages algorithm. A mesoscopic traffic simulator is employed to determine (experienced) time-dependent travel disutilities. A time-dependent shortest-path algorithm is applied to generate new paths and augment a grand path set. Two vehicle-based implementation techniques are proposed and compared in order to show their impact on solution quality and computational efficiency. One uses the classical Monte Carlo simulation approach to explicitly compute path choice probabilities, and the other determines probabilities by sampling vehicles’ path travel costs from an assumed perception error distribution (also using a Monte Carlo simulation process). Moreover, two types of variance-covariance error structures are discussed: one considers temporal and spatial path choice correlation (due to path overlapping) in terms of aggregated path travel times, and the other uses experienced (or empirical) path travel times from a sample of individual vehicle trajectories. A set of numerical experiments are conducted to investigate the convergence pattern of the solution algorithms and to examine the impact of temporal and spatial correlation on path choice behavior.

Sign in / Sign up

Export Citation Format

Share Document