• Smart Structures and Systems
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• v.11 no.1
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• pp.53-67
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• 2013

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.2
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• pp.151-156
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• 2000

The variability of a ventilated filter cigarette depends on the details of its construction and on the variabilities of its components. Variations in filter ventilation arise from many sources, including variations in tobacco rod pressure drop, filter tip pressure drop, tipping paper permeability, and plugwrap permeability. To reduce the filter ventilation variability, the variability of filter ventilation levels in ventilated cigarettes is studied by Monte Carlo Analysis. For each trials a value is selected for tobacco rod pressure drop, filter tip pressure drop, tipping paper permeability, and plugwrap permeability. These values are selected randomly from a normal distribution based on the target and coefficient of variation for each input variable. The results of this analysis for filter ventilation variation suggest that the variations of filter ventilation are dependent on the details of cigarette designs studied and reducing the variability of any cigarette component will reduce filter ventilation variability. For typical cigarettes, variation in the permeability of tipping paper is usually the most significant contributor to filter ventilation variability. Results of a Monte Carlo Analysis could provide both general insights and specific practical guidance about the design of ventilated filter cigarettes.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1341-1345
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• 2005

Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.563-568
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• 2003

In this paper, We propose a target tracking filter which utilizes the PDA for data association in a clutter environment and also propose an analytic solution for ideal filter covariance which accounts for all the possible events in the PDA. Monte Carlo simulation for the proposed filter in a clutter environment indicates that the proposed analytic solution forms the true error covariance of the PDA Filter.

• Progress in Medical Physics
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• v.27 no.3
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• pp.125-130
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• 2016

Dual energy computed tomography (DECT) is used to classify two materials and quantify the mass density of each material in the human body. An energy modulation filter based DECT could acquire two images, which are generated by the low- and high-energy photon spectra, in one scan, with one tube and detector. In the case of DECT using the energy modulation filter, the filter should perform the optimization process for the type of materials and thicknesses for generating two photon spectra. In this study, Geant4 Monte-Carlo simulation toolkit was used to execute the optimization process for determining the property of the energy modulation filter. In the process, various materials used for the energy modulation filter are copper (Cu, $8.96g/cm^3$), niobium (Nb, $8.57g/cm^3$), stannum (Sn, $7.31g/cm^3$), gold (Au, $19.32g/cm^3$), and lead (Pb, $11.34g/cm^3$). The thickness of the modulation filter varied from 0.1 mm to 1.0 mm. To evaluate the overlap region of the low- and high-energy spectrum, Geant4 Monte-Carlo simulation is used. The variation of the photon flux and the mean energy of photon spectrum that passes through the energy modulation filter are evaluated. In the primary photon spectrum of 80 kVp, the optimal modulation filter is a 0.1 mm lead filter that can acquire the same mean energy of 140 kVp photon spectrum. The lead filter of 0.1 mm based dual energy CBCT is required to increase the tube current 4.37 times than the original tube current owing to the 77.1% attenuation in the filter.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.331-340
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• 2000

This study aims to develop a methodology of real time forecasting of mundation risk based on DAMBRK model and Kalman filter. The model is based on implicit, nonlinear finite difference approximatIons of the one-dimensional dynamic wave equations. The stochastic estimator uses on extended Kalman filter to provide optimal updating estimates. These are accomplished by combining the predictions of the determurustic model with real time observauons modified by the Kalman filter gain ractor. Inundation risks are also estimated by applying Monte Carlo simulation to consider the variability in cross section geometry and Manning's roughness coefficient. The model calibrated by applying to the floods ot South Han River on September, 1990 and August, 1995. The Kalman tilter model indicates that significant improvement compared to deteriministic analysis in flood routing predictions in the river. Overtopping risk of levee is also presented by comparing levee height with simulated flood level. level.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4244-4252
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• 2022

The accuracy of Monte Carlo (MC) simulations in estimating the computed tomography radiation dose is highly dependent on the accuracy of CT scanner model. A system was developed to observe the 3D model intuitively and to calculate the X-ray energy spectrum and the bowtie (BT) filter model more accurately in Monte Carlo N-particle (MCNP). Labview's built-in Open Graphics Library (OpenGL) was used to display basic surfaces, and constructive solid geometry (CSG) method was used to realize Boolean operations. The energy spectrum was calculated by simulating the process of electronic shooting and the BT filter model was accurately modeled based on the calculated shape curve. Physical data from a study was used as an example to illustrate the accuracy of the constructed model. RMSE between the simulation and the measurement results were 0.97% and 0.74% for two filters of different shapes. It can be seen from the comparison results that to obtain an accurate CT scanner model, physical measurements should be taken as the standard. The energy spectrum library should be established based on Monte Carlo simulations with modifiable input files. It is necessary to use the three-segment splicing modeling method to construct the bowtie filter model.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.737-753
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• 2021

As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.