• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.583-592
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• 2017

A method is proposed for locating the heat sources from temperature observations, and its applicability is investigated for urban thermal environment simulations. A Lagrangian particle dispersion model, which is originally built for simulating the pollutants spread in the air, is exploited to identify the heat sources by transporting the Lagrangian heat particles backwards in time. The urban wind fields are estimated using a diagnostic meteorological model incorporating the morphological model for the urban canopy. The proposed method is tested for the horizontally homogeneous urban boundary layer problems. The effects of the turbulence levels and the computational time on the simulation are investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.126-134
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• 2009

This paper describes the vision-based system to track road signs from within a moving vehicle. The proposed system has the standard architecture with particle filter due to its robust tracking performance in complex environment. In the case of tracking road signs in real environment, it has a great difficulty in predicting time series data by reason of an occlusion due to an obstacle and the rapid change of objects on roads. To overcome this problem and improve the tracking performance, this paper proposes the algorithm using an autoregressive model as an state transition model which has affine parameters as states and using robust statistics for determining occlusion due to obstacles. The experiments of this paper show that the proposed method is efficient for real time tracking of road signs and performs well in road signs under occlusion due to obstacles.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1457-1457
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• 2005

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.557-562
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• 2006

In order to predict the dispersion of suspended sediment arising from dredging operation in port and navigation channel, a hybrid model for dispersive transport of turbidity plume was developed using Lee's(1998) hybrid method. Using hybrid modeling scheme advection-diffusion equation was solved by the forward particle-tracking method for advection process and by the fixed Eulerian grid method for diffusion process. To examine numerical model simulation in accuracy, the simulated results for 1-D, 2-D, and 3-D cases were compared with the analytical solutions including Kuo, et al's (1985) 3-D mathematical model. The model results were in a good agreement with the analytical solutions and mathematical model for the dispersion of turbidity plume.

• Journal of the Korean Society of Marine Environment & Safety
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• v.3 no.1
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• pp.73-83
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• 1997

To predict the oil-spill dispersion in marine waters, the oil-spill dispersion model based on Lagrangian particle-tracking method was developed and applied to Kwangyang and Jinju Bay. The tidal current movements to be required as input data of the oil-spill dispersion model were obtained by a two-dimensional numerical tidal model. Evaluation of tidal current movements using mean tide was successful. Modelling results were compared with the field data obtained at spill site. There were some descrepancies between modeling results and field data. However, the general pattern of modelling results was similar to that of field data. Provided the real-time tidal currents and more accurate wind data are supported, more favorable results can be obtained.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.283-293
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• 2024

In this study, advanced unscented Kalman filter (UKF) and particle filter (PF) implementations are introduced and applied to perform system identification (SI) for sudden structural damage induced by seismic loading. These two methods are then compared to validate their applicability to SI tasks. For this validation, the Bouc- Wen model is used to simulate the nonlinear shear-building response, and an adaptive rule (i.e., an adaptive tracking method) is applied to the two filter methods to improve their tracking performance during sudden changes in system properties. When the original UKF and PF are applied to an earthquake scenario, both methods fail to estimate the damage initiation time and post-damage parameter values. After applying the adaptive tracking method, it is found for both methods that although the occurrence time is identified, the estimation of the damage state is still not accurate. To improve the accuracy, an adjusted adaptive tracking method is applied, and the two methods then derive accurate estimates. Finally, when considering the computation time, UKF is promoted as a better choice for practical applications, provided that a proper adaptive tracking method is implemented.

• Particle and aerosol research
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• v.5 no.3
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• pp.123-131
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• 2009

This paper presents simulation results of particle transport in low-pressure, low-temperature plasma environment. The size dependent transport of particles in the plasma is investigated with a two-dimensional simulation tool developed in-house for plasma chamber analysis and design. The plasma model consists of the first two and three moments of the Boltzmann equation for ion and electron fluids respectively, coupled to Poisson's equation for the self-consistent electric field. The particle transport model takes into account all important factors, such as gravitational, electrostatic, ion drag, neutral drag and Brownian forces, affecting the motion of particles in the plasma environment. The particle transport model coupled with both neutral fluid and plasma models is simulated through a Lagrangian approach tracking the individual trajectory of each particle by taking a force balance on the particle. The size dependant trap locations of particles ranging from a few nm to a few ${\mu}m$ are identified in both electropositive and electronegative plasmas. The simulation results show that particles are trapped at locations where the forces acting on them balance. While fine particles tend to be trapped in the bulk, large particles accumulate near bottom sheath boundaries and around material interfaces, such as wafer and electrode edges where a sudden change in electric field occurs. Overall, small particles form a "dome" shape around the center of the plasma reactor and are also trapped in a "ring" near the radial sheath boundaries, while larger particles accumulate only in the "ring". These simulation results are qualitatively in good agreement with experimental observation.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1520-1529
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• 2013

In this paper, we propose and assess the performance of "H infinity filter ($H_{\infty}$, HIF)" and "cost reference particle filter (CRPF)" in the problem of tracking a target based on the measurements of the range and the bearing of the target. HIF and CRPF have the common advantageous feature that we do not need to know the noise statistics of the problem in their applications. The performance of the extended Kalman filter (EKF) is also compared with that of the proposed filters, but the noise information is perfectly known for the applications of the EKF. Simulation results show that CRPF outperforms HIF, and is more robust because the tracking of HIF diverges sometimes, particularly when the target track is highly nonlinear. Interestingly, when the tracking of HIF diverges, the tracking of the EKF also tends to deviate significantly from the true track for the same target track. Therefore, CRPF is very effective and appropriate approach to the problems of highly nonlinear model, especially when the noise statistics are unknown. Nonetheless, HIF also can be applied to the problem of timevarying state estimation as the EKF, particularly for the case when the noise statistcs are unknown. This paper provides a good example of how to apply CRPF and HIF to the estimation of dynamically varying and nonlinearly modeled states with unknown noise statistics.