• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.516-518
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• 2012

본 논문은 Particle Filtering과 계층적인 Boosting 알고리즘을 이용한 다중 객체 추적 기법을 제안한다. Particle Filtering을 이용하여 각 객체를 단일 객체로 추적하고 Boosting 기반의 데이터 연관 알고리즘을 사용하여 영상에서 움직이는 물체들을 추적한다. 본 제안한 알고리즘에서는 객체들의 이동경로 정확한 감지를 위해 Particle Filtering을 통해 각 객체가 움직이는 예측 정보를 이용하고, Boosting 알고리즘을 계측적인 형태로 설계함에 따라 데이터 물체의 추적 정확도를 높일 수 있도록 하였다.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.77-82
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• 2013

The object tracking method using particle filtering has been proved successful since it is based on the Monte Carlo simulation to estimate the posterior distribution of the state vector that is nonlinear and non-Gaussian in the real-world situation. In this paper, we present two nobel methods that can improve the performance of the object tracking algorithm based on the particle filtering. First one is the feedback method that replace the low-weighted tracking sample by the estimated state vector in the previous frame. The second one is an tracking box correction method to find an confidence interval of back projection probability on the estimated candidate object area. An sample propagation equation is also presented, which is obtained by experiments. We designed well-organized test data set which reflects various challenging circumstances, and, by using it, experimental results proved that the proposed methods improves the traditional particle filter based object tracking method.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.6
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• pp.867-872
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• 2009

In centrifugal oil filters particles are forced to move toward the filter casing wall by centrifugal force in the rotating oil flow and the particles are trapped and removed on the filter paper installed at the wall. In the present study, flow field of oil and particle motion in a centrifugal oil filter has been numerically calculated in order to estimate the filtering efficiency for various operating conditions. Fluent code was used for the numerical calculations. Uncoupling the oil flow and the particle motion and the use of particle tracking trajectory enabled the estimation of filtering efficiency for various particle sizes, particle density and the filter rotational speed. Higher filtering efficiency was observed for heavier and larger particles as well as higher filter rotational speed. For the typical case of the particle density of $6000kg/m^3$ and the particle size of $10{\mu}m$ at 3500 RPM, the calculated filtering efficiency per passage was 0.31.

• 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 Electrical Engineering and Technology
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• v.9 no.6
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• pp.2107-2113
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• 2014

Particle filtering (PF) has shown its outperforming results compared to that of classical Kalman filtering (KF), particularly for highly nonlinear problems. However, PF may not be universally superior to the extended KF (EKF) although the case (i.e. an example that the EKF outperforms PF) is seldom reported in the literature. Particularly, PF approaches show degraded performance for problems where the state noise is very small or zero. This is because particles become identical within a few iterations, which is so called particle impoverishment (PI) phenomenon; consequently, no matter how many particles are employed, we do not have particle diversity regardless of if the impoverished particle is close to the true state value or not. In this paper, we investigate this PI phenomenon, and show an example problem where a classical KF approach outperforms PF approaches in terms of mean squared error (MSE) criterion. Furthermore, we compare the processing speed of the EKF and PF approaches, and show the better speed performance of classical EKF approaches. Therefore, PF approaches may not be always better option than the classical EKF for nonlinear problems. Specifically, we show the outperforming result of unscented Kalman filter compared to that of PF approaches (which are shown in Fig. 7(c) for processing speed performance, and Fig. 6 for MSE performance in the paper).

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.3
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• pp.367-376
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• 2016

Objectives: The production and use of nanoparticles have been increased. In 2014 Workplace Survey Results, 335 companies produce and treat nanoparticls. However, lack of data on nano-toxicity and a method for risk management and regulation on nanoparticles and the standard test method are not sufficient. Protective equipment selection guidelines for nanoparticles are not established. It is required to carry out respirator efficiency test against nanoparticles. This study was performed to evaluate filtration efficiency and manikin-based total inward leakage of particle filtering mask using in Korean country challenged with silver nanoparticles. Methods: We investigated filtration efficiency and total inward leakage of 7 respirator with silver nanoparticle. Results: The geometric mean diameters of Silver nanoparticles were 30 nm and number concentration were about $10^6{\sharp}/cm^3$. Filtration efficiency of six of the seven particle filtering masks was more than 98% and one particle filtering masks filtration efficiency was 94.9%. The filtration efficiency of particle filtering masks to 20 nm silver nanoparticels was highest. Artificial breathing machine with manikin based total inward leakage were 7.6% ~ 42.3%. Conclusions: The results of this study nano-silver filter efficiency was high but the total inward leakage was higher than filter penetration. Therefore, education on how to wear a respirator should be demanded. Especially for workers handling nanoparticles and toxic material, user seal checking and fit test must be performed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Transactions on Electrical and Electronic Materials
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• v.13 no.5
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• pp.233-236
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• 2012

The driving properties of a particle-insertion method that filters non-moving particles are analyzed, by measuring its optical and electrical properties. An area that is occupied by the moved particles is proposed, as a desirable evaluation method for a reflective display. To compare the driving property of the particle-moving method with that of the reported simple particle-loading method, two panels are fabricated, according to the different particle-insertion methods, in the same panel condition, of which the width of ribs is $30{\mu}m$, the cell size is $220{\mu}m{\times}220{\mu}m$, the cell gap is $116-120{\mu}m$, the q/m value of the black particles is $+1.8{\mu}C/g$ and that for the white particles is $-4.3{\mu}C/g$. The particle-moving method has a filtering effect which excludes the non-moving particles, inserting only movable particles into the respective cell, so that a panel fabricated by the particle-moving method can drive most of the particles in a cell. Also, most of the particles move at the threshold voltage of 40 V, with enhanced reflectivity. The driving property is also verified by measurement of the occupation rate of the moved particles.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.2
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• pp.73-78
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• 2007

The suitability of a flexible fiber filter for removing suspended solid (SS) in a recirculating aquaculture system was evaluated. This study focused on variation in the performance with a change in filtering time, influent water quality, and filtering mode duration. The particle distribution diagram of the filter effluent showed that the number of particles bigger than $5-8{\mu}m$ decreased dramatically, and the removal efficiency exceeded 80%. Although the removal efficiencies of SS and chemical oxygen demand (COD) were dependent on the quality of the influent, the SS and COD concentrations of the effluent were not affected by the influent concentrations. This was despite the deterioration if water quality after feeding in the rearing tank. The performance of the filter was not affected by the filtering mode duration, feeding conditions, or filtering time. The SS concentration and turbidity of the recirculating-type rearing tank were 30% and 50% lower, respectively, than of the a non-recirculating-type rearing tank under the same operating conditions. The flexible fiber filter was applicable to a recirculating aquaculture system that uses plenty of seawater, based on its low filtering resistance $(2kg_f/cm^2)$, high flux $(330m^3/m^2/hr)$, and high fine particle removal efficiency (80%, $5-8{\mu}m$).

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.743-744
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• 2016

Object tracking in video sequences is a challenging task and has various applications. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this study, we first try to develop a color-based particle filter. In this approach, the color distributions of video frames are integrated into particle filtering. Color distributions are applied because of their robustness and computational efficiency. The model of the particle filter is defined by the color information of the tracked object. The model is compared with the current hypotheses of the particle filter using the Bhattacharyya coefficient. The proposed tracking method directly incorporates the scale and motion changes of the objects. Experimental results have been presented to show the effectiveness of our proposed system.