• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.102-109
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• 2014

A particle system is used for modeling the physical phenomenon. There are many traditional ways for simulation modeling which can be well suited for application including the landscapes of branches, clouds, waves, fog, rain, snow and fireworks in the three-dimensional space. In this paper, we present a new fireworks modeling technique for modeling 3D firework based on Firework Particle Tracking (FPT) using the particle system. Our method can track and recognize the launched and exploded particle of fireworks, and extracts relatively accurate 3D positions of the particles using 3D depth values. It can realize 3D simulation by using tracking information such as position, speed, color and life time of the firework particle. We exploit Region of Interest (ROI) for fast particle extraction and the prevention of false particle extraction caused by noise. Moreover, Kalman filter is used to enhance the robustness in launch step. We propose a new fireworks particle tracking method for the efficient tracking of particles by considering maximum moving range and moving direction of particles, and shall show that the 3D speeds of particles can be obtained by finding the rotation angles of fireworks. Also, we carry out the performance evaluation of particle tracking: tracking speed and accuracy for tracking, classification, rotation angle respectively with respect to four types of fireworks: sphere, circle, chrysanthemum and heart.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.69-74
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• 2004

본 논문에서는 CONDENSATION 알고리즘을 이용하여 입자 필터(particle filter)에 기반한 물체 추적 알고리즘을 제안한다. 입자 필터는 조건 확률 전파 모델(Conditional Density Propagation)인 베이지안(Bayesian) 추론 규칙을 적용하는 추적 구조를 갖고 있기 때문에 다른 어떤 종류의 추적 알고리즘보다 뛰어난 성능을 보인다. 논문에서는 실험 결과를 통해, 외곽(Contour) 추적 입자 필터가 복잡한 환경 속에서 강인한 추적 성능을 나타냄을 증명한다.

• Journal of Soil and Groundwater Environment
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• v.13 no.6
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• pp.50-61
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• 2008

In this study, new integrated particle tracking algorithm was developed to reduce the inherent problem of Eulerian- Lagrangian method, or adverse effect of particle tracking error on mass balance error. The new integrated particle tracking algorithm includes numerical method, semi-analytical method, and analytical method which consider both temporal and spatial changes of velocity field during time step. Detail of mathematical derivations is well illustrated and four examples are made to verify through the comparison of the new integrated particle tracking with analytical solution or Runge-Kutta method. Additionally, It was shown that the there is better superiority of the new integrated particle tracking algorithm over other existing particle tracking method such as Lu's method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.4
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• pp.211-222
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• 2020

This study suggested a method calculating the influence of effluent discharge from Saemangeum sluice-gates using the particle tracking model. For 2017, we presented the seasonal effects of effluent discharge as probability spatial distributions and compared with the results of the water age, one of the indicators of transport time scale. The influence of sluice-gates effluent discharge increases radially around Sinshi or Gaseok gates, which are expected to be biased toward the south in winter and north in summer due to the effect of seasonal winds. Although the results of the prediction are limited to the 2017 situation, the method of calculating the influence of sluice-gates effluent discharge using the Lagrangian particle tracking model can be used to predict the future of the around Saemangeum.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• Smart Media Journal
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• v.4 no.1
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• pp.16-24
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• 2015

In this paper, we propose a method for multi-touch tracking using MRF-based particle filters and gesture likelihood estimation Each touch (of one finger) is considered to be one object. One of frequently occurring issues is the hijacking problem which means that an object tracker can be hijacked by neighboring object. If a predicted particle is close to an adjacent object then the particle's weight should be lowered by analysing the influence of neighboring objects for avoiding hijacking problem. We define a penalty function to lower the weights of those particles. MRF is a graph representation where a node is the location of a target object and an edge describes the adjacent relation of target object. It is easy to utilize MRF as data structure of adjacent objects. Moreover, since MRF graph representation is helpful to analyze multi-touch gestures, we describe how to define gesture likelihoods based on MRF. The experimental results show that the proposed method can avoid the occurrence of hijacking problems and is able to estimate gesture likelihoods with high accuracy.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.563-569
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• 2002

To overcome the weaknesses of conventional finite difference model in pollutant dispersion modelling, the particle tracking method is used. In this study, a three dimensional particle tracking model which can be used in Princeton Ocean Model was developed and verified through the various numerical tests. Usability of the model was also confirmed through the ocean outfall modelling in Tampa Bay, Florida. As it is expected, random walk model showed the less dispersion in a range compared to the conventional finite difference model and its reason is estimated due to an error from numerical diffusion which the conventional model holds. This newly developed model is expected to be used in various ocean dispersion modelling.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.368-369
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• 2003

각종 배출원에서 배출된 입자상 물질은 대기 중 확산, 이동, 침착 등을 통해 국지적 도시오염 뿐만 아니라 장거리까지도 이동함으로서 광역적 대기오염의 원인이 되고 있는 등 입자상 물질의 발생원 추적에 관한 연구가 필요한 실정이다. 지금까지 입자상 물질의 발생원 추적은 대부분 배출원의 발생량 조사나 필터에 포집된 시료의 전체적인 화학적 분석의 개념으로 단시간의 시간변동과 개개의 입자가 가지는 정보가 무시되고 평균화되고 있다. (중략)

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.417.2-417.2
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• 2014

금 나노입자의 특성은 그 크기와 모양 그리고 균일한 정도에 의해 결정되므로, 균일한 크기의 금 나노입자를 사용하는 것이 매우 중요하다. Citrate 환원법으로 금 나노입자 합성 시 입자의 크기 분포에 가장 큰 영향을 주는 요인은 pH 이고, 반응용액의 pH를 높이면 크기가 균일한 금 나노입자의 합성이 가능함을 선행연구를 통해 확인한 바 있다[1]. 본 연구는 금 나노입자 형성 반응이 진행됨에 따라 나타나는 pH 변화를 실시간으로 추적하여 pH 변화가 금 나노입자의 균일도에 미치는 영향을 관찰한 것이다. 반응용액의 pH는 반응이 진행됨에 따라 지속적으로 변하는데, 반응초기에 pH가 감소하다가 (Stage I) 전환점 이후 pH가 증가하는 (Stage I) 양상을 보인다. 이러한 현상은 Au 이온의 리간드가 Cl-에서 OH-로 변화하기 때문으로 생각되고, 이로 인해 Stage I의 핵 형성반응과 Stage II의 성장반응에 영향을 주게 되어 결과적으로 입자의 크기 균일성에 영향을 주는 것으로 판단된다.