• 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.

• 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.

• 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 Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.67-67
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• 2021

하구에서의 흐름은 하천의 담수와 바다에서부터 유입되는 염수, 조석, 파랑 등으로 인해 복잡한 흐름구조와 혼합 양상을 보인다. 특히 만 내에 하천이 있을 경우 만의 해류특성은 하구에서의 혼합과 이송에 지배적인 영향을 미치며, 하천에서부터 방류되는 입자들은 만의 해류특성 따라 만에서의 체류시간과 이송이 결정된다. 잔차류 특성에 의한 순 물질 플럭스의 방향과 조석비대칭에 따른 하구에서의 퇴적 형태들이 결정되며, 이로 인해 하구에서의 퇴적물 퇴적 및 물질의 집적 위치, 하구 인근과 만에서의 환경변화에 영향을 줄 수 있다. 따라서 만 내에서의 혼합과 입자의 이송확산, 하천 담수의 영향역 등과 같은 만과 하천의 흐름 특성을 이해하는 것은 연안 및 하구의 환경 및 관리에 중요하다. 본 연구에서는 영일만과 형산강을 대상으로 계절변화에 따른 영일만 내 흐름과 형산강 하구에서의 퇴적양상에 대해 수치모의를 통해 수행하였다. 수치모델로는 천수방정식으로 준 3차원 유동해석을 하는 Delft-3D Flow와 파랑모형인 SWAN 모델을 결합하여 형산강 하구와 영일만의 유동을 해석하였다. 상류개방경계는 형산강하구 9 km, 하류개방경계는 영일만 외해 50 km로 설정하였고, 경계조건은 대상지역의 관측소 자료와 전지구 모형자료를 결합하여 구성하였다. 또한, 라그랑쥬 입자추적모델을 통해 형산강 상류에서 유입한 입자들의 영일만 내 체류 시간과 집적 위치를 평가하였다.

• 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.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.955-967
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• 2022

Existing particle tracking models predict vertical displacement of particles based on the terminal settling velocity in the stagnant water. However, experimental results of the present study confirmed that the settling velocity of particles is influenced by the turbulence effects in turbulent flow, consistent with the previous studies. The settling velocity of particles and turbulent characteristics were measured by using PTV and PIV methods, respectively, in order to establish relationship between the particle settling velocity and the ambient turbulence. It was observed that the settling velocity increase rate starts to grow when the particle diameter is of the same order as Kolmogorov length scale. Compared with the previous studies, the present study shows that the graphs of the settling velocity increase rate according to the Stokes number have concave shapes for each particle density. In conclusion, since the settling velocity in the natural flow is faster than in the stagnant water, the existing particle tracking model may estimate a relatively long time for particles to reach the river bed. Therefore, the results of the present study can help improve the performance of particle tracking models.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.231-231
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• 2022

하구에서의 흐름은 하천의 담수와 바다에서부터 유입되는 염수, 조석, 파랑 등으로 인해 복잡한 흐름구조와 혼합 양상을 보인다. 특히 만 내에 하천이 있을 경우 만의 해류특성은 하구에서의 혼합과 이송에 지배적인 영향을 미치며, 하천에서부터 방류되는 입자들은 만의 해류특성 따라 만에서의 체류시간과 이송이 결정된다. 잔차류 특성에 의한 순 물질 플럭스의 방향과 조석비대칭에 따른 하구에서의 퇴적 형태들이 결정되며, 이로 인해 하구에서의 퇴적물 퇴적 및 물질의 집적 위치, 하구 인근과 만에서의 환경변화에 영향을 줄 수 있다. 따라서 만 내에서의 혼합과 입자의 이송확산, 하천 담수의 영향역 등과 같은 만과 하천의 흐름 특성을 이해하는 것은 연안 및 하구의 환경 및 관리에 중요하다. 본 연구에서는 영일만과 형산강을 대상으로 계절변화에 따른 영일만 내 흐름과 형산강 하구에서의 퇴적양상에 대해 수치모의를 통해 수행하였다. 수치모델로는 천수방정식으로 준 3차원 유동해석을 하는 Delft-3D Flow와 파랑모형인 SWAN 모델을 결합하여 형산강하구와 영일만의 유동을 해석하였다. 상류개방경계는 형산강하구 9 km, 하류개방경계는 영일만 외해 50 km로 설정하였고, 경계조건은 대상지역의 관측소 자료와 전지구 모형자료를 결합하여 구성하였다. 또한, 라그랑쥬 입자추적모델을 통해 형산강 상류에서 유입한 입자들의 영일만 내 체류시간과 집적 위치를 평가하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.584-591
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• 2007

The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle filter and apply the MAILAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment.