• 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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2088-2091
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• 2003

We presents a new algorithm for tracking person in video sequence that integrates the meanshift iteration procedure into the particle filtering. Utilizing the nice property of convergence to the modes in the meanshift iteration we show that only a few sample points are sufficient, while in general the particle filtering requires a large number of sample points. Multi-parts of a person is tracked independently of each other based on the color Then, the similarity against the reference model color and the geometric constraints between multi-parts are reflected as the sample weights. Also presented is the computer simulation results, which show successful tracking even for complex background clutter.

• Journal of the HCI Society of Korea
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• v.11 no.1
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• pp.39-45
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• 2016

Effective and efficient estimation of camera poses is a core method in implementing augmented reality systems or applications. The most common one is using markers, e.g., ARToolkit. However, use of markers suffers from a notorious problem that is vulnerable to occlusion. To overcome this, this paper proposes a top-down method that iteratively estimates the current camera pose by using particle swarm optimization. Through experiments, it was confirmed that the proposed method enables to implement augmented reality on severely-occluded markers.

• Journal of Korea Water Resources Association
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• v.37 no.4
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• pp.257-271
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• 2004

To evaluate behavior in representing solute transport in natural streams, the storage zone model of the axially periodic transient storage zones is developed. The periodic transient storage zone model and continuous storage zone model are verified using the parameters and the tracer concentration vs. time curves observed in laboratory channels. The periodic storage zone model best fit the measured concentration vs. time curves, while the continuous storage model fails to describe some fluctuations and the plateau region of the tail occurring in a discontinuous transient storage system. Dispersion data from Shingobee River, Minnesota, U. S. A. show that the concentration curves simulated by the proposed model fit the observed concentration curves well.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.10 no.2
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• pp.73-82
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• 1998

In order to simulate surface discharged heat dispersion in costal area, a 2-dimensional Eulerian-Lagrangian model for far field and semi-active particle tracking random walk model in near field has been combined. The mass of discharged heat water in near field has treated as particles with buoyancy and this is eventually converted to horizontal additive dispersion in random walk equations. This model is applied to both a simplified coastal geometry and a real site. In simple application it can simulate plume-like characteristics around discharging point than a near field-model, CORMIX/3. Actual application in the Chonsu Bay shows farther spreading of heat water in near field comparing the observed data, and this shows that the developed model might be applied with satisfaction.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.34 no.4
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• pp.410-418
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• 1998

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05b
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• pp.759-762
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• 2003

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.37-46
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• 2009

Typhoon Rusa in 2002 was recorded as causing the biggest damage due to flood in our country. With the enormous damage to the land, the flood was totally discharged to the open sea. As a result, in the coastal area, the discharging of a river had a big influence in comparison to the scale of the coastal area, which suffered damaged due to the discharging of the river. As it cleared the land, the load was totally discharging into the sea, where it caused various problems due to its influence on the ecosystem. These included changes to the environment, like a difference in salinity and the inflow of a land load. Therefore, in this study, a Lagrangian particle tracking model was constructed using a flow model capable of solving the behavior of a river plume, supposing Sachon Bay. It is performed the research able to tendency-like valuation and reappearance about real event. The result was that the model was well approximated the sea area tendency and the river plume of the specific event.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.496-496
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• 2000

We propose a neuron computer for tracking motion of particles in multi-dimensional space. The neuron computer is constructed of neural networks and their connections, which is a simplified model of the brain. The neuron computer is assemblage of neural networks, it includes a control unit, and the actions of the unit are represented by instructions. We designed a neuron computer to recognize and predict motion of particles. The recognition unit is constructed of neuron-array, encoder, and control part. The neuron-array is a model of the retina, and particles crease an image on the array, where the image is binary. The encoder picks one particle from the array, and translates the particle's location to Cartesian coordinates, which is scaled in [0, 1] intervals. Next, the encoder picks another particle, and does same process. The ordering and reduction of complex processes are executed by instructions. The instructions are held in the control part. The prediction unit is constructed of a multi-layer neural network and a feedback loop, where real time learning is executed. The particles' future locations are forecasted by coordinate values. The neuron computer can chase maximum 100 particles that take evasions.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.638-643
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• 2012

This paper presents an intelligent reduction method for external noise. The main idea comes from PSO-FCM (Particle Swam Optimization Fused fuzzy C-Means) clustering. The data of the target is transformed from the antenna coordinates to the vessel one and to the system coordinates. In the conversion, the overall noises hinder observer to get the exact position and velocity of the maneuvering target. While the filter is used for tracking system, unexpected acceleration becomes the main factor which makes the uncertainty. In this paper, the tracking efficiency is improved with the PSO-FCM and the compensation methodology. The acceleration is approximated from the external noise splitted by the proposed clustering method. After extracting the approximated acceleration, the rest in the noise is filtered by the filter and the compensation is added to after that. Proposed tracking method is applicable to the linear model and nonlinear one together. Also, it can do to the on-line system. Finally, some examples are provided to examine the reliability of the proposed method.