• 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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.691-694
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• 1999

This paper presents a performance improvement technique of 2-D towed array shape estimation using Kalman filters. The proposed algorithm by linear model approximation corrects the position errors caused by the Kalman filter results. However, since the assumed linear model makes errors at bending parts, the spline interpolation algorithm based on curve is proposed to reduce the errors.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.113-119
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• 1991

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level.

• Wind and Structures
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• v.38 no.2
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• pp.119-128
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• 2024

The aerodynamic damping is an essential factor that can considerably affect the dynamic response of the cable-stayed bridge induced by crosswind load. However, developing an accurate and efficient aerodynamic damping model is crucial for evaluating the crosswind load-induced response on cable-stayed bridges. Therefore, this study proposes a new method for identifying aerodynamic damping of the bridge structures under crosswind load using an extended Kalman filter (EKF) and the particle filter (PF) algorithm. The EKF algorithm is introduced to capture the aerodynamic damping ratio. PF technique is used to select the optimal spectral representation of the noise. The effectiveness and accuracy of the proposed solution were investigated through full-scale vibration measurement data of the crosswind-induced on the bridge's girder. The results show that the proposed solution can generate an efficient and robust estimation. The errors between the target and extracted values are around 0.01mm and 0.003^o, respectively, for the vertical and torsional motion. The relationship between the amplitude and the aerodynamic damping ratio is linear for small reduced wind velocity and nonlinear with the increasing value of the reduced wind velocity. Finally, the results show the influence of the level of noise.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.565-569
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• 2003

In this paper, a robust filter is proposed to effectively estimate the system states in the case where system model uncertainties as well as disturbances are present. The proposed robust filter is constructed based on the linear approximation methods for a general nonlinear uncertain system with an integral quadratic constraint. We also derive the important characteristic of the proposed filter, a modified $H_{\infty}$ performance index. Analysis results show that the proposed filter has robustness against disturbances, such as process and measurement noises, and against parameter uncertainties. Simulation results show that the proposed filter effectively improves the performance.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.955-958
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• 1998

This paper describes the algorithm which extracts moving vehicles from sequential images and tracks those vehicles using Kalman filter. This work is composed of a motion segmentation stage which extracts moving objects from sequential images and gets features of objects, and a motion estimation stage which estimates the position and the motion of moving objects using Kalman filter. In the motion estimation stage, applying to affine motion model we divided the Kalman filter into position filter and velocity filter to employ linear Kalman filter. Multi-target tracking requires a data association component that decides which measurement to use for updating the state of which object. We use pattern recognition method to solve this problem.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.256-264
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• 1996

In this paper, the decentralized suboptimal H$_{2}$ filtering problem is considered. An additional term is added to the centralized optimal H$_{2}$ filter so that the whole filter is decentralized. We derive a necessary and sufficient condition for existence of proposed decentralized filters By employing the solution procedure for the exact model matching problem, we obtain a set of decentralized H$_{2}$ filters, and choose a suboptimal filter from this set of decentralized H$_{2}$ filters.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1289-1291
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• 1996

In this paper, we propose a new adaptive pre-processing technique for the linearization of a weakly nonlinear system which can be modeled by a Volterra series up to third order. To compensate the nonlinear effects of a given system, an update algorithm for the linear filter coefficients of the proposed adaptive pre-processor is introduced, and to compensate the linear distortion of the given system, the linear inverse filter is also utilized. For the performance test of the proposed adaptive pre-processor, computer simulation results obtained by analyzing an ANRSS loudspeaker model are provided.

• International Journal of Automotive Technology
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• v.8 no.2
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• pp.165-177
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• 2007

Predictions of diesel particulate filtration are typically made by modeling of a particle collection, and providing particle trapping levels in terms of a pressure drop. In the present study, a series of single channel diesel particulate filter (DPF) experiments are conducted, the pressure traces are inversely analyzed and essential filtration parameters are deducted for model closure. A DPF filtration model is formulated with a non-linear description of soot cake regression. Dependence of soot cake porosity, packing density, permeability, and soot density in filter walls on convective-diffusive particle transportation is examined. Sensitivity analysis was conducted on model parameters, relevant to the mode of transition. Soot cake porosity and soot packing density show low degrees of dispersion with respect to the Peclet number and have asymptotes at 0.97 and $70\;kg/m^3$, respectively, at high Peclet number. Soot density in the filter wall, which is inversely proportional to filter wall Peclet number, controls the filtration mode transition but exerts no influence on termination pressure drop. The percolation constant greatly alters the extent of pressure drop, but is insensitive to volumetric flow rate or temperature of exhaust gas at fixed operation mode.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.72-76
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• 2000

A calibration technique is proposed to improve the performance of 2-D towed array shape estimation using the Kalman filter. In the case of using displacement sensors, 2-D hydrophone positions estimated by the Kalman filter are calculated by assuming that the adjacent hydrophones are horizontally equi-spaced so that maximum distance is equal to the array length. The assumption causes errors in estimating hydrophone positions. The proposed technique using linear model approximation or spline interpolation can reduce the errors by exploiting the fact that the whole length of array is preserved whatever the array shape is. The numerical experiments show that the proposed method is very effective.