• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.257-266
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• 2007

In many multimedia applications, image compression is required to substantially reduce the amount of image data. This compression, however, sometimes brings artifacts. Typical artifacts are blocking artifacts and mosquito noise in DCT-coded images, and ringing artifacts around edges in wavelet-coded images. We propose a new directional postprocessing algorithm, which includes detection of the edge direction, interpolation scheme, and directional nonlinear filtering, to enhance the quality of decoded images. Simulation results show that the proposed algorithm is as effective as or more effective than other nonlinear filtering techniques.

• Proceedings of the KSRS Conference
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• v.2
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.115-119
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• 2002

In this paper we present a generalized directional morphological filtering algorithm for the removal of impulse noise, which is based on a combination of impulse noise detection and a weighted rank-order morphological filtering technique. For salt (or pepper) noise suppression, the generalized directional opening (or closing) filtering of the input signal is selectively used. The detection of impulse noise can be done by the geometrical difference of opening and closing filtering. Simulations show that this new filter has better detail feature preservation with effective noise reduction compared to other nonlinear filtering techniques.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.

• Current Optics and Photonics
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• v.7 no.4
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• pp.408-418
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• 2023

Due to the technological limitations of infrared thermography, infrared focal plane array (IFPA) imaging exhibits stripe non-uniformity, which is typically fixed pattern noise that changes over time and temperature on top of existing non-uniformities. This paper proposes a stripe non-uniformity correction algorithm based on scene-adaptive nonlinear filtering. The algorithm first uses a nonlinear filter to remove single-column non-uniformities and calculates the actual residual with respect to the original image. Then, the current residual is obtained by using the predicted residual from the previous frame and the actual residual. Finally, we adaptively calculate the gain and bias coefficients according to global motion parameters to reduce artifacts. Experimental results show that the proposed algorithm protects image edges to a certain extent, converges fast, has high quality, and effectively removes column stripes and non-uniform random noise compared to other adaptive correction algorithms.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.592-597
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• 2003

In this paper, some differential geometric conditions for the observer using a recurrent neural network are provided in terms of a stochastic nonlinear system control. In the stochastic nonlinear system, it is necessary to make an additional condition for observation of stochastic nonlinear system, called perfect filtering condition. In addition, we provide a observer using a recurrent neural network for the observation of a stochastic nonlinear system with the proposed observation conditions. Computer simulation shows that the control performance of the stochastic nonlinear system with a observer using a recurrent neural network satisfying the proposed conditions is more efficient than the conventional observer as Kalman filter

• 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).

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.