• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.362-368
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• 2009

The SA filters encompass a large class of filters based on order statistics as well as linear FIR filters. The class of SA filters is defined as a multi-stage filters whose output is linear combination of nonlinear(minimum, maximum, exclusive-OR) sub-filter outputs. According to the lust stage nonlinear sub-filters, SA filters are called SAMAX, SAMIN, and SAXOR filters. In this paper, optimal SAMAX and SAMED filters are designed for images corrupted by biased noise. The performance analysis of this experiment shows that SAMAX filters outperforms SAMED filters for biased noise. In the case of un-biased noise, the SAMAX and SAMED filters give the same performance. This result leads us to a new guideline in the application of SA filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.471-478
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• 2007

The SA fillers encompass a large class of filters based on order statistics as veil as linear FIR filters. Using SA later structure, it is possible to design linear and non-linear filters under a unified framework. In this paper SA filters are applied to an image smoothing problem for mixed noise. Original image is contaminated by Gaussian and impulsive noise. Optimal SA filters are designed and applied to contaminated image. The experimental result shows that SA filters outperform linear FIR and ordering-based nonlinear filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1556-1562
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• 2003

In this paper, a new representations of extended threshold Boolean filter(ETBF), called SA filter, is introduced. The structure of this representations is a one of multistage filters. The first stage is subfilters of nonlinear filters such as maximum, minimum, or exclusive-OR operators. The second stage is linear combination of Int stage outputs. Although the structure of this representations is very similar to SAM filters, SA filters encampass all ETBF not subset of ETBF.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1765-1770
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• 2005

Recently, particle filters have attracted attentions for nonlinear state estimation. In this approaches, a posterior probability distribution of the state variable is evaluated based on observations in simulation using so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter to circumvent degeneracy phenomena in the importance weights, which deteriorates the filter performance, and apply it to simultaneous state and parameter estimation of nonlinear state space models. Results of numerical simulation studies illustrate the applicability of this approach.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.470-472
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• 1987

This paper deals with the continuous-discrete estimation problem using FIR filters and performs modeling error analyses of the FIR filters, compared to Kalman filter and the limited memory filters, via computer simulations. It is shown that, the less driving noise the system has, the better performance the FIR filter exhibits and that this characteristic appears rare distinctly in nonlinear system than in linear systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.559-564
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• 2003

Recently, particle filters have attracted attentions for nonlinear state estimation. They evaluate a posterior probability distribution of the state variable based on observations in simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. A new filter, Evolution Strategies Based Particle Filter, is proposed to circumvent this difficulty and to improve the performance. Numerical simulation results illustrate the applicability of the proposed idea.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.3
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• pp.10-21
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• 1993

Bilinear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately, and usually with considerable parsimony in the number of coefficients compared with Volterra models. But bilinear filters have stability problem because they involve nonlinear feedback. Adaptive algorithms for bilinear filters may be diverge and have poor convergence characteristics when input signal is large In this paper, necessary and sufficient condition for mean square stability of bilinear filters for given input signal statistics is briefly described, and the method obtaining the input bound to guarantee the stability of bilinear filters is presented. New RPEM algorithm, which does not diverge and has the superior convergence characteristics compared with the conventional RPEM algorithm when input signal is large, is derived by applying the time-varying Kalman filtering concept to the conventional RPEM algorithm.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.292-295
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• 2003

Conventional methods in time / frequency domain for evaluating the compensation performance of active power filters are not provided easy solutions. So, the authors have previously proposed 3-D current coordinates which is composed into active component, fundamental reactive component and distorted component of nonlinear loads current. This method has excellent performance, but can not evaluate the characteristics of nonlinear load current whether it is inductive or capacitive. Therefore, To overcome problems mentioned previously, this paper deals with the simple Average power theory and the expanded 3-D current coordinates for evaluating the compensation performance of active power filters. To confirm the validity, active power filters simulator is developed using C-language. From the simulation, results are discuss their utility.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.1
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• pp.15-20
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• 1990

Signal restoration by linear filters based on the methods of least squares and its generalizations has been studied for many years. However, linear filters are inadequate for signal restoration in many situations. For such cases, nonlinear filters have been proposed and extensively studied recently. In this paper, we briefly review some of the general propoerties of nonlineat filters used for signal processing.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.139-145
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• 2006

Feature extraction technique in digital images has many applications such as robot vision, medical diagnostic system, and motion video transmission, etc. There are several methods for extracting features in digital images for example nonlinear gradient, nonlinear laplacian, and entropy convolutional filter. However, conventional convolutional filters are usually not efficient to extract features in an image because image feature formation in eyes is more sensitive to dark regions than to bright regions. A few nonlinear filters using difference between arithmetic mean and harmonic mean in a window for extracting sketch features are described in this paper They have some advantages, for example simple computation, dependence on local intensities and less sensitive to small intensity changes in very dark regions. Experimental results demonstrate more successful features extraction than other conventional filters over a wide variety of intensity variations.