• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.346-352
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• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.258-262
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• 2005

An adaptive beamforming system based on code filtering and differential correlation approaches is proposed. The differential correlation method was originally proposed for time delay estimation of direct sequence code division multiple access (DS-CDMA) systems under near-far ratio conditions and the code filtering correlation algorithm, on the other hand, was proposed for array response estimation in DS-CDMA systems under perfect power control. In this paper, by combining differential correlation concept with the code filtering beamforming technology, an accurate estimate of the beam forming weights and an enhanced performance of DS-CDMA systems under sever near-far ratio conditions is achieved. The system performance in terms of beam pattern and bit-error-rate (HER) shows that the proposed adaptive beamformer outperforms the conventional code filtering correlation technique.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1435-1438
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• 2005

In hands-free telephone systems, the received speech signal is fed back to the microphone and constitutes the so-called echo. To cancel the effect of this time-varying echo path, it is necessary to device an adaptive filter between the receiving and the transmitting ends. For a typical FIR realization, the length of the fullband adaptive filter results in high computational complexity and low convergence rate. Consequently, subband adaptive filtering schemes have been proposed to improve the performance. In this work, we use deterministic approach to analyze the relationship between fullband and subband adaptive filtering structures. With block adaptive filtering structure as an intermediate stage, the analysis is divided into two parts. First, to avoid aliasing, it is found that the matrix of block adaptive filters is in the form of pseudocirculant, and the elements of this matrix are the polyphase components of the fullband adaptive filter. Second, to transmit the near-end voice signal faithfully, the analysis and the synthesis filter banks in the subband adaptive filtering structure must form a perfect reconstruction pair. Using polyphase representation, the relationship between the block and the subband adaptive filters is derived.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.235-238
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• 2001

Deformation phase can be obtained by using Least-Square Fitting. In extraction of phase values, Least-Square Fitting is superior to usual method like as 2, 3, 4-Bucket Algorithm. That can extract almost noise-free phase and retain 2$\pi$discontinuities. But more fringe in phase map, 2$\pi$ discontinuities is destroyed when that is filtered and reconstruction of deformation is not reliable. So, we adapted Least-Square Fitting using an isotropic window in dense fringe. using Sine-Cosine filter give us perfect 2$\pi$discontinuities information. We showed the process and result of extraction of phase map and filtering in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.12
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• pp.20-26
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• 2002

Deformation phase can be obtained by using Least-Square fitting. In extraction of phase values, Least-Square Fitting is superior to usual method such as 2, 3, 4-Bucket Algorithm. That can extract almost noise-free phase and retain 2 $\pi$ discontinuities. But more fringes in phase map, 2 $\pi$ discontinuities are destroyed when that are filtered and reconstruction of deformation is not reliable. So, we adapted Least-Square fitting using an isotropic window in dense fringe. Using Sine/cosine filter give us perfect 2 $\pi$ discontinuities information. We showed the process and result of extraction of phase map and filtering in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

• Genomics & Informatics
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• v.7 no.2
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• pp.136-140
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• 2009

Genome-wide association (GWA) studies may benefit from the inclusion of imputed SNPs into their dataset. Due to its predictive nature, the imputation process is typically not perfect. Thus, it would be desirable to develop a scheme for filtering out the imputed SNPs by maximizing the concordance with the observed genotypes. We report such a scheme, which is based on the combination of several parameters that are calculated by PLINK, a popular GWA analysis software program. We imputed the genotypes of 8,842 Korean individuals, based on approximately 2 million SNP genotypes of the CHB＋JPT panel in the International HapMap Project Phase II data, complementing the 352k SNPs in the original Affymetrix 5.0 dataset. A total of 333,418 SNPs were found in both datasets, with a median concordance rate of 98.7%. The concordance rates were calculated at different ranges of parameters, such as the number of proxy SNPs (NPRX), the fraction of successfully imputed individuals (IMPUTED), and the information content (INFO). The poor concordance that was observed at the lower values of the parameters allowed us to develop an optimal combination of the cutoffs (IMPUTED${\geq}$0.9 and INFO${\geq}$0.9). A total of 1,026,596 SNPs passed the cutoff, of which 94,364 were found in both datasets and had 99.4% median concordance. This study illustrates a conservative scheme for filtering imputed SNPs that would be useful in GWA studies.