• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.12-12
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• 2000

This paper consider a new weighted model reduction using block diagonal solutions of Lyapunov inequalities. With the input and/or output weighting function, the stability of reduced order system is quaranteed and a priori error bound is proposed. to achieve this, after finding the solutions of two Lyapunov inequalities and balancing the full order system, we find the reduced order systems using the direct truncation and the singular perturbation approximation. The proposed method is compared with other existing methods using numerical example.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.32-41
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• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.549-559
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• 2012

This paper presents a comparative analysis on the applicability of a priori statistic information about adjustment models when the surface shape parameters are estimated at an arbitrary point in an elevation data. Although the reliability of the estimates are known to be affected by surface condition and the adjustment models, there has been little research in a systematic and detail way. When the raw data have been taken from a real measurement, its true value cannot be known, however, thus this study used simulation data in order to analyze clearly the applicability of adjustment models. The generation of simulated data was performed by superimposing horizontal, slope, and curve surfaces and adding a certain amount of noise. Comparative analysis was performed by associating the a posteriori estimates with a priori statistics of each adjustment models. The experimental results show the estimation characteristics of adjustment models against varying surface conditions.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.668-673
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• 2009

In this paper, we propose the minima controlled speech presence uncertainty tracking method to improve a speech enhancement. In the conventional tracking speech presence uncertainty, we propose a method for estimating distinct values of the a priori speech absence probability for different frames and channels. This estimation is inherently based on a posteriori SNR and used in estimating the speech absence probability (SAP). In this paper, we propose a novel estimation of distinct values of the a priori speech absence probability, which is based on minima controlled speech presence uncertainty tracking method, for different frames and channels. Subsequently, estimation is applied to the calculation of speech absence probability for speech enhancement. Performance of the proposed enhancement algorithm is evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various noise environments. We show that the proposed algorithm yields better results compared to the conventional tracking speech presence uncertainty.

• ETRI Journal
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• v.43 no.6
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• pp.966-977
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• 2021

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.3 no.4
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• pp.245-250
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• 2002

This paper presents a priori and the local font classification method. The font classification uses ascenders, descenders, and serifs extracted from a word image. The gradient features of those sub-images are extracted, and used as an input to a neural network classifier to produce font classification results. The font classification determines 2-font styles (upright or slant), 3-font groups (serif, sans serif, or typewriter), and 7-font names (PostScript fonts such as Avant Garde, Helvetica, Bookman, New Century Schoolbook, Palatino, Times, or Courier). The proposed a priori and local font classification method allows an OCR system consisting of various font-specific character segmentation tools and various mono-font character recognizers.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.394-399
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• 2002

In this paper, Multisource data classification methods based on Bayesian formula are considered. For this decision fusion scheme, the individual data sources are handled separately by statistical classification algorithms and then Bayesian fusion method is applied to integrate from the available data sources. This method includes the combination of each expert decisions where the weights of the individual experts represent the reliability of the sources. The reliability measure used in the statistical approach is common to all pixels in previous work. In this experiment, the weight factors have been assigned to have different value for all pixels in order to improve the integrated classification accuracies. Although most implementations of Bayesian classification approaches assume fixed a priori probabilities, we have used adaptive a priori probabilities by iteratively calculating the local a priori probabilities so as to maximize the posteriori probabilities. The effectiveness of the proposed method is at first demonstrated on simulations with artificial and evaluated in terms of real-world data sets. As a result, we have shown that Bayesian statistical fusion scheme performs well on multispectral data classification.

• Journal of Integrative Natural Science
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• v.7 no.4
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• pp.273-277
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• 2014

In this article, we consider a singular and a degenerate elliptic operators in a divergence form. The singularities exist on a part of boundary, and comparable to the logarithmic distance function or its inverse. If we assume that the operator can be treated in a pointwise sense than distribution sense, with this operator we obtain a priori Harnack continuity near the boundary. In the proof we transform the singular elliptic operator to uniformly bounded elliptic operator with unbounded first order terms. We study this type of estimations considering a De Giorgi conjecture. In his conjecture, he proposed a certain ellipticity condition to guarantee a continuity of a solution.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1859-1865
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• 2002

In the conventional sliding mode control technique, a priori knowledge of the bound of external disturbances or/and parameter uncertainties is required to assure control robustness. This, however, may not be easy to obtain in practical situation. This work presents a novel methodology, a sliding mode controller with perturbation estimator, which offers a robust control performance without a priori knowledge about the perturbations (disturbances and parameter uncertainties). The proposed technique is featured by an integrated average value of the imposed perturbation over a certain sampling period. In order to demonstrate the effectiveness of the proposed methodology, a two-link robotic system is adopted and its position control performance is evaluated. In addition, a comparative work between the conventional technique and the proposed one is undertaken.